free

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>Containers and Cloud Native are the most significant invention in IT since the introduction of virtualization. Everyone from a small startup to a large multinational corporation is transitioning to this technology and they are looking for people who have the skills.</p>

<p>After completing this course, you will be able to build applications the Cloud Native way and be able to deploy your applications at a scale that will make Google envious.</p>

<p>This course introduces you to containers and explains how containers differ from virtual machines. &nbsp;It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, and Istio.</p>

<p>This course is of interest to anyone who wants to be a cloud practitioner and use container skills as developers, architects, system engineers, network specialists and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving and managing cloud solutions.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course you will be able to:</p>

<ul>
	<li>Understand the benefits of containers</li>
	<li>Build and run a container image</li>
	<li>Understand Kubernetes architecture</li>
	<li>Write a YAML deployment file</li>
	<li>Expose deployment as a service</li>
	<li>Manage applications with Kubernetes</li>
	<li>Use ReplicaSets, auto-scaling, rolling updates and service bindings</li>
	<li>Understand the benefits of OpenShift, Istio and other important tools</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module&nbsp;1</strong></p>

<ul>
	<li>Introduction to containers</li>
	<li>Introduction to Docker</li>
	<li>Building container images</li>
	<li>Using container registries</li>
	<li>Running containers</li>
</ul>

<p><strong>Module&nbsp;2</strong></p>

<ul>
	<li>Understanding container orchestration</li>
	<li>Understanding Kubernetes architecture</li>
	<li>Introduction to Kubernetes objects</li>
	<li>Using basic Kubernetes objects</li>
	<li>Using the kubectl command</li>
	<li>Leveraging Kubernetes</li>
</ul>

<p><strong>Module&nbsp;3</strong></p>

<ul>
	<li>Using ReplicaSets</li>
	<li>Using autoscaling</li>
	<li>Understanding rolling updates</li>
	<li>Understanding ConfigMaps and secrets</li>
	<li>Using service bindings</li>
</ul>

<p><strong>Module&nbsp;4</strong>&nbsp;-&nbsp;The Kubernetes ecosystem</p>

<ul>
	<li>The Kubernetes Ecosystem</li>
	<li>Introduction to Red Hat OpenShift</li>
	<li>Red Hat OpenShift and Kubernetes</li>
	<li>Builds</li>
	<li>Operators</li>
	<li>Istio</li>
</ul>

<p><strong>Final exam</strong></p>

<hr />
<h2>REQUIREMENTS</h2>

<p>There are no hard requirements needed to be able to take the course, however a foundation level understanding of Cloud Computing will help, such as that obtained from the&nbsp;<a href="https://cognitiveclass.ai/courses/introduction-to-cloud">Introduction to Cloud (CC0101EN)</a>&nbsp;and&nbsp;<a href="https://cognitiveclass.ai/courses/ibm-cloud-essentials">IBM Cloud Essentials (CC0103EN)</a>.</p>

<hr />
<h2>COURSE INSTRUCTORS</h2>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alex%20Parker%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Alex Parker</strong>&nbsp;is an offering manager for IBM Cloud Kubernetes Service, Red Hat OpenShift on IBM Cloud, and IBM Cloud Container Registry, with many years of experience as a software engineer. He creates articles, tutorials and education materials to equip technical professionals to become successful with cloud solutions. Alex is known for his ability to drive technical teams toward excellence. He currently resides in North Carolina, USA.</p>

<p>&nbsp;</p>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Upkar%20Lidder.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Upkar Lidder</strong>&nbsp;is a senior software engineer with 10+ years experience in IT development including team management, functional and technical leadership roles with a deep experience in full-stack technology. Currently focused on Machine Learning and Artificial Intelligence, he can be seen speaking at various conferences and participating in local tech groups and meetups. Upkar went to graduate school in Canada and currently resides in the United States.</p>

<p>&nbsp;<br />
<img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/heathernelson-300x300.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Heather Nelson</strong>&nbsp;is a senior program manager for IBM Garage Practitioner Activation with many years of experience designing and developing education that rapidly accelerates the growth of new skills required for emerging technology. She is well known for her ability to identify and overcome learning roadblocks and implementing techniques for effective learning. She currently resides in the greater Boston area in the United States.</p>

<hr />
<h2>FREQUENTLY ASKED QUESTIONS</h2>

<article>
<h3>What web browser should I use?</h3>

<p>The Open edX platform works best with current versions of Chrome, Firefox, or Safari, or with Internet Explorer version 9 and later.</p>

<p>See our&nbsp;<a href="http://edx.readthedocs.org/projects/open-edx-learner-guide/en/latest/front_matter/browsers.html">list of supported browsers</a>&nbsp;for the most current information.</p>
</article>
</section>

Introduction to Containers, Kubernetes, and OpenShift V2

This course introduces the core concepts of Containers and Kubernetes and explains how containers differ from virtual machines. It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, Istio and Knative.

<section class="about"> <h2>About This Course</h2> <p> Get answers to fundamental questions such as: <strong>What is Big Data?</strong> How do we tackle Big Data? Why are we interested in it? How does Big Data add value to businesses? </p> </section> <section class="about"> <ul> <li>Gain insights on how to run better businesses and provide better services to customers</li> <li>Get recommendations on how to process big data on platforms that can handle the volume, velocity, variety and veracity of Big Data</li> <li>Learn why Hadoop is a great Big Data solution and why it's not the only Big Data solution</li> </ul> </section> <h2>Course Syllabus</h2> <ul> <li><strong>Module 1 - What is Big Data?</strong> <ol> <li>Characteristics of Big Data</li> <li>What are the V’s of Big Data?</li> <li>The Impact of Big Data</li> </ol> </li> </ul> <ul> <li><strong>Module 2 - Big Data - Beyond the Hype</strong> <ol> <li>Big Data Examples</li> <li>Sources of Big Data</li> <li>Big Data Adoption</li> </ol> </li> </ul> <ul> <li><strong>Module 3 - The Big Data and Data Science</strong> <ol> <li>The Big Data Platform</li> <li>Big Data and Data Science</li> <li>Skills for Data Scientists</li> <li>The Data Science Process</li> </ol> </li> </ul> <ul> <li><strong>Module 4 - BDUse Cases</strong> <ol> <li>Big Data Exploration</li> <li>The Enhanced 360 View of a Customer</li> <li>Security and Intelligence</li> <li>Operations Analysis</li> </ol> </li> </ul> <ul> <li><strong>Module 5 - Processing Big Data</strong> <ol> <li>Ecosystems of Big Data</li> <li>The Hadoop Framework</li> </ol> </li> </ul> <h2>General Information</h2> <ul> <li>This Big Data course is free.</li> <li>It is self-paced.</li> <li>It can be taken at any time.</li> <li>It can be audited as many times as you wish.</li> </ul> <h2>Recommended skills prior to taking this course</h2> <ul> <li>None</li> </ul> <h2>Requirements</h2> <ul> <li>None</li> </ul> <h2>Course Staff</h2> <section class="course-staff"> <article class="teacher"> <div class="teacher-image"> <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" alt="Shingai Manjengwa, author of What is Big Data?" /> </div> <h3>Fireside Analytics Inc.</h3> <p> Shingai Manjengwa <a href="https://twitter.com/Tjido" target="_blank">(@Tjido)</a> is the Director of Insights and Analytics at <a href="http://www.firesideanalytics.com/" target="_blank">Fireside Analytics Inc</a>. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on <a href="https://www.facebook.com/firesideanalytics/" target="_blank">Facebook</a>, <a href="https://twitter.com/fireside_info" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/fireside-analytics-inc-" target="_blank">LinkedIn</a>. </p> </article> <p> This course has been adapted from the original version created by Glen R.J. Mules (below), a Senior Instructor and Principal Consultant with IBM Information Management World-Wide Education. </p> <article class="teacher"> <div class="teacher-image"><img class="alignnone wp-image-3844 size-full" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Glen%20Mules.png" alt="Glen R.J. Mules" /></div> <h3>Glen R.J. Mules</h3> <p> <a href="https://www.linkedin.com/in/glenmules" target="_blank">Glen R.J. Mules</a> is a Senior Instructor and Principal Consultant with IBM Information Management World-Wide Education and works from New Rochelle, NY. Glen joined IBM in 2001 as a result of IBM's acquisition of Informix Software. He has worked at IBM, and previously at Informix Software, as an instructor, a course developer, and in the enablement of instructors worldwide. He teaches courses in BigData (BigInsights and Streams), Optim, Guardium, and DB2, and Informix databases. He has a BSc in Mathematics from the University of Adelaide, South Australia; an MSc in Computer Science from the University of Birmingham, England; and has just completed a Ph.D. in Education (Educational Technology) at Walden University. His early work life was as a high school teacher in Australia. In the 1970s he designed, programmed, and managed banking systems in Manhattan and Boston. In the 1980's he was a VP in Electronic Payments for Bank of America in San Francisco and New York. In the early 1990's he was an EVP in Marketing for a software development company and chaired the ANSI X12C Standards Committee on Data Security for Electronic Data Interchange (EDI). </p> </article> <article class="teacher"> <div class="teacher-image"> <img class="alignnone size-thumbnail wp-image-4107" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Leo%20Petrazickis.jpg" alt="Leons Petrazickis photo" /> </div> <h3>Leons Petrazickis</h3> <p> <a href="https://ca.linkedin.com/in/leonsp">Leons Petrazickis</a> was the Ombud for Hadoop content on Skills Network as well as the Platform Architect for Skills Network Labs. As a senior software developer at IBM, he uses Ruby, Python, and Javascript to develop microservices and web applications, as well as manage containerized infrastructure. </p> </article> </section>

Big Data 101

How big is big and why does big matter and what does Apache Hadoop have to do with it? In this course you will see the Big Data big picture and you will learn the terminology used in Big Data discussions.

<div class="trix-content">
  <h1><strong>About</strong></h1><div>
<strong> </strong>In this laboratory, we will explore how land use types can be classified based on aerial and satellite photographs of the earth's surface. We will learn to build our own photo masks based on ready-made masks and display them on the screen, we will learn how to upload photos, transform pixels and colors into a data set. Then we will learn how to build a classifier and predict land use masks based on it.<br><br>
</div><div>In this lab, we will learn the basic methods of images transformation classification. The laboratory consists of four stages:</div><ul>
<li>Download and visualization of images</li>
<li>DataSet creation</li>
<li>Classification model creation</li>
<li>Create your own mask of land use</li>
</ul><div><br></div><h1><strong>After completing this lab, you will be able to:</strong></h1><ul>
<li>Download and transform images.</li>
<li>Create a DataSet of the colors of images.</li>
<li>Build a classification model.</li>
<li>Build land use masks based on a classifier.</li>
</ul><div> <br><br>
</div>
</div>


Data Science in Agriculture. Land use classification

In this lab, we will learn the basic methods of images transformation classification.

<section class="about">
<h2>About This Course</h2>

<p>Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.</p>

<p>You can use accelerated hardware such as Google&rsquo;s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.</p>

<p>But the problem is that your data might be sensitiveand you may not feel comfortable uploading it on a public cloud, preferring to analyze it on-premise. In this case, you need to use an in-house system with GPU support. One solution is to use IBM&rsquo;s Power Systems with Nvidia GPU and Power AI. The Power AI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano.</p>

<p>In this course, you&#39;ll understand what GPU-based accelerated hardware is and how it can benefit your deep learning scaling needs. You&#39;ll also deploy deep learning networks on GPU accelerated hardware for several problems, including the classification of images and videos.</p>
</section>

<h2>What will I get after passing this course?</h2>

<ul>
	<li>You will receive a completion certificate.</li>
</ul>

<h2>What will you learn?</h2>

<ul>
	<li>Explain what GPU is, how it can speed up the computation, and its advantages in comparison with CPUs.</li>
	<li>Implement deep learning networks on GPUs.</li>
	<li>Train and deploy deep learning networks for image and video classification as well as for object recognition.</li>
</ul>

<h2>Course Syllabus</h2>

<ul>
	<li>Module 1 - Quick review of Deep Learning</li>
	<li>Module 2 - Hardware Accelerated Deep Learning</li>
	<li>Module 3 - Deep Learning in the Cloud</li>
	<li>Moduel 4 - Distributed Deep Learning</li>
</ul>

<h2>General Information</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be taken as many times as you wish.</li>
</ul>

<h2>Recommended skills prior to taking this course</h2>

<p>None</p>

<h2>Grading scheme</h2>

<ul>
	<li>The minimum passing mark for the course is 70%, where the review questions are worth 50% and the final exam is worth 50% of the course mark.</li>
	<li>You have 1 attempt to take the exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
<h2>Requirements</h2>

<p>None.</p>
</section>

Accelerating Deep Learning with GPUs

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

<section class="about">
<h2>About This Course</h2>
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don&#39;t have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. 
  
  <h2>Learning Objectives</h2>
  in this course, you will learn:

<ul>
	<li>The major steps involved in tackling a data science problem.</li>
	<li>The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.</li>
	<li>How data scientists think!</li>
</ul>
</section>


<h2>Course Syllabus</h2>

<p><strong>Module 1: From Problem to Approach</strong></p>

<ul>
	<li>Business Understanding</li>
	<li>Analytic Approach</li>
</ul>

<p><strong>Module 2: From Requirements to Collection </strong></p>

<ul>
	<li>Data Requirements</li>
	<li>Data Collection</li>
</ul>

<p><strong>Module 3: From Understanding to Preparation </strong></p>

<ul>
	<li>Data Understanding</li>
	<li>Data Preparation</li>
</ul>

<p><strong>Module 4: From Modeling to Evaluation</strong></p>

<ul>
	<li>Modeling</li>
	<li>Evaluation</li>
</ul>

<p><strong>Module 5: From Deployment to Feedback</strong></p>

<ul>
	<li>Deployment</li>
	<li>Feedback</li>
</ul>

<h2 style="padding-top: 15px">General Information</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>


<h2>Requirements</h2>

<section class="prerequisites">
<ul>
	<li>Data Science Hands-on with Open Source Tools(Tools for Datascience) (DS0105EN)</li>
</ul>

<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>Passion for Data Science</li>
</ul>
</section>


Data Science Methodology

Grab your lab coat, beakers, and pocket calculator ... wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed.

<section class="about">
<h2>ABOUT THIS CLOUD COURSE</h2>

<p>This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives.</p>

<p>You will learn about the definition and essential characteristics of cloud computing, its history, emerging trends, and the business case for cloud computing. You also learn about the various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public Cloud, Private Cloud, Hybrid Cloud) and the key components of a cloud architecture (Virtualization, VMs, Storage, Networking, Containers, Serverless).</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course, you will be able to:</p>

<ul>
	<li>Define cloud computing and explain its essential characteristics, history, &amp; emerging trends</li>
	<li>Articulate the business case for cloud computing</li>
	<li>Describe the service &amp; deployment models of cloud computing</li>
	<li>Explain the various components of a cloud architecture</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<h3><strong>Module 1: Overview of Cloud Computing</strong></h3>

<ul>
	<li>Definition and essential characteristics</li>
	<li>A brief history and evolution of Cloud</li>
	<li>Key cloud service providers and their services</li>
</ul>

<h3><strong>Module 2: Cloud Adoption and Emerging Technologies</strong></h3>

<ul>
	<li>Business case for Cloud Computing</li>
	<li>Emerging technologies supported by Cloud: AI, IoT, Blockchain, Analytics</li>
</ul>

<h3><strong>Module 3: Cloud Computing Service and Deployment Models</strong></h3>

<ul>
	<li>Service Models: IaaS, PaaS, SaaS</li>
	<li>Deployment Models: Public, Private, Hybrid</li>
</ul>

<h3><strong>Module 4: Components of Cloud Computing</strong></h3>

<ul>
	<li>Cloud Infrastructure Overview</li>
	<li>Virtualization, VMs, Bare Metal</li>
	<li>Secure Cloud Networks</li>
	<li>Containers</li>
</ul>

<h3><strong>Module 5: Cloud Storage</strong></h3>

<ul>
	<li>Direct Attached</li>
	<li>File Storage</li>
	<li>Block Storage</li>
	<li>Object Storage</li>
	<li>Content Delivery Networks (CDN)</li>
</ul>

<h3><strong>Module 6: Cloud Native and Emergent Cloud Trends</strong></h3>

<ul>
	<li>Hybrid Multicloud</li>
	<li>Serverless</li>
	<li>Microservices</li>
	<li>Cloud Native</li>
	<li>Application Modernization</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>Basic Computer Literacy</li>
</ul>

<hr />
<section>
<h2>COURSE STAFF</h2>
</section>

<section>
<article>
<p><img alt="Rav Ahuja" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Rav.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Rav Ahuja</h3>

<p>Rav Ahuja is a Program Director for IBM Skills Network and leads the work on strategy, programs, and partnerships. He works at IBM Canada Lab in Toronto and is also a co-founder of CognitiveClass.ai, an initiative offering free courses for democratizing in-demand technology skills. He presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in AI, Data Science, Data Engineering, and Cloud Computing. Rav holds B. Eng. from McGill University and MBA from Richard Ivey School of Business, University of Western Ontario.</p>

<p>&nbsp;</p>

<h3>Other Contributors</h3>

<p>The following individuals also contributed content for this course:</p>

<ul>
	<li>James Belton</li>
	<li>Ahmad Zamer</li>
	<li>Mary Ann Johnson</li>
</ul>
</article>

Introduction to Cloud

This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives.

<section class="about">
    <h2>About This Course</h2>
    Learn the basics of <strong>Apache Hadoop</strong>, a free, open source, Java-based programming framework. <strong>Why was it invented?</strong>
    <ul>
        <li>Learn about Hadoop's architecture and core components, such as MapReduce and the Hadoop Distributed File System (HDFS).</li>
        <li>Learn how to add and remove nodes from Hadoop clusters, how to check available disk space on each node, and how to
            modify configuration parameters.</li>
        <li>Learn about other Apache projects that are part of the Hadoop ecosystem, including Pig, Hive, HBase, ZooKeeper, Oozie,
            Sqoop, Flume, among others.  BDUprovides separate courses on these other projects, but we recommend you start
            here.</li>
    </ul>
</section>
<h2>Course Syllabus</h2>
<ul>
    <li><strong>Module 1 -  Introduction to Hadoop</strong>
        <ol>
            <li>Understand what Hadoop is</li>
            <li>Understand what Big Data is</li>
            <li>Learn about other open source software related to Hadoop</li>
            <li>Understand how Big Data solutions can work on the Cloud</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 2 - Hadoop Architecture</strong>
        <ol>
            <li>Understand the main Hadoop components</li>
            <li>Learn how HDFS works</li>
            <li>List data access patterns for which HDFS is designed</li>
            <li>Describe how data is stored in an HDFS cluster</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 3 - Hadoop Administration</strong>
        <ol>
            <li>Add and remove nodes from a cluster</li>
            <li>Verify the health of a clusterStart and stop a clusters components</li>
            <li>Modify Hadoop configuration parameters</li>
            <li>Setup a rack topology</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 4 - Hadoop Components</strong>
        <ol>
            <li>Describe the MapReduce philosophy</li>
            <li>Explain how Pig and Hive can be used in a Hadoop environment</li>
            <li>Describe how Flume and Sqoop can be used to move data into Hadoop</li>
            <li>Describe how Oozie is used to schedule and control Hadoop job execution</li>
        </ol>
    </li>
</ul>
<h2>General Information</h2>
<ul>
    <li>This course is free.</li>
    <li>It is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be audited as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Knowledge about <a href="/courses/introduction-to-big-data/">Big Data concepts</a></li>
</ul>
<section class="prerequisites">
    <h2> Requirements</h2>
    <ul>
        <li>None</li>
    </ul>
</section>
<section class="course-staff">
    <h2>Course Staff</h2>
    <article class="teacher">
       
        <h3>Warren Pettit</h3>
        Warren has been with IBM for over 30 years. For the last 16 years, he has worked in Information Management education where
        he has been both an instructor and a course developer in the Data Warehouse and Big Data curriculums. For the nine
        years prior to his joining IBM, he was an application programmer and was responsible for developing a training program
        for newly hired programmers.
<p>&nbsp</p>
      <p>&nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone wp-image-1996" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Asma%20Desai%20pic.png"
                alt="" /></div>
        <h3>Asma Desai</h3>
        <a href="https://www.linkedin.com/in/asmadesai" target="_blank">Asma Desai</a> just recently started with IBM. She
        has developed course content for introductory Java and graph theory. Prior to course development, she worked as a consultant
        using Big Data to fight fraud.
<p>&nbsp</p>
      <p>&nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone wp-image-3371 size-thumbnail" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Daniel%20Tran.jpg"
                alt="" /></div>
        <h3>Daniel Tran</h3>
        <a href="https://www.linkedin.com/in/daniel-tran-84ab26b3" target="_blank">Daniel Tran</a> is an IBM Co-op Student
        working as a Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of
        customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them
        to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly
        developed cloud environment. He has worked with various components that deal with Big Data, including Hadoop, Pig,
        Hive, HBase, MapReduce &amp; YARN, Sqoop, Oozie, and Phoenix. He has also worked on separate courses involving Machine
        Learning. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer
        Engineering Co-op.
<p>&nbsp</p>
      <p>&nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone size-thumbnail wp-image-4107" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Leo%20Petrazickis.jpg"
                alt="" /></div>
        <h3>Leons Petrazickis</h3>
        <a href="https://ca.linkedin.com/in/leonsp">Leons Petrazickis</a> is the Ombud for Hadoop content on IBM Big Data
        U as well as the Platform Architect for Big Data U Labs. As a senior software developer at IBM, he uses Ruby, Python,
        and Javascript to develop microservices and web applications, as well as manage containerized infrastructure.

    </article>
</section>

Hadoop 101

This free Apache Hadoop course introduces you to Big Data concepts, and teaches you how to perform distributed processing of large data sets with Hadoop.

<div class="trix-content">
  <h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment. Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!<br><br><br>
</div><h1>A Look at the Project Ahead</h1><div>Once you have completed this project, you'll be able to use the Python to: </div><ul>
<li>Explore yelp business reviews dataset to perform text cleaning, vectorization, and classification </li>
<li>Use scikit-learn library tools to extract some meaning from the sentiments</li>
<li>Create a model to classify reviews based on their positive or negative  sentiments</li>
</ul><h1>What You'll Need</h1><div>Before starting this project, it'll be helpful to have the following:</div><ul><li>Basic Python knowledge</li></ul><div><br></div><h1>Instructor</h1><div>Svitlana Kramar, IBM</div>
</div>


Classification of Yelp Reviews using Sentiment Analysis

Sentiment Analysis has become a very popular tool in extracting subjective information from the social media.  It can help businesses to understand their brand, product or service better.  In this Guided Project, you will be introduced to several Natural Language Processing Techniques to help you derive some meaning from yelp business reviews, as well as to build and test a classification model that can divide these reviews based on their  polarities.

<section class="about"> <h2>About This Course</h2> <p>This course introduces you to the strange world of quantum computing. You’ll learn about quantum concepts including superposition, entanglement, and uncertainty. You’ll also perform “labs” to get a first-hand look at these concepts. At the end, there’s a quiz to test your knowledge.</p> </section> 

<h2>Course Syllabus</h2> 
<ul>
  <li>The Hall of Wonders</li>
  <li>The Superposition Lab</li>
  <li>The Entanglement Lab</li>
  <li>The Uncertainty Lab</li>
  <li>Return to the Hall of Wonders</li>
  <li>Show what you know!</li>
 </ul>

<h2>Recommended skills prior to taking this course</h2>
<p>This is a course for beginners. You do not need any special skills to succeed with this course.</p> 

<h2>Grading scheme</h2>
<ul>
 <li>The minimum passing mark for the course is 80%, the final exam is worth 100% of the course mark.</li>
 <li>You have 1 attempt to take the exam with multiple attempts per question.</li> 
</ul> 
<section class="prerequisites"> </section> <section class="course-staff"> 
<h2>Course Contributors</h2> <article class="teacher"> <div class="teacher-image"> <img src="/static/_AY_3435.jpg" align="left" style="margin:0 20 px 0" alt=""> </div>
<ul>
  <li>Katie Pizzolato, IBM</li>
  <li>Bob Suter, IBM</li>
  <li>John Preskill</li>
  <li>Spiros Michalakis</li>
</ul>

Introduction to Quantum Computing

This course introduces you to the strange world of quantum computing.

<section>
 <h2>About This Course</h2>
<p>
In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift.
<p>
  <section>
<h2>
 Learning Objectives<h2>
 </h3>
After completing this course you will be able to:
  <ul>
    <li> Understand the core principles and practice of build Cloud Native applications</li>
 <li>How to modernize existing applications to be Cloud Native</li>
 <li>How to deconstruct and monolithic application in a microservice architecture</li>
 <li>Build and deploy containers to a Kubernetes cluster</li>
 <li>Understand the guiding principles and benefits of a CI/CD pipeline</li>
 <li> How to build a CI/CD pipeline</li>
 <li>The benefits of adopting a hybrid-cloud and multicloud architectures</li>
</ul>
   <p>
     <p>
  <section>
 <h2> Syllabus </h2>
  <ul>
     <p>
 <strong>Module 1: Cloud Native and Multicloud Concepts and Goals <strong>
   <ul>
<li>Course Welcome Video</li>
<li>Module Introduction and Learning Objectives</li>
<li>What is Cloud Native</li>
<li>What are Cloud Native Applications?</li>
<li>Achieving Agility</li>
<li>Maximizing Operability with DevOps</li>
<li>Ensuring Observability for DevSecOps</li>
<li>Increasing Resiliency</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
</ul>
<p>
 <p> 
<strong>Module 2: Migrating Apps to Advantage Cloud Infrastructure <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>Considerations and benefits of adopting Cloud Native</li>
<li>Paths for Application Modernization</li>
<li>Application Migration using Containers</li>
<li>Data Migration to Cloud</li>
<li>Demo: Application Migration Planning</li>
<li>Demo: Data Migration (Aspera on IBM Cloud)</li>
<li>Lab</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
  </ul>
<p>
  <p>
<strong>Module 3: Modernizing applications to be CN <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>What is Architectural Modernization and its Benefits?</li>
<li>Adopting a Microservices Architecture</li>
<li>Reactive programming with Serverless</li>
<li>Why are Containers so important?</li>
<li>Leveraging Container Orchestration Platforms</li>
<li>Using the IBM Garage Method to Modernize</li>
<li>Demo: Using the IBM Garage Method to Modernize</li>
<li>Managing Cloud Native Applications on Kubernetes</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 4: Applying CI/CD to CN applications <strong>
   <ul>
  <li>Module Introduction and Learning Objectives</li></li>
<li>What are the Benefits of employing CI/CD</li>
<li>Automating Validation</li>
<li>Configuring Infrastructure using GitOps</li>
<li>Improving Observability through CI/CD</li>
<li>Building and Deploying to the Cloud</li>
<li>Demo: IBM Toolchains (IBM Cloud with Tekton)</li>
<li>Building Pipelines with Tekton</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 5: Managing Applications in Multicloud Deployments <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>Understanding HC, MC, and Hybrid MC</li>
<li>What is MCM and its use cases?</li>
<li>What are the capabilities of MCM?</li>
<li>Utilizing Hybrid Cloud Platforms to enable MCM</li>
<li>Automating MC apps</li>
<li>Demo: Cloud Pak for MultiCloud Manager</li>
<li>Exploring the OpenShift Advanced Web Console</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 6: Final Exam<strong>
  <ul>
  <li>Final Exam</li>
</ul>
<p>
   <section>
<h2>General Information</h2>
<ul>
 <li>This course is free.</li>
 <li>It is self-paced.</li>
 <li>It can be taken at any time.</li>
 <li>It can be audited as many times as you wish.</li>
 <li>There is only <strong>ONE</strong> chance to pass the course, but multiple attempts per question</li>
</ul>
     <p>
   <section>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 <li> Basic Math</li>
</ul>
<section>
</section>
<h2>Course Staff</h2>
<article>
</article>
  <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Matt%20pic.png" alt="Building Cloud Native and Multicloud Applications Instructor" width="214" height="214" />
  <div><strong>Matt Rutkowski</strong><p>
</p>&nbsp <p>An STSM and Master Inventor at IBM developing open infrastructure and industry standards along with open source for over 20 years in areas including Banking, Digital Media and Entertainment, Government and specializing in Cloud for the last 9 years. Most recently, he is the IBM lead for and a Committer to the Apache OpenWhisk Serverless computing project at Apache Software Foundation (ASF) serving on its Project Mgmt. committee and as a Committer. In addition, he has worked on Cloud Orchestration, Security, Audit and Compliance standards. Specifically, he has Chaired and been Lead Editor for such standards as OASIS Topology Orchestration for Cloud Applications (TOSCA), OASIS CloudID and DMTF Cloud Auditing (CADF) which he founded. Furthermore, he has contributed to implementations of these standards within communities such as OpenStack.</p></div> 
<p>
</p>
&nbsp
<p>
</p>
<img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/billy_korando%20(1).png" alt="Building Cloud Native and Multicloud Applications Instructor" width="214" height="214" />
  <div><strong>Billy Korando </strong><p>
</p>&nbsp <p>Java Spring developer with over nine years of experience. I have been involved in the initial conception to the design, coding, and push to production of enterprise web applications as well as major updates to existing web applications. I enjoy learning about software development, improving team development practices, and mentoring junior developers.</p></div> 

Building Cloud Native and Multicloud Applications

In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift.

<section class="about">
  <h2>About This Course</h2>
  <p>Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers.

In this course in the Big Data University you will learn the basics to get started with Predictive Modeling.</p>
  
  <h2>Course Syllabus</h2>
  <h3>After completing this course, you should be able to:</h3><br />

  <p>- Describe what Predictive Modeling is all about and know why you would want to use it<br />
    - Understand the CRISP-DM methodology and the IBM SPSS Modeler Workbench<br />
    - Understand Common Modeling Techniques<br />
    - Use IBM SPSS Modeler to solve a Kaggle competition<br />
  	- Explore, Prepare, Model and Evaluate your data using IBM SPSS Modeler</p>
  
  
</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None</p>
  
  <h2>Recommended skills prior to taking this course</h2>
  <p>Basic knowledge of business statistics (recommended but not required)</p>
  
  <h2>Grading Scheme</h2>
  <p>The minimum passing mark for the course is 60%, where the final test is worth 100% of the course mark. You have 3 attempts to take the test</p>
</section>

<section class="course-staff">
  <h2>About your instructors</h2>
 

    <h3>Mikhail Lakirovich</h3>
    <p>Mikhail Lakirovich is an Advisory Data Scientist, Strategy Consulting at IBM. He joined IBM in 2014 and worked as a Technical Product Marketing Manager. Prior to his work at IBM, Mikhail worked as a marketing manager at Baxter International Inc.</p>
 


    <h3>Armand Ruiz</h3>
    <p>Armand Ruiz is the product manager of Advanced Analytics at IBM. He joined IBM in 2011 and has worked as a Product Engineer, Research Developer for Smarter Cities, Developer Advocated for IBM Data Science, and Technical Product Manager for SPSS Programmability and Extensibility. Prior to IBM, Armand worked as a Data Scientist at Vodafone.</p>
 
  

    <h3>Greg Filla</h3>
    <p>Greg Filla is a Product manager intern - SPSS at IBM. He is a Masters Candidate - Predictive Analytics at DePaul University. Prior to this work, Greg was a ACA HR Analyst at LaSalle Network, and a Retirement Plan Specialist at RPS Benefits.</p>
  </article>
</section>

Predictive Modeling Fundamentals I

The Predictive Modelling Fundamentals I course where students can take this course many times as they wish and is totally free. After the students completed the course, they will be to print their online certificate of achievement.   Labs are available for download only for Free trial of IBM SPSS Modeler.

<section class="about">
<h2>About This Course</h2>
This chatbot course provides a practical introduction that will teach you everything you need to know to plan, build, and deploy your first chatbot.

Leveraging the cognitive computing power of Watson Conversation, you will be able to design your own chatbot without the need to write any code. You'll also learn how to quickly deploy your chatbot on WordPress-based sites.

If you want to learn how to create virtual assistants — and perhaps create one for your own business — this free chatbot course is for you.

</section>
<h2>Course Syllabus</h2>

 	<b>Module 1 - Introduction to Chatbots</b>
<ul>
 	<li>What are chatbots?</li>
 	<li>Emergence of Messengers and Cognitive Computing</li>
 	<li>Who are chatbots for?</li>
 	<li>The chatbot we're going to build</li>
</ul>
<b>Module 2 - Introduction to Watson Conversation</b>
<ul>
 	<li>Watson Conversation components</li>
 	<li>Intents</li>
 	<li>Entities</li>
 	<li>Dialog</li>
 	<li>DO and DO NOT of dialog design</li>
</ul>
<b>Module 3 - Building the Flower Shop Chatbot</b>
<ul>
 	<li>Creating a workspace</li>
 	<li>Defining intents</li>
 	<li>Defining entities</li>
 	<li>Creating the dialog</li>
 	<li>Trying out our Chatbot</li>
 	<li>Iteratively improving the Chatbot</li>
</ul>

 	<class="p1"><b>Module 4 - Deploying Our Chatbot on WordPress</b>
<ul>
 	<li>Introduction to the WordPress plugin</li>
 	<li>Installing the plugin</li>
 	<li>Configuring the settings</li>
 	<li>Testing our Chatbot</li>
</ul>
</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 	<li>None</li>
</ul>
<h2>Grading scheme</h2>
<ul>
 	<li>The minimum passing mark for the course is 70%, where the review questions are worth 50% and the final exam is worth
50% of the course mark.</li>
 	<li>You have 1 attempt to take the exam with multiple attempts per question.</li>
</ul>
<section class="prerequisites">
<h2>Requirements</h2>
None

</section>
<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <p>
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Antonio%20Cangiano%20pic.png" alt="" />
    </p>
    <h3>Antonio Cangiano</h3>
    <p>Antonio is a Software Developer and Technical Evangelist with IBM. A published author with Wrox and The Pragmatic Bookshelf, he enjoys web programming, cutting edge technologies, and technical marketing. He is the Chief Cognitive Computing Evangelist for Cognitive Class.</p>
  </article>
</section>

Build Your Own Chatbot

Learn how to build chatbots without having to write any code by leveraging Watson Assistant. Then deploy your chatbot to a real site in less than five minutes.

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>This course introduces you to the IBM Cloud. You will learn about the many offerings and services on IBM Cloud that make it the most open and secure public cloud for developers and enterprises.</p>

<p>The course begins with an introduction to the IBM Cloud platform which covers topics such as data center locations and configuring identity and access management. You will discover the various Infrastructure-as-a-Service (IaaS) options available on IBM Cloud. Next, you will learn about the deployment options on IBM Cloud; this includes topics such as Containers, Kubernetes, and OpenShift. You will also become familiar with IBM Cloud services such as Databases, Artificial Intelligence and Watson, Blockchain, Internet of Things, and many others.</p>

<p>In addition to videos, you will also see demos of various IBM Cloud features and services in action, as well as perform hands-on labs to gain practical experience with IBM Cloud at no charge.</p>

<p>This course is of interest to anyone who wants to be a cloud practitioner and use Cloud skills as developers, architects, system engineers, network specialists, and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving, and managing cloud solutions. This course is also suitable for learners who want to prepare for IBM Cloud Foundations Certification.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course, a learner will be able to:</p>

<ul>
	<li>Understand the different infrastructure services available on IBM Cloud.</li>
	<li>Access IBM Cloud using graphical interfaces, command line tools, and APIs.</li>
	<li>Discover appropriate IBM Cloud services available to deliver specific functionality.</li>
	<li>Articulate the different ways IBM Cloud delivers services to developers and operational teams.</li>
	<li>Summarize core groups of available database, integration, analytics, artificial intelligence, and DevOps services.</li>
	<li>Deploy an application on IBM Cloud using a Starter Kit.</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module 1 - Introduction to IBM Cloud</strong></p>

<ul>
	<li>IBM Cloud Overview</li>
	<li>Locations, Regions, and Zones</li>
	<li>Account Types and Support Plans</li>
	<li>Billing and Usage</li>
	<li>Cost Estimator</li>
	<li>IAM</li>
</ul>

<p><strong>Module 2 - Infrastructure (Compute, Networking, and Storage)</strong></p>

<ul>
	<li>Virtual Servers and Bare Metal</li>
	<li>Block and File Storage</li>
	<li>Object Storage</li>
	<li>Network Services</li>
	<li>Virtual Private Cloud</li>
	<li>VMWare</li>
</ul>

<p><strong>Module 3 - Deploying Applications</strong></p>

<ul>
	<li>Containers and Kubernetes</li>
	<li>OpenShift</li>
	<li>Cloud Foundry</li>
	<li>Cloud Functions</li>
</ul>

<p><strong>Module 4 - Services on IBM Cloud</strong></p>

<ul>
	<li>Databases</li>
	<li>Integration</li>
	<li>Artificial Intelligence</li>
	<li>Analytics</li>
	<li>DevOps</li>
	<li>Blockchain</li>
	<li>Internet of Things</li>
	<li>Cloud Paks</li>
</ul>

<p><strong>Module 5 - Final Exam</strong></p>

<ul>
	<li>Final Exam</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<p>There are no hard requirements needed to be able to take the course, however a foundation level understanding of Cloud Computing will help, such as that obtained from the&nbsp;Introduction to Cloud course (CC0101EN)</p>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>None</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>
</section>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Steve%20Martinelli.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Steve Martinelli</h3>

<section><strong>Steve Martinelli</strong>&nbsp;is a a Senior Technical Staff Member (STSM) at IBM, where he&#39;s focused on delivering developer advocacy content. Prior to this role he worked on upstream OpenStack features; internal tools for generating compatibility reports across all IBM products; and developing WebSphere Application Server&rsquo;s configuration tools. Aside from whipping up features and fixing bugs he tends to find himself in leadership roles where his knack for organization can be used for good.</section>

<section>&nbsp;</section>

<section>
<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Horea.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Horea Porutiu</h3>

<section><strong>Horea Porutiu&nbsp;</strong>is a software engineer interested in artificial intelligence, data science, and cloud. In my personal time, I enjoy playing tennis and surfing.</section>
</article>

IBM Cloud Essentials V3

Get hands-on experience with IBM Cloud, Cloud Foundry, and best practices for agile and test-driven development.

<section class="about">
<h2>About This Course</h2>

<p>From social media to news articles to machine logs, text data is everywhere. This class will teach you about Information Extraction: how to extract structured data from text in order to derive valuable insights. You will learn about information extraction applications in various domains, such as social media, healthcare analytics, and financial risk analysis. You will explore common text analytics tasks, including entity, relation, and event extraction, as well as sentiment analysis. Finally, you will dive into &quot;Declarative Information Extraction&quot;, a powerful method for doing high-performance and high-quality text analytics, and gain hands-on experience writing your own extractors.</p>
</section>

<hr />
<h2>What will I get after passing this course?</h2>

<ul>
	<li>You will receive a completion certificate.</li>
	<li>You will receive the <a href="https://cognitiveclass.ai/badges/text-analytics-level-1"><strong>Text Analytics - Level 1</strong></a> badge.</li>
</ul>

<hr />
<h2>Course Syllabus</h2>

<ul>
	<li>
	<h4><strong>Module 1</strong>&nbsp;- Getting to Know Information Extraction</h4>
	</li>
	<li>
	<h4><strong>Module 2</strong>&nbsp;- Limitations in Information Extraction</h4>
	</li>
	<li>
	<h4><strong>Module 3</strong>&nbsp;- Getting to Know SystemT</h4>
	</li>
	<li>
	<h4><strong>Module 4</strong>&nbsp;- Information Extraction with AQL</h4>
	</li>
	<li>
	<h4><strong>Module 5</strong>&nbsp;- AQL Basics</h4>
	</li>
	<li>
	<h4><strong>Module 6</strong>&nbsp;- Advanced AQL</h4>
	</li>
	<li>
	<h4><strong>Module 7</strong>&nbsp;- Declarative Information Extraction and the SystemT Optimizer</h4>
	</li>
	<li>
	<h4><strong>Module 8</strong>&nbsp;- Best Practices</h4>
	</li>
</ul>

<hr />
<h2>General Information</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be taken as many times as you wish.</li>
</ul>

<hr />
<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>None</li>
</ul>

<section class="prerequisites">&nbsp;</section>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Yunyao Li" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Yunyao%20Li%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/yunyao-li-979a337" rel="noopener" target="_blank">Yunyao Li</a>&nbsp;</h3>

<p><a href="https://www.linkedin.com/in/yunyao-li-979a337" rel="noopener" target="_blank">Yunyao Li</a>&nbsp;is a Principle Research Staff Member and Senior Research Manager at &nbsp;<a href="http://www.almaden.ibm.com/">IBM Almaden Research Center</a>&nbsp;where she manages the&nbsp;<a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a>&nbsp;department. She is also a Master Inventor and a member of IBM Academy of Technology.&nbsp; Her expertise is in the interdisciplinary areas of natural language processing, databases, human-computer interaction, and information retrieval. &nbsp;She is a founding member of&nbsp;<a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a>, a state-of-the-art NLP system currently powering multiple IBM products, and numerous projects. She received her PhD and master degrees from&nbsp;&nbsp;<a href="http://www.umich.edu/">the University of Michigan</a>&nbsp;Ann Arbor and undergraduate degrees from&nbsp;<a href="http://www.tsinghua.edu.cn/eng/index.jsp">Tsinghua University</a>, Beijing, China.&nbsp; You can read about Yunyao&#39;s inspiring story from small-town China to Silicon Valley&nbsp;<a href="http://www.cbronline.com/news/internet-of-things/cognitive-computing/small-town-china-silicon-valley-giant-amazing-story-one-ibm-researcher" rel="noopener" target="_blank">here</a>.&nbsp;&nbsp;Follow her on Twitter&nbsp;<a href="http://yunyao_li/">@yunyao_li.</a></p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="Laura Chiticariu" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Laura%20Chiticariu%20pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/laura-chiticariu" rel="noopener" target="_blank">Laura Chiticariu</a>&nbsp;&nbsp;</h3>

<p><a href="https://www.linkedin.com/in/laura-chiticariu" rel="noopener" target="_blank">Laura Chiticariu</a>&nbsp;is the Chief Architect of Watson Knowledge and Language Foundation, with technical leadership responsibilities over Watson Natural Language Understanding, Watson Knowledge Studio and Watson Knowledge Graph. Laura is a core member of the&nbsp;<a href="http://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a>, R&amp;D team, and strongly&nbsp;believes in the notion of &quot;Transparent NLP&quot;: leveraging machine learning techniques, while ensuring that the NLP system remains transparent - easy to comprehend, debug and enhance.&nbsp;She holds a Ph.D. in Computer Science, and has been teaching NLP across universities within and outside the U.S.</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="Marina Danilevsky" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Marina%20Danilevsky%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/marina-danilevsky-a083b63" rel="noopener" target="_blank">Marina Danilevsky</a></h3>

<p><a href="https://www.linkedin.com/in/marina-danilevsky-a083b63" rel="noopener" target="_blank">Marina Danilevsky</a>&nbsp;is a Research Staff Member in the&nbsp;<a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a>&nbsp;group at&nbsp;<a href="http://www.research.ibm.com/labs/almaden/index.shtml">IBM Almaden Research Center</a>&nbsp;and a core member of the&nbsp;<a href="http://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a>&nbsp;R&amp;D team.&nbsp;She works in the intersection of data analytics, text mining, natural language processing, information networks, and human-computer interaction. She holds&nbsp;a&nbsp;Ph.D. in Computer Science from&nbsp;the&nbsp;<a href="http://www.cs.illinois.edu/">University of Illinois at Urbana-Champaign</a>&nbsp;and a B.S. in&nbsp;<a href="http://www.math.uchicago.edu/">Mathematics</a>&nbsp;from the&nbsp;<a href="http://www.uchicago.edu/">University of Chicago</a>.</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Huaiyu%20Zhu%20pic.png" style="float: left; height: 90px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/huaiyu-zhu-075608134">Huaiyu Zhu</a>&nbsp;&nbsp;</h3>

<p><a href="https://www.linkedin.com/in/huaiyu-zhu-075608134">Huaiyu Zhu</a>&nbsp;is a Research Staff Member in the&nbsp;<a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a>&nbsp;group at&nbsp;<a href="http://www.research.ibm.com/labs/almaden/index.shtml">IBM Almaden Research Center</a>. His main research focus is on text analytics, natural language processing, machine learning and statistical information processing.</p>

<p>&nbsp;</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Atsushi%20Ono%20pic.png" style="float: left; height: 88px; width: 85px" /></div>
<h3><a href="https://www.linkedin.com/in/atsushi-ono-38a62a13/">Atsushi Ono</a>&nbsp;&nbsp;</h3>


<p><a href="https://www.linkedin.com/in/atsushi-ono-38a62a13/">Atsushi Ono</a>&nbsp;is a software engineer at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. After several years of experience on business partner technical enablement missions, he has been working as a front-end developer on various projects, including contributing to the open source&nbsp;<a href="https://dojotoolkit.org/reference-guide/dojox/mobile.html">Dojo Mobile</a>&nbsp;project. He has worked on the development of&nbsp;<a href="https://www.ibm.com/cloud/watson-knowledge-studio">IBM Watson Knowledge Studio</a>&nbsp;since the project&rsquo;s inception.</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="Yuka Nomura" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Yuka%20Nomura%20pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/yukanomura">Yuka Nomura</a>&nbsp;&nbsp;</h3>

<p><a href="https://www.linkedin.com/in/yukanomura">Yuka Nomura</a>&nbsp;is a software engineer working on front-end development of&nbsp;<a href="https://www.ibm.com/watson/services/knowledge-studio/">IBM Watson Knowledge Studio</a>&nbsp;at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. She has contributed to user interface design and product development from her very first project start-up. She also specializes in robot application programming that runs on communication robots such as&nbsp;<a href="https://www.softbankrobotics.com/emea/en/pepper">Pepper</a>.</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="Chikako Oyanagi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Chikako%20Oyanagi%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3>Chikako Oyanagi</h3>

<p>Chikako Oyanagi is a front-end software developer of&nbsp;<a href="https://www.ibm.com/watson/services/knowledge-studio/">IBM Watson Knowledge Studio</a>&nbsp;at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan.</p>

<p>&nbsp;</p>

<p>&nbsp;</p>

<div class="teacher-image"><img alt="Teruki Tauchi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Teruki%20Tauchi%20Pic.png" style="float: left; height: 84px; width: 85px" /></div>

<h3>Teruki Tauchi</h3>

<p>Teruki Tauchi is a front-end software developer of&nbsp;<a href="https://www.ibm.com/cloud/watson-knowledge-studio">IBM Watson Knowledge Studio</a>&nbsp;at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. He joined IBM after obtaining a Master of Engineering degree in Computer Science from University College London in 2015.</p>

<p>&nbsp;</p>
</article>
</section>

Text Analytics 101

From social media to news articles to machine logs, text data is everywhere. This class will teach you about Information Extraction: how to extract structured data from text in order to derive valuable insights.

<div class="trix-content">
  <h1><strong>A Look at the Project Ahead:</strong></h1><ol>
<li>Run a simple Node.js app on your localhost. </li>
<li>Learn what is NPM.</li>
<li>Add some styling to your app using HTML and CSS.</li>
</ol><h1><strong>Prerequisites:</strong></h1><ul><li>Any kind of experience with JavaScript language. </li></ul><h1><strong>What you will need:</strong></h1><ul>
<li>You will just need a computer to take do this Project on. Everything else is provided to you via the IBM Skill<br>s Network Labs environment.</li>
<li>Your willingness to learn. (Most important thing)</li>
</ul><h1>Your Instructor</h1><div>Jason Song, IBM</div>
</div>


Getting Started with Node.js

Have you heard of Node.js before? Node.js is one of the most popular technology to create server-side apps. Why? Because if you know JavaScript, you build both the frontend and the backend using JavaScript. Take this Guided Project to get started on Node.js.

<section class="about">
<h2>About This Course</h2>

<p>This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You&#39;ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.</p>

<p>Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!</p>

<p><strong>Explore many algorithms and models:</strong></p>

<ul>
	<li>Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.</li>
</ul>

<ul>
	<li>Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.</li>
</ul>

<p>Get ready to do more learning than your machine!</p>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><b>Module 1 - Machine Learning&nbsp;</b><b>vs Statistical Modeling &amp; Supervised vs Unsupervised Learning</b></p>

<ul>
	<li>Machine Learning&nbsp;Languages,&nbsp;Types, and&nbsp;Examples&nbsp;</li>
</ul>

<ul>
	<li>Machine Learning vs Statistical Modelling</li>
</ul>

<ul>
	<li>Supervised vs Unsupervised Learning&nbsp;</li>
</ul>

<ul>
	<li>Supervised Learning Classification&nbsp;</li>
</ul>

<ul>
	<li>Unsupervised Learning&nbsp;</li>
</ul>

<p><b>Module 2 - Supervised Learning I</b></p>

<ul>
	<li>K-Nearest Neighbors&nbsp;</li>
</ul>

<ul>
	<li>Decision Trees&nbsp;</li>
</ul>

<ul>
	<li>Random Forests</li>
</ul>

<ul>
	<li>Reliability of Random Forests&nbsp;</li>
</ul>

<ul>
	<li>Advantages &amp; Disadvantages of Decision Trees&nbsp;</li>
</ul>

<p>&nbsp;<b>Module 3 - Supervised Learning II</b></p>

<ul>
	<li>Regression Algorithms&nbsp;</li>
</ul>

<ul>
	<li>Model Evaluation&nbsp;</li>
</ul>

<ul>
	<li>Model Evaluation: Overfitting &amp; Underfitting</li>
</ul>

<ul>
	<li>Understanding Different Evaluation Models&nbsp;</li>
</ul>

<p>&nbsp;<b>Module 4 - Unsupervised Learning</b></p>

<ul>
	<li>K-Means Clustering&nbsp;plus Advantages &amp; Disadvantages&nbsp;</li>
</ul>

<ul>
	<li>Hierarchical Clustering plus Advantages &amp; Disadvantages&nbsp;</li>
</ul>

<ul>
	<li>Measuring the Distances Between Clusters&nbsp;- Single Linkage Clustering&nbsp;</li>
</ul>

<ul>
	<li>Measuring the Distances Between Clusters&nbsp;- Algorithms for Hierarchy Clustering</li>
</ul>

<ul>
	<li>Density-Based Clustering&nbsp;</li>
</ul>

<p><b>Module 5 - Dimensionality Reduction &amp; Collaborative Filtering</b></p>

<ul>
	<li>Dimensionality Reduction: Feature Extraction &amp; Selection&nbsp;</li>
</ul>

<ul>
	<li>Collaborative Filtering &amp; Its Challenges&nbsp;</li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>R programming</li>
</ul>
</section>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free&nbsp;Data Science Hands-on with Open Source Tools.</li>
	<li>This hands-on lab requires that you have working knowledge of R programming language as it applies to data analytics. If you don&#39;t feel you have sufficient skill in Data Analysis with R, I recommend you take&nbsp;Data Analysis with R&nbsp;courses.</li>
</ul>
</section>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Saeed Aghabozorgi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Saeed%20pic.png" style="height: 100px; width: 100px; border-radius: 10px" /></div>

<h3><a href="https://ca.linkedin.com/in/saeedaghabozorgi" rel="noopener" target="_blank">Saeed Aghabozorgi</a></h3>

<p><a href="https://ca.linkedin.com/in/saeedaghabozorgi" rel="noopener" target="_blank">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients&rsquo; ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>

<h3>&nbsp;</h3>

<h3>Cognitive Class Course Development Team</h3>

<p>Thanks to the course contributors:&nbsp;<a href="https://www.linkedin.com/in/daniel-tran-84ab26b3" rel="noopener" target="_blank">Daniel Tran</a>,&nbsp;<a href="https://www.linkedin.com/in/kevin-wong-15982093" rel="noopener" target="_blank">Kevin Wong</a></p>
</article>
</section>

Machine Learning with R

Learn what machine learning is all about in this beginner-friendly course. Through videos and labs, learn how to apply different machine learning techniques such as classification, clustering, neural networks, regression, and recommender systems.

 	<section class="about">
  <h2>About This Course</h2>

Learn about privacy from 5 prominent cases of <strong>data breaches</strong> from 2012 to 2015, which highlight vulnerabilities in typical organizations.
<ul>
 	<li>Get an outline of data privacy laws.</li>
 	<li>Walk away with some guiding principles for how to stay out of trouble.</li>
 	<li>Knowing how to hack a colleague's password will raise some ethical questions. Justify your decisions in the course exam.</li>
</ul>
  
</section>
  
  <h2>Course Syllabus</h2>
<ul>
 	<li><strong>Module 1:</strong>
<ul>
 	<li><span style="font-size: 1em;line-height: 1.6em">An overview of privacy laws in Canada </span></li>
 	<li><span style="line-height: 1.6"><span style="line-height: 25.6px">Case Study:</span> </span><span style="font-size: 1em;line-height: 1.6em">Student loans data breach (Canada) -</span><strong style="line-height: 1.5"> </strong><span style="font-size: 1em;line-height: 1.6em">A breach involving the personal information of about more than half a million clients of Human Resources and Skills Development Canada (HRSDC) and 250 departmental employees</span></li>
</ul>
<strong>Module 2:</strong>
<ul>
 	<li><span style="font-size: 1em;line-height: 1.6em">An overview of the Personal Information Protection and Electronic Documents Act (PIPEDA)</span></li>
 	<li><span style="line-height: 1.6">Case Study: </span><span style="font-size: 1em;line-height: 1.6">Target Corp. (USA) - A data breach involving information on 40 million payment cards (i.e., credit, debit, and ATM cards) and personally identifiable information (PII) on 70 million customers</span></li>
</ul>
<strong>Module 3</strong>
<ul>
 	<li>Dr. Ann Cavoukian's 7 Foundational Principles of 'Privacy by Design'</li>
 	<li><span style="font-size: 1em;line-height: 1.6"><span style="line-height: 25.6px">Case Study:</span> </span><span style="font-size: 1em;line-height: 1.6em">Think W3 (UK) - A d</span><span style="font-size: 1em;line-height: 1.6em">ata breach involving 1.2 million credit and debit card details</span></li>
 	<li><span style="font-size: 1em;line-height: 1.6">Case Study: </span><span style="font-size: 1em;line-height: 1.6">Doritex Corp. (USA) - A d</span><span style="font-size: 1em;line-height: 1.6">ata breach exposed the social security numbers of over 500 job applicants</span></li>
</ul>
<strong>Module 4  </strong>
<ul>
 	<li>Data breaches and passwords</li>
 	<li><span style="font-size: 1em;line-height: 25.6px">Case Study: </span><span style="font-size: 1em;line-height: 1.6em">Home Depot (USA)</span><span style="font-size: 1em;line-height: 1.6em"> - A data breach estimated to have put payment card information at risk for approximately 56 million unique payment cards</span></li>
 	<li>Class Participation Questionnaire</li>
</ul>

</li>
</ul>
  
	<h2>Recommended skills prior to taking this course</h2>
  <ul>
    <li>None</li>
  </ul>
  <h2>Grading scheme</h2>
  <ul>
    <li>The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.</li>
    <li>You have 1 attempt to take the exam with multiple attempts per question.</li>
  </ul>


<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None.</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" align="left" style="margin:0 20 px 0" alt="Shingai Manjengwa">
    </div>

    <h3>Shingai Manjengwa</h3>
    <p>Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. </p>
  </article>
</section>


Data Privacy Fundamentals

Learn about the fundamentals of Data Privacy.

<div class="trix-content">
  <div>Reinforcement Learning is a different style of machine learning different from supervised and unsupervised learning. It is learning what to do through trial and error. Reinforcement learning is also an excellent method used to train robots to play games. OpenAI Gym is a python library that standardizes the interaction between Agent/User/Robot and the environment so you can interact with a variety of Gym environments. Reinforcement Learning and OpenAI Gym is a great combination that can be used to create games with an AI aspect.<br><br>In this Guided Project, you will learn how to interact with the OpenAI Gym environment. We will be working with a custom environment created to play TicTacToe so you will also learn how to install custom environments. Additionally, we will learn about Reinforcement Learning and the algorithm Temporal Difference Learning, and how to implement an Agent using Temporal Difference Learning to play TicTacToe. Finally, we will play TicTacToe with our trained agent and environment and see an example of a TicTacToe game with a graphical user interface.<br><br>
</div><h1><strong>Learn by Doing</strong></h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples, and exercises that can be followed and practiced in a lab environment.<br><br>
</div><div>Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job! </div><div><br></div><h1><strong>A Look at the Project Ahead</strong></h1><div>Once you have completed this project, you'll be able to:</div><ul>
<li>Install a custom OpenAI Gym environment</li>
<li>Work with an OpenAI Gym environment and the TicTacToe environment</li>
<li>Explain what Reinforcement Learning is</li>
<li>Explain what Temporal Difference Learning is</li>
<li>Create an agent that uses Temporal Difference Learning to play TicTacToe</li>
<li>Train and Test the agents using the TicTacToe environment</li>
<li>Play some games against the trained agent</li>
</ul><h1><strong>What You’ll Need  </strong></h1><div>Just a web browser and Python programming knowledge are required!</div><div>Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to OpenAI Gym which is a tool that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br><br>
</div><h1><strong>Your Instructor</strong></h1><div>Azim Hirjani, IBM</div>
</div>


Playing TicTacToe with Reinforcement Learning and OpenAI Gym

Learn how to create and teach an agent that never loses to play TicTacToe using a Reinforcement Learning algorithm called Temporal Difference Learning and Open AI Gym

<section class="about">
<h2>About This Course</h2>
Learn the fundamentals of <strong>Spark</strong>, the technology that is revolutionizing the analytics and big data world! Spark is an open source processing engine built around speed, ease of use, and analytics. If you have large amounts of data that requires low latency processing that a typical <strong>MapReduce&nbsp;</strong>program cannot provide, Spark is the way to go.

<ul>
	<li>Learn how it performs at speeds up to 100 times faster than Map Reduce for iterative algorithms or interactive data mining.</li>
	<li>Learn how it&nbsp;provides in-memory cluster computing for lightning fast speed and supports Java, Python, R, and Scala APIs for ease of development.</li>
	<li>Learn how it can handle a wide range of data processing scenarios by combining SQL, streaming and complex analytics together seamlessly in the same application.</li>
	<li>Learn how it runs on top of Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources such as HDFS, Cassandra, HBase, or S3.</li>
</ul>
</section>

<section class="course-syllabus">
<h2>Course Syllabus</h2>

<ul>
	<li><strong>Module 1 -&nbsp;Introduction to Spark - Getting started</strong>

	<ol>
		<li>What is&nbsp;Spark and what is its purpose?</li>
		<li>Components of the Spark unified stack</li>
		<li>Resilient Distributed Dataset (RDD)</li>
		<li>Downloading and installing Spark standalone</li>
		<li>Scala and Python overview</li>
		<li>Launching and using Spark&rsquo;s Scala and Python shell &copy;</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 2 -&nbsp;Resilient Distributed Dataset and DataFrames</strong>

	<ol>
		<li>Understand how to create parallelized collections and external datasets</li>
		<li>Work with Resilient Distributed Dataset (RDD) operations</li>
		<li>Utilize shared variables and key-value pairs</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 3 -&nbsp;Spark application programming</strong>

	<ol>
		<li>Understand the purpose and usage of the SparkContext</li>
		<li>Initialize Spark with the various programming languages</li>
		<li>Describe and run some Spark examples</li>
		<li>Pass functions to Spark</li>
		<li>Create and run a Spark standalone application</li>
		<li>Submit applications to the cluster</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 4 -&nbsp;Introduction to Spark libraries</strong>

	<ol>
		<li>Understand and use the various Spark libraries</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 5 -&nbsp;Spark configuration, monitoring and tuning</strong>

	<ol>
		<li>Understand components of the Spark cluster</li>
		<li>Configure Spark to modify the Spark properties, environmental variables, or logging properties</li>
		<li>Monitor Spark using the web UIs, metrics, and external instrumentation</li>
		<li>Understand performance tuning considerations</li>
	</ol>
	</li>
</ul>
</section>

<section class="information">
<h2>General Information</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>
</section>

<section class="recommended skills prior to taking this course">
<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>Basic understanding of <a href="https://bigdatauniversity.com/courses/introduction-to-hadoop/">Apache Hadoop</a> and <a href="https://bigdatauniversity.com/courses/introduction-to-big-data/">Big Data</a>.</li>
	<li>Basic Linux Operating System knowledge.</li>
	<li>Basic understanding of the <a href="http://bigdatauniversity.com/learn/scala/" target="_blank">Scala</a>, <a href="https://bigdatauniversity.com/courses/introduction-python/">Python</a>, <a href="https://bigdatauniversity.com/courses/introduction-to-r/">R</a>, or Java programming languages.</li>
</ul>
</section>

<section class="course-staff">
<h2>Requirements</h2>

<ul>
	<li>Have taken the <a href="http://bigdatauniversity.com/courses/introduction-to-hadoop/" target="_blank">Hadoop 101</a>&nbsp;course on BDU.</li>
</ul>

<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Henry Quach, Instructor of" class="alignnone wp-image-2258 " src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Henry%20Quach%20pic.jpg" /></div>

<h3>Henry L. Quach</h3>

<p><a href="https://www.linkedin.com/in/henry-quach-87b60846" target="_blank">Henry L. Quach</a> is the Technical Curriculum Developer Lead for Big Data. He has been with IBM for 9 years focusing on education development. Henry likes to dabble in a number of things including being part of the original team that developed and designed the concept for the IBM Open Badges program. He has a Bachelor of Science in Computer Science and a Master of Science in Software Engineering from San Jose State University.</p>

<p>&nbsp;</p>
</article>

<article class="teacher">
<div class="teacher-image"><img alt="Alan Barnes" class="alignnone size-medium wp-image-2177" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alan%20Barnes%20pic.png" /></div>

<h3>Alan Barnes</h3>
<a href="https://www.linkedin.com/in/alan-barnes-5b342330" target="_blank">Alan Barnes</a> is a Senior IBM Information Management Course Developer / Consultant. He has worked in several companies as a Senior Technical Consultant, Database Team Manager,&nbsp;Application Programmer, Systems Programmer, Business Analyst, DB2 Team Lead and more. His career in IT spans more than 35 years.</article>
</section>

Spark Fundamentals I

Ignite your interest in Spark with an introduction to the core concepts that make this general processor an essential tool set for working with Big Data.

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<section>Expand your knowledge of the concepts discussed in&nbsp;Spark Fundamentals I&nbsp;with a focus on&nbsp;<strong>RDDs</strong>&nbsp;(Resilient Distributed Datasets). RDDs are&nbsp;the main abstraction Spark provides to enable parallel processing across the nodes of a Spark cluster.</section>

<ul>
	<li>Get in-deptth knowledge on Spark&rsquo;s architecture and how data is distributed and tasks are parallelized.</li>
	<li>Learn how to optimize your data for joins using Spark&rsquo;s memory caching.</li>
	<li>Learn how to use the more advanced operations available in the API.</li>
	<li>The lab exercises for this course are performed exclusively on the Cloud and using a Notebook interface.</li>
</ul>

<section>IBM Data Science Experience&nbsp;provides you with Jupyter notebooks that is already connected to Spark and supports Python, R, and Scala so that you start creating your Spark projects and collaborating with other data scientists. When you sign up, you get free access to Data Science Experience and all other IBM services for&nbsp;<strong>30 days</strong>. Start now and take advantage of this offer.

<hr />
<h2>COURSE SYLLABUS</h2>
</section>

<section>
<article>
<ul>
	<li><strong>Module 1 - Introduction to Notebooks</strong>

	<ol>
		<li>Understand how to use Zeppelin in your Spark projects</li>
		<li>Identify the various notebooks you can use with Spark</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 2 - Spark RDD Architecture</strong>

	<ol>
		<li>Understand how Spark generates RDDs</li>
		<li>Manage partitions to improve RDD performance</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 3 - Optimizing Transformations and Actions</strong>

	<ol>
		<li>Use advanced Spark RDD operations</li>
		<li>Identify what operations cause shuffling</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 4 - Caching and Serialization</strong>

	<ol>
		<li>Understand how and when to cache RDDs</li>
		<li>Understand storage levels and their uses</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 5 - Develop and Testing</strong>

	<ol>
		<li>Understand how to&nbsp;use sbt to build Spark projects</li>
		<li>Understand how to use Eclipse and IntelliJ for Spark development</li>
	</ol>
	</li>
</ul>
</article>

<section>
<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited&nbsp;as many times as you wish.</li>
</ul>

<article>
<article>
<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>Basic understanding of&nbsp;<a href="https://cognitiveclass.ai/courses/introduction-to-hadoop/">Apache Hadoop</a>&nbsp;and&nbsp;<a href="https://cognitiveclass.ai/courses/introduction-to-big-data/">Big Data</a>.</li>
	<li>Have taken the&nbsp;<a href="https://cognitiveclass.ai/courses/what-is-spark/">Spark Fundamentals I</a>&nbsp;course on BDU.</li>
	<li>Basic understanding of the&nbsp;<a href="https://cognitiveclass.ai/learn/scala/" rel="noopener" target="_blank">Scala</a>,&nbsp;<a href="https://cognitiveclass.ai/courses/introduction-python/">Python</a>,&nbsp;<a href="https://cognitiveclass.ai/courses/introduction-to-r/">R</a>, or Java programming languages.</li>
	<li>Basic Linux Operating System knowledge.</li>
</ul>
</article>
</article>

<section>
<hr />
<h2>REQUIREMENTS</h2>

<ul>
	<li>None</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>

<article>
<p><img alt="Henry Quach, Instructor of" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Henry%20Quach%20pic.jpg" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Henry L. Quach</h3>

<p><a href="https://www.linkedin.com/in/henry-quach-87b60846" rel="noopener" target="_blank">Henry L. Quach</a>&nbsp;is the Technical Curriculum Developer Lead for Big Data. He has been with IBM for 9 years focusing on education development. Henry likes to dabble in a number of things including being part of the original team that developed and designed the concept for the IBM Open Badges program. He has a Bachelor of Science in Computer Science and a Master of Science in Software Engineering from San Jose State University.</p>

<p>&nbsp;</p>
</article>

<article>
<p><img alt="Alan Barnes" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alan%20Barnes%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Alan Barnes</h3>

<p><a href="https://www.linkedin.com/in/alan-barnes-5b342330" rel="noopener" target="_blank">Alan Barnes</a>&nbsp;is a Senior IBM Information Management Course Developer / Consultant. He has worked in several companies as a Senior Technical Consultant, Database Team Manager,&nbsp;Application Programmer, Systems Programmer, Business Analyst, DB2 Team Lead and more. His career in IT spans more than 35 years.</p>
</article>
</section>

Spark Fundamentals II

Building on your foundational knowledge of Spark, take this opportunity to move your skills to the next level. With a focus on Spark Resilient Distributed Data Set operations this course exposes you to concepts that are critical to your success in this field.

<section class="about"> <h2>ABOUT THIS COURSE</h2> <p>This course covers Golang operator reconciliation, use of OLM, and use of Scorecard.</p>
<p>
<strong>Reconciliation</strong></p><p>
Operators mimic the behavior of core Kubernetes components in many ways, chief among them the process of reconciliation. When a user creates an instance of your operator's type, one or more controllers is responsible for creating that desired state. This course covers the creation of a basic reconcile loop in Golang to create a simple Memcached operator, similar to the Golang operator from the Intermediate course.</p>
<p>
<strong>Operator Lifecycle Manager (OLM)</strong></p><p>
OLM is a tool for installing and upgrading operators on a Kubernetes cluster. Operator authors create a Bundle for each version of their operator, which is then published and consumed by OLM. This course covers the creation of a bundle for the simple Memcached operator from above, and then covers the upgrade case by adding a new field, implements migration via conversion webhook, creates a new bundle,  and upgrades the deployed operator in-place.</p>
<p>
<strong>Scorecard</strong></p><p>
Scorecard is an Operator-sdk command for scaffolding and running test frameworks on a deployed operator. This course first covers using Scorecard to automatically generate and run a basic verification test suite. Then, it shows how to create your own custom test suite with a non-trivial unit test for the above simple Memcached operator.</p> <hr />

  <h2>LEARNING OBJECTIVES</h2> 
  <p>In this course you will learn about:</p> <ul> <li>How reconciliation logic works in Kubernetes controllers</li> <li>Constructing your own reconcile loop in a Golang operator</li> <li>Getting an introduction to the Operator Lifecycle Manager (OLM) </li> <li>Deploying and managing an operator using OLM <li>Using Scorecard to create a test suite for your operator</li> </ul>
 
<h2>COURSE SYLLABUS</h2> 
<p><strong>Module&nbsp;1 - Golang Operator Reconciliation </strong></p> <ul> <li>Overview of Kubernetes controller reconciliation </li> <li>Kubernetes' declarative API</li> <li>Explanation of Memcached operator reconciliation via finite state diagram</li> 
  
<li>Hands-on Lab</li><ul> <li>Build a simple Golang operator with Operator SDK</li> <li>Write reconciliation loop using controller-runtime library</li></ul>
  
 </ul> <p><strong>Module&nbsp;2 - Overview of Operator Lifecycle Manager (OLM) </strong></p> <ul> <li>What is OLM?</li> <li>What is an operator bundle</li> 
  
 <li>Hands-on Lab&nbsp;1</li><ul> <li>Install OLM on your cluster</li><li>Package a Golang operator with OLM</li><li>Deploy it to the cluster using OLM</li></ul> 
  
  <li>Hands-on Lab&nbsp;2</li><ul> <li>Update operator with a breaking change</li> <li>Create a conversion webhook to manage the upgrade</li> <li>Use OLM to upgrade operator in-place</li></ul> </ul> 
    
  <p><strong>Module&nbsp;3 - Testing your operator with Scorecard</strong></p> <ul> <li>Overview of Scorecard, an Operator SDK tool for building test images for a deployed operator</li> 
  <li>Hands-on Lab</li><ul> <li>Build an Ansible operator with Operator-sdk</li><li>Write an integration test for Memcached operator</li></ul> </ul> <hr />
  
<h2>REQUIREMENTS</h2> 
<p>Basic familiarity with Kubernetes, including using kubectl to create resources on a Kubernetes cluster. You should know how to create, update, and delete Kubernetes resources such as Pods and Services.Understanding of the basic construction, deployment, and use of operators
  Completion of the Intermediate course on Kubernetes operators</p> <p> The labs in this course cover the creation, deployment, and use of operators on a Kubernetes cluster. It assumes you are already familiar with the basics of Kubernetes, such as using kubectl to create resources on a Kubernetes cluster. </p> <p><h3>The requirements for the hands-on labs are as follows:</h3></p> <ul> <strong>Requirements for all hands-on labs </strong><ul><li>Operator SDK v1.5.0+ installed</li> <li>Kubectl v1.17.0+ installed</li> <li>Admin access to a Kubernetes cluster. Note that the version of Operator Lifecycle Manger currently(8/23/21) on IMB Cloud's Kubernetes Service is incompatible with this tutorial. For running a cluster locally, see the Minikube docs.</li> <li>Docker v3.2.2+ installed</li> <li>Access to a Docker image repository such as Docker Hub or quay.io</li><li>Golang v1.16.0+ installed</li></p> </ul>
<strong>Module&nbsp;1 </strong><ul> <li>No additional requirements</li>
</ul> 
<strong>Module&nbsp;2 </strong><ul> <li>Operator Lifecycle Manager installed on your Kubernetes cluster. The Operator-sdk command operator-sdk olm install will install a compatible version of OLM on your cluster. Note that currently(8/23/21) the version of OLM installed on IBM Cloud IKS clusters is incompatible with this tutorial.</li> 
</ul> 
<strong>Module&nbsp;3 </strong><ul> <li>No additional requirements</li> 
</ul> <hr />

<h2>COURSE INSTRUCTORS</h2>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Jonathan%20Berkhahn%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p> <p><a href="https://www.linkedin.com/in/jonathan-berkhahn-285779103"><strong>Jonathan Berkhahn </strong></a>&nbsp;is an open source software contributor for IBM. He’s worked on various OSS cloud computing projects such as Cloud Foundry and Kubernetes and currently contributes to Operator-sdk and related projects.</p> <p>&nbsp;</p> <hr /> 

Kubernetes Operators Advanced

This course covers advanced operator topics including reconciliation, use of Operator Lifecycle Manager to deploy and upgrade operators, and use of Scorecard to test operators.

<div class="trix-content">
  <div><br></div><h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment.<br><br>
</div><div>Hands-on skills are highly sought out by employers when determining job readiness. </div><div>
<br>Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!<br><br>
</div><h1>A Look at the Project Ahead</h1><div>Once you have completed this project, you'll be able to:</div><ul>
<li>Create lightweight, standardized apps using Docker containers</li>
<li>Use the IBM Cloud command-line interface</li>
<li>Upload your containers to IBM Cloud Container Registry</li>
<li>Deploy your containers on IBM Code Engine</li>
</ul><h1>What You’ll Need </h1><div>To do this project, you will need the following:</div><ul>
<li>An IBM Cloud account (<a href="https://cloud.ibm.com/registration">sign up for a free account</a>)</li>
<li>A very basic knowledge of Python</li>
</ul><h1>Your Instructor</h1><div>Arnav Shah, IBM</div>
</div>


Deploy a Web Server Using Python and IBM Code Engine

In this guided project, you'll learn how to take any project you've built, containerize it, and easily deploy it to a Code Engine server. 

<div class="trix-content">
  <div>You will be using the Django web framework to build a full-stack movie recommender app.  In this app, you can save your movie watch history, and review the recommended movies from more than 40,000 movies. The recommendation algorithm included in this project is very simple but effective so you do not even need any prior Machine Learning knowledge to understand and complete this project.<br><br><br>
</div><h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples, and exercises that can be followed and practiced in a lab environment.</div><div>Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!</div><div>
<br><br>
</div><h1>A Look at the Project Ahead</h1><div>After completing this project, you will be able to<br>-  Describe the key components of the Django web framework <br>-  Describe the basic principles of a recommendation system <br>-  Build a simple but effective personal movie recommender with Django<br><br><br>
</div><h1>
<strong>What You’ll Need  </strong> </h1><div>Firstly, you just need a web browser!  Regarding prior skills,  you will also need basic Python programming knowledge to write the backend and some basic HTML/CSS knowledge to create the frontend. For the recommendation algorithm part, you only need to have a basic understanding of the Set data structure<br><br>Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. <br><br>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br><br><br>
</div><h1>Your Instructors</h1><div>Yan Luo, IBM<br><br>
</div>
</div>


Build Your Movie Recommender with Django

Choosing a good movie to watch on a weekend evening is always hard.  It would be great if there is a 
handy movie recommender to help you make a good decision.  In this project, you will be building a personal movie recommender using the popular Django web framework.

<section class="about">
  <h2>About This Course</h2>
  <p>Data Science is like triathlon. Programming is cycling, by far the biggest investment is required in hardware and software. Running is domain expertise and communication skills and, swimming is mathematics, statistics and modelling. There are competitions in each of these disciplines cycling, running and swimming (and there always will be), but the need for super athletes who can do all 3 is growing. An athlete who is brilliant at one discipline can learn the other two and succeed in triathlon. </p>

  <p>No matter your core discipline(s), this course will take you through a short triathlon. You will define a business problem, establish the data required to solve it, you will write scripts in R programming language to build a model to give you insights and you will learn to present your findings in a business format to a business audience. </p>
</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>  Students will benefit from having a basic understanding of Statistics.
    
    Students with a basic knowledge of how Search Engines work may have a better appreciation for the case study context however, this is not a requirement.
</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>
  
    <h3>Fireside Analytics Inc.</h3>
    <p>Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on Facebook, Twitter and LinkedIn. </p>
  </article>



<section class="faq">
  <section class="responses">
    <h2>Frequently Asked Questions</h2>
    <article class="response">
      <h3>What web browser should I use?</h3>
      <p>The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.</p>
      <p>See our <a href="http://edx.readthedocs.org/en/latest/browsers.html">list of supported browsers</a> for the most up-to-date information.</p>
    </article>

    <article class="response">
      <h3>Will I have to install new software in this course?</h3>
      <p>All the software you will need can be accessed through your browser at www.datasicentistworkbench.com. You will need to register and create an account. If you choose to not use Data Scientist Workbench, you may need to install R on your local computer.</p>
    </article>
  </section>
</section>

Digital Analytics & Regression

This course uses a case study approach to take you through the end to end process of identifying a business objective, designing the model to address it, sourcing the data and ultimately arriving at the insights. When you complete this course, you can apply these methods and principles in a variety of contexts, with big, medium or small data.

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>This course serves as an introduction to Kubernetes operators. Operators are a design pattern for building and deploying an application onto a Kubernetes cluster such that
it can be used in the same manner as core Kubernetes resources. It covers the internal architecture of operators and how they fit into the larger Kubernetes ecosystem, as
well as how to build basic operators using Operator-sdk. Operator-sdk is a command line tool for scaffolding operators, generating the code skeleton and Kubernetes yaml needed
to deploy the operator.</p>
<p>This course is of interest to anyone who wants to learn how to deploy and manage applications on Kubernetes using a more Kubernetes-native style, such as Kubernetes application
devlopers, architects, or operators. It is also of interest to consumers of Kubernetes applications users interested in learning how some of the applications they use work internalally.
It features hands-on labs that have you constructing and deploying real operators on a live Kubernetes cluster.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>In this course you will learn about:</p>

<ul>
	<li>What a Kubernetes operator is</li>
<li>Why you would want to use an operator</li>
<li>The basic internal architecture of an operator</li>
<li>How to build a basic operator using<ul>
<li>Golang</li>
<li>Helm</li>
<li>Ansible</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module&nbsp;1 - Intro to Operators and Building your first Golang operator

</strong></p>

<ul>
	<li>Overview of operators</li>
<li>Overview of basic Kubernetes architecture</li>
<li>Architecture of an operator</li>
<li>Hands-on Lab</li><ul>
<li>Build a Golang operator with Operator-sdk</li></ul>
</ul>

<p><strong>Module&nbsp;2 - Building your first Helm operator</strong></p>

<ul>
	<li>Overview of Helm operators</li>
<li>Hands-on Lab</li><ul>
<li>Build a Helm operator with Operator-sdk</li></ul>
</ul>

<p><strong>Module&nbsp;3 - Building your first Ansible operator</strong></p>

<ul>
	<li>Overview of Ansible operators</li>
<li>Hands-on Lab</li><ul>
<li>Build an Ansible operator with Operator-sdk</li></ul>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<p>Basic familiarity with Kubernetes (i.e. creating/updating/deleting Kubernetes resources such as Pods and Services)</p>
 <p> The labs in this course cover the creation, deployment, and use of operators on a Kubernetes cluster. It assumes you are already familiar with the basics of Kubernetes, such as using kubectl to create resources on a Kubernetes cluster. </p>
  
  <p>The requirements for the hands-on labs are as follows:</p>
<ul>
<li>Requirements for all hands-on labs</li>
<li>Operator-sdk v1.5.0+ installed</li>
<li>Kubectl v1.17.0+ installed</li>
<li>Admin access to a Kubernetes cluster. The next section covers how to provision a small cluster for free on IBM Cloud if you do not have a one already.</li>
<li>Docker v3.2.2+ installed</li>
<li>Access to a Docker image repository such as Docker Hub or quay.io</li></p>
</ul>
<hr />
<h2>COURSE INSTRUCTORS</h2>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Jonathan%20Berkhahn%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><a href="https://www.linkedin.com/in/jonathan-berkhahn-285779103"><strong>Jonathan Berkhahn </strong></a>&nbsp;is an open source software contributor for IBM. He’s worked on various OSS cloud computing projects such as Cloud Foundry and Kubernetes and currently contributes to Operator-sdk and related projects.</p>
  
  <p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Martin%20Hickey.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>
  
  <p><strong>Martin Hickey </strong></a>&nbsp;works on the Open Technology team at IBM focusing on open source software. He is a regular contributor to open source and a core maintainer for Helm. He has also contributed previously to the OpenStack and Elastic communities. Martin enjoys speaking at conferences and meet-ups. He has many years’ experience in the creation of enterprise software for different industries, from Telcos to Cloud.</p>

<p>&nbsp;</p>


<hr />
<h2>FREQUENTLY ASKED QUESTIONS</h2>

<article>
<h3>What web browser should I use?</h3>

<p>The Open edX platform works best with current versions of Chrome, Firefox, or Safari, or with Internet Explorer version 9 and later.</p>

<p>See our&nbsp;<a href="http://edx.readthedocs.org/projects/open-edx-learner-guide/en/latest/front_matter/browsers.html">list of supported browsers</a>&nbsp;for the most current information.</p>
</article>
</section>

Kubernetes Operators Intermediate

This course serves as an introduction to Kubernetes operators. Operators are a design pattern for building and deploying an application onto a Kubernetes cluster such that it can be used in the same manner as core Kubernetes resources.

<div class="trix-content">
  <div>You will learn the basics of writing and testing a microservice that uses bean validation and the new functionality of Bean Validation 2.0. The service uses bean validation to validate that the supplied JavaBeans meet the defined constraints.</div><div>Bean Validation is a Java specification that simplifies data validation and error checking. Bean validation uses a standard way to validate data stored in JavaBeans. Validation can be performed manually or with integration with other specifications and frameworks, such as Contexts and Dependency Injection (CDI), Java Persistence API (JPA), or JavaServer Faces (JSF). To set rules on data, apply constraints by using annotations or XML configuration files. Bean validation provides both built-in constraints and the ability to create custom constraints. Bean validation allows for validation of both JavaBean fields and methods. For method-level validation, both the input parameters and return value can be validated.</div><div>Several additional built-in constraints are included in Bean Validation 2.0, which reduces the need for custom validation in common validation scenarios. Some of the new built-in constraints include @Email, @NotBlank, @Positive, and @Negative. Also in Bean Validation 2.0, you can now specify constraints on type parameters.</div><div>The example microservice uses both field-level and method-level validation as well as several of the built-in constraints and a custom constraint.</div>
</div>


Validating constraints for JavaBeans in Java microservices

Explore the use of bean validation to validate user input data for microservices.

<div class="trix-content">
  <div>The starting point of this guide is the finished application from Spring’s <a href="https://spring.io/guides/gs/spring-boot/">Building an Application with Spring Boot</a> guide. If you are not familiar with Spring Boot, complete that guide first. Java 8 is required to run this project.</div><div>You will learn how to use the springBootUtility command to deploy a Spring Boot application in Docker on an Open Liberty server without modification. This command stores the dependent library JAR files of the application to the target library cache, and packages the remaining application artifacts into a thin application JAR file.</div><div>You will also learn how to run the Spring Boot application locally with an Open Liberty server, and how to package it so that it is embedded with an Open Liberty server.</div>
</div>


Containerizing, packaging, and running a Spring Boot application

Learn how to containerize, package, and run a Spring Boot application on an Open Liberty server without modification.

<h2>About This Course</h2>
<p>Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.</p>

<p>TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.</p>

<p>In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.</p>

<h2>Course Syllabus</h2>

<strong>Module 1 – Introduction to TensorFlow</strong>
<ul>
 	<li>HelloWorld with TensorFlow</li>
 	<li>Linear Regression</li>
 	<li>Nonlinear Regression</li>
 	<li>Logistic Regression</li>
 	<li>Activation Functions</li>
</ul>
<strong>Module 2 – Convolutional Neural Networks (CNN)</strong>
<ul>
 	<li>CNN History</li>
 	<li>Understanding CNNs</li>
 	<li>CNN Application</li>
</ul>
<strong>Module 3 – Recurrent Neural Networks (RNN)</strong>
<ul>
 	<li>Intro to RNN Model</li>
 	<li>Long Short-Term memory (LSTM)</li>
 	<li>Recursive Neural Tensor Network Theory</li>
 	<li>Recurrent Neural Network Model</li>
</ul>
<strong>Module 4 - </strong><strong>Unsupervised Learning</strong>
<ul>
 	<li>Applications of Unsupervised Learning</li>
 	<li>Restricted Boltzmann Machine</li>
 	<li>Collaborative Filtering with RBM</li>
</ul>
<strong>Module 5 - Autoencoders</strong>
<ul>
 	<li>Introduction to Autoencoders and Applications</li>
 	<li>Autoencoders</li>
 	<li>Deep Belief Network</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 	<li>Neural Network</li>
</ul>
<h2>Requirements</h2>
<section class="prerequisites">
<ul>
 	<li>Python programming</li>
</ul>
<h2>Course Staff</h2>
  <section class="course-staff">
<article class="teacher">
<div class="teacher-image"><img class="alignnone wp-image-2254 size-medium" title="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Saeed%20pic.png" alt="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" /></div>
<a href="https://ca.linkedin.com/in/saeedaghabozorgi" target="_blank">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

    </article>
<h3>Course Development Team</h3>
Thanks to course developement team, interns and all individuals contributed to the development of this course: Kiran Mantri, Shashibushan Yenkanchi, Jag Rangrej, Naresh Vempala, Walter Gomes, Anita Vincent, Gabriel Sousa, Francisco Magioli, Victor Costa, Erich Sato, Luis Otavio and Rafael Belo.
</section>

Deep Learning with TensorFlow

Majority of data in the world are unlabeled and unstructured data, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structure in these kind of data, but deep networks are capable of discovering hidden structures within these data. In this course, you will use TensorFlow library to apply deep learning on different data types to solve real world problems.

<div class="trix-content">
  <h1>About</h1><div>Could you imagine using YouTube without the ability to search? Or having to spell the name of a person perfectly to find them on Facebook?<br><br>These websites would be far less useful to us.</div><div>
<br>Great search is an essential feature that all of the best applications share. In fact, it is so common that people have come to expect it on every site and application they use. However, unlike YouTube and Facebook, most of us don't have the time or resources to build a fully-functional search engine from scratch. This doesn't mean our websites and applications can't have great search though!<br><br>With the help of Algolia, we can implement efficient, flexible, and insightful search in our own applications while keeping time and monetary costs to a minimum.<br><br>In this project, you will learn how to build a smart search form from scratch using Algolia. We'll store our searchable data in an index and build a simple NodeJS application that queries it through the Algolia API.<br><br>More specifically, you will be able to:</div><ol>
<li>Setup an index in Algolia using the Algolia UI</li>
<li>Perform basic queries on an Algolia index using the Algolia Javascript Client</li>
<li>Create a form that displays search results in real-time using HTML and Javascript</li>
</ol><div>
<br>The dataset for this project contains a list of people categorized as "Billionaires of 2021" by Forbes in their 35th Annual World's Billionaires List. It has 7 fields: Name, Net worth, Country, Source, Rank, Age, and Industry.<br><br>
</div><h1>Requirements</h1><div>To get the most out of this project, you should know:</div><ul>
<li>Basic HTML and CSS syntax</li>
<li>Basic Javascript syntax</li>
</ul><div>You will also need an Algolia account but don't worry, we'll create one together inside the project (it's free).<br><br>
</div><h1>Author</h1><ul><li>Ben Honda</li></ul>
</div>


Build a Smart Search Form with Algolia

Great search is an essential feature that all of the best applications share. In this project, we'll leverage the power of Algolia to build our own smart search form from scratch.

<div class="trix-content">
  <div><br></div><h1><strong>A Look At the Project Ahead</strong></h1><div>Once you have completed, this project, you’ll be able to:</div><ul>
<li>Clone a GitHub repository through the command line, otherwise known as Terminal</li>
<li>Create and launch a web app with Flask, a micro-framework for Python</li>
<li>Describe the necessary frontend files that help in launching the app, such as the HTML, CSS and Javascript files</li>
</ul><h1>What You'll Need</h1><div>Before starting this project, it'll be helpful to have the following:</div><ul>
<li>Basic Python knowledge</li>
<li>IBM Cloud account</li>
<li>Basic HTML, CSS and Javascript (optional)</li>
<li>GitHub account (optional)</li>
</ul><div>If you're interested in learning more about the behind-the-scenes process of creating a model for image classification, check out the following project: <a href="https://cognitiveclass.ai/courses/course-v1:IBM+GPXX0WTIEN+v1">Train an Image Recognition Model with Python</a>!<br><br>
</div><h1>Instructor</h1><div>Kathy An, IBM<br><br>
</div><h1>Other Contributors</h1><div>Joseph Santarcangelo, IBM<br>Weiqing Wang, IBM</div>
</div>


Launch an AI Hotdog Detector as a Serverless Python App

Have you ever wondered if a picture has a hotdog in it or not? Well, no longer do you have to wonder—you can now prove it! With this fun project, you’ll learn how to create and launch a web app in Python that tells you just that: whether or not a picture has a hotdog in it. Let’s settle the great debate once and for all, learning skills that can help you land your next big job or pursue your million dollar idea while you’re at it!

<div class="trix-content">
  <div>In this hands-on project you will learn about the Monte Carlo method and calculate the ruin probability of an insurance company using Apache Spark by means of parallel Monte Carlo method.<br><br>
</div><h1><strong> Learning Objectives</strong></h1><div><br></div><div>1.  After completing this lab you will be able to:</div><ul>
<li>Understand and apply Monte Carlo method.</li>
<li>Get the basic idea about accuracy of the method</li>
<li>Calculate the "Pi" number by means of the method</li>
<li>Parallelize the method using Apache Spark</li>
</ul><div>2.  Understand the Ruin probability of an insurance company<br>3.  Calculate the Ruin probability using Monte Carlo method.</div>
</div>


Insurance Risk assessment with MonteCarlo method using Apache Spark.

Learn about the Monte Carlo method and calculate the ruin probability of an insurance company using Apache Spark.

<div class="trix-content">
  <div>In this guided project, you will learn how to start MongoDB. You will create your first database and gain hands-on experience listing and creating collections. You will explore basic document management functions and run your own commands in an actual terminal<br><br>
</div><div>
<strong>Why should you learn to use MongoDB?<br></strong><br> This popular non-relational database is easy to access by indexing and supports various data types, including dates and numbers. Use MongoDB to effortlessly query, manipulate, and extract powerful insights from your collected data. Working with schemaless MongoDB is easy because you can focus on the data you are writing and how you are going to read it.</div><div><br></div><div><strong>Learning Objectives</strong></div><ul>
<li>Start MongoDB server on the Skills Network Cloud IDE (Theia).</li>
<li>Connect to MongoDB using a command-line interface.</li>
<li>Find the version of the server.</li>
<li>List databases.</li>
<li>Create databases.</li>
<li>Create collections.</li>
<li>Insert documents into a collection.</li>
<li>List collections.</li>
<li>List all documents in a collection.</li>
<li>Disconnect from MongoDB server</li>
</ul><div>
<strong>Author </strong><br><br>Ramesh Sannareddy</div>
</div>


Create Your First MongoDB Database

In this guided project, you will get started with MongoDB by creating your first database, working with collections, and doing basic document management. 

<section class="about">
<h2>About This Course</h2>
<p>R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science. In this course, you'll be learning about the basics of R, and you'll end with the confidence to start writing your own R scripts.</p>
 <p>But this isn't your typical textbook introduction to R. You're not just learning about R fundamentals, you'll be using R to solve problems related to movies data. Using a concrete example makes the learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R's most important data structures -- vectors!</p>
<p>From vectors, you'll then learn about lists, matrix, arrays and data frames. Then you'll jump into conditional statements, functions, classes and debugging. Once you've covered the basics - you'll learn about reading and writing data in R, whether it's a table format (CSV, Excel) or a text file (.txt). Finally, you'll end with some important functions for character strings and dates in R.</p>

</section>
<h2>Course Syllabus</h2>
<strong>Module 1 - R basics</strong>
<ul>
 	<li>Math, Variables, and Strings</li>
 	<li>Vectors and Factors</li>
 	<li>Vector operations</li>
</ul>
<strong>Module 2 - Data structures in R</strong>
<ul>
 	<li>Arrays &amp; Matrices</li>
 	<li>Lists</li>
 	<li>Dataframes</li>
</ul>
<strong>Module 3 - R programming fundamentals</strong>
<ul>
 	<li>Conditions and loops</li>
 	<li>Functions in R</li>
 	<li>Objects and Classes</li>
 	<li>Debugging</li>
</ul>
<strong>Module 4 - Working with data in R</strong>
<ul>
 	<li>Reading CSV and Excel Files</li>
 	<li>Reading text files</li>
 	<li>Writing and saving data objects to file in R</li>
</ul>
<strong>Module 5 - Strings and Dates in R</strong>
<ul>
 	<li>String operations in R</li>
 	<li>Regular Expressions</li>
 	<li>Dates in R</li>
</ul>
<h2>General Information</h2>
<ul>
 	<li>This course is free.</li>
 	<li>It is self-paced.</li>
 	<li>It can be taken at any time.</li>
 	<li>It can be audited as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 	<li>None</li>
</ul>
<h2>Requirements</h2>
<section class="prerequisites">
<ul>
 	<li>None</li>
</ul>
<h2>Course Staff</h2>
  <section class="course-staff">
<article class="teacher">
<div class="teacher-image"><img class="alignnone wp-image-2254 size-medium" src="/static/saeed-data-visualization-with-r.jpg" alt="Dr. Saeed Aghabozorgi, Data Science Bootcamp instructor" /></div>
<h3>Saeed Aghabozorgi</h3>
<a href="https://ca.linkedin.com/in/saeedaghabozorgi" target="_blank">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

</article><article class="teacher">
<div class="teacher-image"><img class="alignnone wp-image-2253 size-medium" src="/static/Polong-Lin-300x300.jpg" alt="Polong Lin, Data Science Bootcamp instructor" /></div>
<h3>Polong Lin</h3>
<a href="https://ca.linkedin.com/in/polonglin" target="_blank">Polong Lin</a> is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU. Polong is a regular speaker in conferences and meetups, and holds a M.Sc. in Cognitive Psychology.

</article></section>
<h3>BDU Course Development Team</h3>
Thanks to BDU course developement team, BDU interns and all individuals contributed to the development of this course: <a href="https://ca.linkedin.com/in/helly-patel-90344750">Helly Patel </a>, <a href="https://ca.linkedin.com/in/mandeep-kaur-72900394"> Mandeep Kaur </a>, <a href="www.linkedin.com/in/hitenpatel161289">Hiten Patel </a>, <a href="https://ca.linkedin.com/in/marta-aghili-2b184b71">Marta Aghili </a>, <a href="https://www.linkedin.com/in/anita-vincent-a2369b126/en">Anita Vincent </a>, <a href="https://ca.linkedin.com/in/iqbalsingh15">Iqbal Singh </a>, <a href="https://www.linkedin.com/in/rishabhjain66">Rishabh jain </a>, <a href="https://ca.linkedin.com/in/aditya-walia-14b678bb">Aditya Walia </a>, <a href="https://ca.linkedin.com/in/kumar-gaurav-a387a620">Kumar Gaurav </a>
</section>

R for Data Science

Are you ready to dive head-first into R? In just a few hours you'll learn how to write your own R code, learn about data structures and create your own functions. You’ll even be able to import data and do some operations. Try our hands-on exercises as we guide your first steps into your data science journey with R.

<div class="trix-content">
  <div>First, you’ll learn how to create a reactive JAX-RS client application by using the default reactive JAX-RS client APIs. You will then learn how to take advantage of the RxJava reactive extensions with a pluggable reactive JAX-RS client provider that’s published by <a href="https://eclipse-ee4j.github.io/jersey">Eclipse Jersey</a>. The JAX-RS client is an API used to communicate with RESTful web services. The API makes it easy to consume a web service by using the HTTP protocol, which means that you can efficiently implement client-side applications. The reactive client extension to JAX-RS is an API that enables you to use the reactive programming model when using the JAX-RS client.</div><div>Reactive programming is an extension of asynchronous programming and focuses on the flow of data through data streams. Reactive applications process data when it becomes available and respond to requests as soon as processing is complete. The request to the application and response from the application are decoupled so that the application is not blocked from responding to other requests in the meantime. Because reactive applications can run faster than synchronous applications, they provide a much smoother user experience.</div><div>The application in this guide demonstrates how the JAX-RS client accesses remote RESTful services by using asynchronous method calls. You’ll first look at the supplied client application that uses the JAX-RS default CompletionStage-based provider. Then, you’ll modify the client application to use Jersey’s RxJava provider, which is an alternative JAX-RS reactive provider. Both Jersey and Apache CXF provide third-party reactive libraries for RxJava and were tested for use in Open Liberty.</div><div>The application that you will be working with consists of three microservices, system, inventory, and query. Every 15 seconds, the system microservice calculates and publishes an event that contains its current average system load. The inventory microservice subscribes to that information so that it can keep an updated list of all the systems and their current system loads.<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/reactive-rest-client/QueryService.png" width="253" height="327"><figure class="attachment attachment--preview">
  <img width="253" height="327" src="https://openliberty.io/img/guide/reactive-rest-client/QueryService.png">
</figure></action-text-attachment>
</div><div>The microservice that you will modify is the query service. It communicates with the inventory service to determine which system has the highest system load and which system has the lowest system load.</div><div>The system and inventory microservices use MicroProfile Reactive Messaging to send and receive the system load events. If you want to learn more about reactive messaging, see the <a href="https://openliberty.io/guides/microprofile-reactive-messaging.html">Creating reactive Java microservices</a> guide.</div>
</div>


Consuming RESTful services using the reactive JAX-RS client

Learn how to use a reactive JAX-RS client to asynchronously invoke RESTful microservices over HTTP.

<div class="trix-content">
  <div>In this guided project you will learn how to build a mathematical model of an insurance company using Risk Process and to do risk and revenue assessment<br><br>
</div><h1>Learning Objectives</h1><div>After completing this guided project you will be able to:</div><ul>
<li>Build the model of an Insurance Company with Risk Process.<ul><li>Understand Classical Risk Process</li></ul>
</li>
<li>Understand basic actuarial approximations and their limitations <ul><li>Use Actuarial approximations for Risk Assessment of the Classical Risk Process</li></ul>
</li>
<li>Calculate the Ruin Probability of the Classical Risk Process using Monte Carlo method and compare it to theoretical results</li>
</ul>
</div>


Insurance Business Modelling and Basic Actuarial Calculations

In this guided project you will learn how to build a mathematical model of an insurance company using Risk Process and to do risk and revenue assessment.

<div class="trix-content">
  <h1><strong>About </strong></h1><div>In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks. The lab consists of three stages:</div><ul>
<li>Download and preliminary analysis of data</li>
<li>Forecasting</li>
<li>Artificial Neural Networks</li>
</ul><div>The first stage will show you how to download data and pre-prepare it for the analysis:</div><ul>
<li>downloading data</li>
<li>changing the data types of columns</li>
<li>grouping data</li>
<li>DataSet transformation</li>
</ul><div><br></div><div>At the stage of forecasting, we will deal with the methods of building and fitting models, as well as with the automation of statistical information calculation, in particular:</div><ul>
<li>hypothesis creation</li>
<li>splitting the DataSet into training and test sets</li>
<li>creating a linear model using sklearn</li>
<li>calculation of basic statistical indicators</li>
<li>creating a linear model using statsmodels</li>
</ul><div><br></div><div>At the stage of Artificial Neural Networks, we will deal with the methods of building and fitting models based on Artificial Intelligence:</div><ul>
<li>creating a linear model using Scikit-learn</li>
<li>creating a linear model using keras</li>
</ul><div>The statistical data was obtained from the <a href="https://ec.europa.eu/eurostat/databrowser/view/aact_eaa01/default/table?lang=en">https://ec.europa.eu/eurostat/databrowser/view/aact_eaa01/default/table?lang=en</a>. Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes.<strong> <br></strong><br>
</div><h1><strong>Prerequisites</strong></h1><ul>
<li>Python - basic level</li>
<li>Pandas - basic level</li>
<li>SeaBorn - basic level</li>
<li>Statistics - basic level</li>
<li>Scikit-learn - basic level</li>
<li>keras - basic level</li>
</ul><div> </div><h1><strong>After completing this lab, you will be able to:</strong></h1><ul>
<li>Download a DataSet from *.csv files</li>
<li>Automatically change data in the DataSet</li>
<li>Transform a table</li>
<li>Visualize data with pandas and seaborn</li>
<li>Make Linear forecast models</li>
<li>Build and fit Neural Networks</li>
</ul><div><br></div><div> <br><br>
</div>
</div>


Data Science in Agriculture. Prognostication using by Neural Network

In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks. 

<div class="trix-content">
  <h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment.  Hands-on skills are highly sought out by employers when determining job readiness.   Guided projects are </div><div>interactive, on-demand and will equip you with practical abilities that can be applied on the job!</div><div><br></div><h1>A Look at the Project Ahead</h1><div>Have you ever wondered what techniques are used to make photo filters like the ones present in applications such as Adobe Photoshop? Well the answer is computer vision and image processing techniques! Or wondered how your photographs would look like as art work? <br><br>In this project you will learn how to use built-in OpenCV functions to transform your photographs into artistic paintings and sketches.  OpenCV  is a powerful and popular open source computer vision and image processing library that contains an extensive set of algorithms. We will be utilizing this library to transform your photographs into artwork!</div><div><br></div><div>Express your creativity with technology using OpenCV in python, all while learning about some common and basic image processing and computer vision techniques!</div><div><br></div><div>More specifically after this lab you will be able to:</div><ul>
<li>Convert colored images to grayscale</li>
<li>Apply the Gaussian filter to smooth an image</li>
<li>Detect the edges of an image using the Gradient Magnitude</li>
<li>Convert a grayscale image to a binary image</li>
<li>Create a pencil sketch of an image in OpenCV</li>
<li>Create a water painting of an image in OpenCV</li>
</ul><h1>What You’ll Need </h1><div>To do this project, you will need the following:</div><ul><li>A very basic knowledge of Python</li></ul><h1>Your Instructor</h1><div>Yasmine Hemmati, IBM</div>
</div>


Transform Photos to Sketches and Paintings with OpenCV

Have you ever wanted to transform your photographs into an artistic sketch or painting to showcase your creativity?  In this Guided Project you'll do just that! Transform your photographs to paintings, sketches and more using OpenCV in python.

<div class="trix-content">
  <div>This lab is devoted to predicting the COVID-19 spread dynamics in the world using neural networks of different structures. We will learn how to make predictions based on linear regression, back propagation and long short-term memory neural networks .<br><br>
</div><h1>Learning Objectives</h1><ul>
<li>Download a DataSet from *.csv files</li>
<li>Automatically change data in the DataSet</li>
<li>Transform the table</li>
<li>Visualize data with pandas</li>
<li>Make foreacast models based on Neural Networks </li>
</ul>
</div>


Data Science in Health Care. Advanced prognostication using Neural Networks

Learn to predict the COVID-19 spread dynamics in the world using neural networks of different structures. 

<div class="trix-content">
  <div>You will learn how to stream messages from a MicroProfile Reactive Messaging service to a front-end client by using Server-Sent Events (SSE).</div><div>MicroProfile Reactive Messaging provides an easy way for Java services to send requests to other Java services, and asynchronously receive and process the responses as a stream of events. SSE provides a framework to stream the data in these events to a browser client.</div><div><br></div><h1>What is SSE?</h1><div>Server-Sent Events is an API that allows clients to subscribe to a stream of events that is pushed from a server. First, the client makes a connection with the server over HTTP. The server continuously pushes events to the client as long as the connection persists. SSE differs from traditional HTTP requests, which use one request for one response. SSE also differs from Web Sockets in that SSE is unidirectional from the server to the client, and Web Sockets allow for bidirectional communication.</div><div>For example, an application that provides real-time stock quotes might use SSE to push price updates from the server to the browser as soon as the server receives them. Such an application wouldn’t need Web Sockets because the data travels in only one direction, and polling the server by using HTTP requests wouldn’t provide real-time updates.</div><div>The application that you will build in this guide consists of a frontend service, a bff (backend for frontend) service, and three instances of a system service. The system services periodically publish messages that contain their hostname and current system load. The bff service receives the messages from the system services and pushes the contents as SSE to a JavaScript client in the frontend service. This client uses the events to update a table in the UI that displays each system’s hostname and its periodically updating load. The following diagram depicts the application that is used in this guide:<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/reactive-messaging-sse/SSE_Diagram.png" width="980" height="1380"><figure class="attachment attachment--preview">
  <img width="980" height="1380" src="https://openliberty.io/img/guide/reactive-messaging-sse/SSE_Diagram.png">
</figure></action-text-attachment>
</div><div>In this guide, you will set up the bff service by creating an endpoint that clients can use to subscribe to events. You will also enable the service to read from the reactive messaging channel and push the contents to subscribers via SSE. After that, you will configure the Kafka connectors to allow the bff service to receive messages from the system services. Finally, you will configure the client in the frontend service to subscribe to these events, consume them, and display them in the UI.</div><div>To learn more about the reactive Java services that are used in this guide, check out the <a href="https://openliberty.io/guides/microprofile-reactive-messaging.html">Creating reactive Java microservices</a> guide.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Streaming updates from a MicroProfile Reactive Messaging microservice using Server-Sent Events (SSE)

Learn how to stream updates from a MicroProfile Reactive Messaging service to a front-end client by using Server-Sent Events (SSE).

<div class="trix-content">
  <h1>About</h1><div>This lab is dedicated to downloading, pre-preparing and making statistical analysis of Economic accounts for agriculture and to creating interactive maps showing the dynamics of the prices.<br><br>
</div><div>In this lab, we will learn how to download data, perform basic statistical analysis, visualize and display it on dynamic maps. All data is obtained from Eurostat DataBase (<a href="https://ec.europa.eu/eurostat/data/database)">https://ec.europa.eu/eurostat/data/database)</a>. This lab consist of the following steps:<br><br>
</div><ul>
<li>Data download - download and display data from a file</li>
<li>Data preparation - preliminary analysis of data structure, change of data types and table structure</li>
<li>Statistical analysis - basic statistical analysis</li>
<li>Data visualization - output of various data on graphs</li>
<li>Build a trend line - the basic method of building a trend line and a forecast based on it</li>
<li>Interactive maps - construction of interactive maps which show the data change over a period of time.</li>
</ul><div><br></div><h1><strong>After completing this lab, you will be able to:</strong></h1><ul>
<li>Download a DataSet from *.csv files</li>
<li>Transform the table</li>
<li>Visualize data with pandas and seaborn</li>
<li>Expect<ul>
<li>Minimum and maximum value</li>
<li>Average</li>
<li>Quarters</li>
<li>Pivot tables</li>
<li>Build Interactive Maps</li>
</ul>
</li>
</ul><div><br></div>
</div>


Data Science in Agriculture. Basic statistical analysis and Geo Visualisation

In this lab, we will learn how to download data, perform basic statistical analysis, visualize and display it on dynamic maps.

Scala 基础入门(中文)

<div class="trix-content">
  <div>You will learn how and why to externalize your microservice’s configuration. Externalized configuration is useful because configuration usually changes depending on your environment. You will also learn how to configure the environment by providing required values to your application using Kubernetes. Using environment variables allows for easier deployment to different environments.<br><br>
</div><div>MicroProfile Config provides useful annotations that you can use to inject configured values into your code. These values can come from any configuration source, such as environment variables. To learn more about MicroProfile Config, read the <a href="https://openliberty.io/guides/microprofile-config.html">Configuring microservices</a> guide.<br><br>
</div><div>Furthermore, you’ll learn how to set these environment variables with ConfigMaps and Secrets. These resources are provided by Kubernetes and act as a data source for your environment variables. You can use a ConfigMap or Secret to set environment variables for any number of containers.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Configuring microservices running in Kubernetes

Explore how to externalize configuration using MicroProfile Config and configure your microservices using Kubernetes ConfigMaps and Secrets.

<div class="trix-content">
  <div>You will learn how to build reactive microservices that can send requests to other microservices, and asynchronously receive and process the responses. You will use an external messaging system to handle the asynchronous messages that are sent and received between the microservices as streams of events. MicroProfile Reactive Messaging makes it easy to write and configure your application to send, receive, and process the events efficiently.<br><br>
</div><div><strong>Asynchronous messaging between microservices</strong></div><div>Asynchronous communication between microservices can be used to build reactive and responsive applications. By decoupling the requests sent by a microservice from the responses that it receives, the microservice is not blocked from performing other tasks while waiting for the requested data to become available. Imagine asynchronous communication as a restaurant. A waiter might come to your table and take your order. While you are waiting for your food to be prepared, that waiter serves other tables and takes their orders too. When your food is ready, the waiter brings your food to the table and then continues to serve the other tables. If the waiter were to operate synchronously, they must take your order and then wait until they deliver your food before serving any other tables. In microservices, a request call from a REST client to another microservice can be time-consuming because the network might be slow, or the other service might be overwhelmed with requests and can’t respond quickly. But in an asynchronous system, the microservice sends a request to another microservice and continues to send other calls and to receive and process other responses until it receives a response to the original request.<br><br>
</div><div><strong>What is MicroProfile Reactive Messaging?</strong></div><div>MicroProfile Reactive Messaging provides an easy way to asynchronously send, receive, and process messages that are received as continuous streams of events. You simply annotate application beans' methods and Open Liberty converts the annotated methods to reactive streams-compatible publishers, subscribers, and processors and connects them up to each other. MicroProfile Reactive Messaging provides a Connector API so that your methods can be connected to external messaging systems that produce and consume the streams of events, such as <a href="https://kafka.apache.org/">Apache Kafka</a>.</div><div>The application in this guide consists of two microservices, system and inventory. Every 15 seconds, the system microservice calculates and publishes an event that contains its current average system load. The inventory microservice subscribes to that information so that it can keep an updated list of all the systems and their current system loads. The current inventory of systems can be accessed via the /systems REST endpoint. You’ll create the system and inventory microservices using MicroProfile Reactive Messaging.<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/microprofile-reactive-messaging/reactive-messaging-system-inventory.png" width="1160" height="1234"><figure class="attachment attachment--preview">
  <img width="1160" height="1234" src="https://openliberty.io/img/guide/microprofile-reactive-messaging/reactive-messaging-system-inventory.png">
</figure></action-text-attachment>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Creating asynchronous Java microservices using MicroProfile Reactive Messaging

Learn how to write reactive Java microservices using MicroProfile Reactive Messaging.

<h2>LEARN TO ANALYZE DATA WITH PYTHON</h2>

<p>Learn how to analyze data using Python. This course&nbsp;will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!</p>

<p>You will learn how to:</p>

<ul>
	<li>Import data sets</li>
	<li>Clean and prepare data for analysis</li>
	<li>Manipulate pandas DataFrame</li>
	<li>Summarize data</li>
	<li>Build machine learning models using scikit-learn</li>
	<li>Build data pipelines</li>
</ul>

<p>Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:</p>

<ul>
	<li>Data Analysis libraries:&nbsp;will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets.&nbsp;We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<h3>Module 1 - Importing Datasets</h3>

<ul>
	<li>Learning Objectives</li>
	<li>Understanding the Domain</li>
	<li>Understanding the Dataset</li>
	<li>Python package for data science</li>
	<li>Importing and Exporting Data in Python</li>
	<li>Basic Insights from Datasets</li>
</ul>

<p><strong>Module 2 - Cleaning and Preparing the Data</strong></p>

<ul>
	<li>Identify and Handle Missing Values</li>
	<li>Data Formatting</li>
	<li>Data Normalization Sets</li>
	<li>Binning</li>
	<li>Indicator variables</li>
</ul>

<h3>Module 3 - Summarizing the Data Frame</h3>

<ul>
	<li>Descriptive Statistics</li>
	<li>Basic of Grouping</li>
	<li>ANOVA</li>
	<li>Correlation</li>
	<li>More on Correlation</li>
</ul>

<h3>Module 4 - Model Development</h3>

<ul>
	<li>Simple and Multiple Linear Regression</li>
	<li>Model Evaluation&nbsp;Using&nbsp;Visualization</li>
	<li>Polynomial Regression and Pipelines</li>
	<li>R-squared and MSE for In-Sample Evaluation</li>
	<li>Prediction and Decision Making</li>
</ul>

<h3>Module 5 - Model Evaluation</h3>

<ul>
	<li>Model &nbsp;Evaluation</li>
	<li>Over-fitting, Under-fitting and Model Selection</li>
	<li>Ridge Regression</li>
	<li>Grid Search</li>
	<li>Model Refinement</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>This course is free.</li>
	<li>It is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited&nbsp;as many times as you wish.</li>
	<li>Python programming, Statistics</li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>Some Python experience is expected</li>
	<li>Python for Data Science</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Joseph%20Santarcangelo%20pic.png" style="width: 150px; height: 147px; border-radius: 10px" /></p>

<h3>Joseph Santarcangelo Ph.D.</h3>

<p><a href="https://www.linkedin.com/in/joseph-s-50398b136/">Joseph</a>&nbsp;has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.</p>

<p>&nbsp;</p>
</article>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Mahdi%20Noorian%20pic.png" style="width: 150px; height: 147px; border-radius: 10px" /></p>

<h3>Mahdi Noorian Ph.D.</h3>

<p><a href="https://www.linkedin.com/in/mahdi-noorian-58219234/">Mahdi Noorian</a>&nbsp;is a Postdoctoral Fellow at the<a href="http://ls3.rnet.ryerson.ca/?page_id=13">&nbsp;Laboratory for Systems, Software and Semantics (LS3)</a>&nbsp;of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick. As a Data Scientist, he is interested in application of machine learning, data mining, optimization, and semantic data analysis for big data to solve the real-world problems.</p>

<p>&nbsp;</p>
</article>
</section>

<h3>Other Contributors</h3>

<p>The following individuals also contributed to this course:&nbsp;<a href="https://www.linkedin.com/in/baharetalayian/">Bahare Talayian</a>,&nbsp;<a href="https://www.linkedin.com/in/fiorellawever/" rel="noopener" target="_blank">Fiorella Wenver,&nbsp;</a><a href="https://www.linkedin.com/in/ke-xing-1987a3b0/">Ke Xing</a>&nbsp;,&nbsp;<a href="https://www.linkedin.com/in/xiao-han-steven-dong-38128b4b/">Steven Dong</a>&nbsp;and&nbsp;<a href="https://www.linkedin.com/in/himavasudevan/">Hima Vasudevan</a></p>

<p>&nbsp;</p>

Data Analysis with Python

In this course you will learn about:  Data Acquisition How to Obtain Basic Insight From a Dataset Data 

<section class="about">
  <h2>About This Course</h2>
  <p>This mini course walks you through how to scaffold a web application, run it locally in a container, and then deploy it to a Kubernetes cluster created with Kubernetes Service. Additionally, you will learn how to bind a custom domain, monitor the health of the environment, and scale the application.</p>

<p>Containers are a standard way to package apps and all their dependencies so that you can seamlessly move the apps between environments. Unlike virtual machines, containers do not bundle the operating system. Only the app code, run time, system tools, libraries, and settings are packaged inside containers. Containers are more lightweight, portable, and efficient than virtual machines.</p>

<p>For developers looking to kickstart their projects, the IBM Cloud Developer Tools CLI enables rapid application development and deployment by generating template applications that you can run immediately or customize as the starter for your own solutions. In addition to generating starter application code, Docker container image and CloudFoundry assets, the code generators used by the dev CLI and web console generate files to aid deployment into Kubernetes environments. The templates generate Helm charts that describe the application’s initial Kubernetes deployment configuration, and are easily extended to create multi-image or complex deployments as needed.</p>


</section>



Scalable web applications on Kubernetes

This mini course walks you through how to scaffold a web application, run it locally in a container, and then deploy it to a Kubernetes cluster created with Kubernetes Service. Additionally, you will learn how to bind a custom domain, monitor the health of the environment, and scale the application.

<section class="about">
    <h2>About This Course</h2>
    <p>
        When we use the term Reactive, we are usually talking about Reactive Microservices. But what is a microservice? And how can we make it Reactive? This course will explore the difference between monoliths and microservices and show the journey to making a system Reactive.
    </p>
</section>
<h2>What will I get after passing this course?</h2>
<ul>
    <li>You will receive a completion certificate.</li>
</ul>
<h2>Course Syllabus</h2>
<ul>
    <li>The Software Spectrum</li>
    <ul>
        <li>An introduction the Monolith to Microservices sprectrum</li>
        <li>The bascis of choosing between a Monolith or Microservice software approach</li>
    </ul>
    <li>Monoliths</li>
    <ul>
        <li>Some Monoliths can be Big Balls of Mud</li>
        <li>Characteristics of Monoliths</li>
        <li>Advantages and Disadvantages of Monoliths</li>
    </ul>
    <li>Microservices</li>
    <ul>
        <li>Service Oriented Architecture</li>
        <li>Characteristics of Microservices</li>
        <li>Responsibilities of Microservices</li>
        <li>Advantages and Disadvantages of Microservices</li>
        <li>Decomposing a Monolith into Microservices</li>
  </ul>
    <li>Principles of Isolation</li>
    <ul>
        <li>State, Space, Time and Failure</li>
        <li>Monoliths and the Principles of Isolation</li>
        <li>Microservices and the Principles of Isolation</li>
    </ul>
    <li>Isolation Techniques</li>
    <ul>
        <li>Bulkheading</li>
        <li>Circuit Breakers</li>
        <li>Message Driven Architecture</li>
        <li>Autonomy</li>
        <li>Gateway Services</li>
  </ul>
</ul>
<h2>General Information</h2>
<ul>
    <li>This course is free.</li>
    <li>It is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be taken as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Experience in the design of software systems.</li>
</ul>
<h2>Grading scheme</h2>
<ul>
    <li>The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark.</li>
    <li>You have 1 attempt to take the final exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
    <h2>Requirements</h2>
    <ul>
          
        <li>You should either understand all the concepts or have taken Reactive Architecture: Introduction to Reactive Systems and Reactive Architecture: Domain Driven Design
        </li>
    </ul>
</section>

<section class="course-staff">
    <h2>Course Staff</h2>
    <article class="teacher">
        <div class="teacher-image">
            <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Wade%20Waldron%20pic.png" align="left" style="margin:0 20 px 0" alt="Wade Waldron">
        </div>

        <h3>Wade Waldron</h3>
        <p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p>
        <p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>
        <p>In 2016, Wade released a book "Applied Akka Patterns" along with his co-author Michael Nash</p>
        <p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

        <div><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow @wdwaldron</a>
            <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
        </div>

        <div>
            <script src="//platform.linkedin.com/in.js" type="text/javascript"></script>
            <script type="IN/MemberProfile" data-id="https://www.linkedin.com/in/wade-waldron" data-format="click" data-related="false" data-text="Wade Waldron"></script>
        </div>
    </article>
</section>

Reactive Architecture: Reactive Microservices

When we use the term Reactive, we are usually talking about Reactive Microservices. But what is a microservice? And how can we make it Reactive? This course will explore the difference between monoliths and microservices and show the journey to making a system Reactive.

<section class="about">
    <h2>About This Course</h2>
    <p>
      Reactive Systems are built on a foundation of asynchronous, non-blocking messages. This course will explore that idea in detail. We will discuss replacing distributed transactions with the Saga pattern. We will explore the consequences of building a distributed system on the delivery guarantees we can provide for our messages. Finally we will look at different techniques for delivering the messages like Point to Point or Publish/Subscribe, and how we can leverage external technologies such as a message bus to help.
    </p>
</section>
<h2>What will I get after passing this course?</h2>
<ul>
    <li>You will receive a completion certificate.</li>
</ul>
<h2>Course Syllabus</h2>
<ul>
    <li>Message Driven Architecture</li>
    <ul>
      <li>The role of messages in a distributed system.</li>
      <li>A comparison of synchronous, and asynchronous messages</li>
    </ul>
    <li>Sagas</li>
    <ul>
      <li>Introducing the Saga pattern as a way to deal with transactional problems in a distributed system.</li>
    </ul>
    <li>Messaging Patterns</li>
    <ul>
        <li>A discussion of The Two Generals Problem</li>
        <li>Delivery Guarantees in a distributed system</li>
        <li>Point to Point messaging</li>
        <li>Publish Subscribe messaging</li>
  </ul>
</ul>
<h2>General Information</h2>
<ul>
    <li>It is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be taken as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Experience in the design of software systems.</li>
</ul>
<h2>Grading scheme</h2>
<ul>
    <li>The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark.</li>
    <li>You have 1 attempt to take the final exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
    <h2>Requirements</h2>
<ul>

<li><li>Reactive Architecture: Introduction to Reactive Systems, Reactive Architecture: Domain Driven Design and Reactive Architecture: Reactive Microservices, Reactive Architecture: Building Scalable Systems or an understanding of the concepts covered in these courses.</li>
</ul>
</section>

<section class="course-staff">
    <h2>Course Staff</h2>
    <article class="teacher">
        <div class="teacher-image">
            <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Wade%20Waldron%20pic.png" align="left" style="margin:0 20 px 0" alt="Wade Waldron">
        </div>

        <h3>Wade Waldron</h3>
        <p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p>
        <p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>
        <p>In 2016, Wade released a book "Applied Akka Patterns" along with his co-author Michael Nash</p>
        <p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

        <div><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow @wdwaldron</a>
            <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
        </div>

        <div>
            <script src="//platform.linkedin.com/in.js" type="text/javascript"></script>
            <script type="IN/MemberProfile" data-id="https://www.linkedin.com/in/wade-waldron" data-format="click" data-related="false" data-text="Wade Waldron"></script>
        </div>
    </article>
</section>

Reactive Architecture: Distributed Messaging Platforms

Reactive Systems are built on a foundation of asynchronous, non-blocking messages. This course will explore that idea in detail.

<section class="about"> <h2>About This Course</h2> Master <strong>Apache Spark</strong>, a popular cluster computing framework used for performing large scale data analysis. <strong>SparkR</strong> provides a distributed data frame API that enables structured data processing with a syntax familiar to <strong>R</strong> users. <ul> <li>Learn why R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks.</li> <li>Learn how SparkR, an R package that provides a light-weight frontend, uses Apache Spark from R.</li> </ul> </section> <section> <h2>Course Syllabus</h2> <ul> <li><strong>Module 1 - Introduction to SparkR</strong> <ol> <li>Learn what SparkR is</li> <li>Understand why you would use SparkR</li> <li>List the features of SparkR</li> <li>Understand the interfaces into SparkR</li> </ol> </li> </ul> <ul> <li><strong>Module 2 - Data manipulation in SparkR</strong> <ol> <li>Understand how to use dataframes</li> <li>Learn to select data</li> <li>Learn to filter data</li> <li>Learn to aggregate data</li> <li>Learn to operate on columns</li> <li>Understand how to write SQL queries</li> </ol> </li> </ul> <ul> <li><strong>Lab 1 - Getting started with SparkR</strong></li> </ul> <ul> <li><strong>Lab 2 - Data manipulation in SparkR</strong></li> </ul> <ul> <li><strong>Module 3 - Machine learning in SparkR</strong> <ol> <li>Understand machine learning</li> <li>Learn how to use GLM model</li> </ol> </li> </ul> <ul> <li><strong>Lab 3 - Linear models in SparkR</strong></li> </ul> <section class="prerequisites"> <h2>Recommended skills prior to taking this course</h2> <ul> <li>None</li> </ul> <h2>Requirements</h2> <ul> <li>None</li> </ul> </section> <section class="course-staff"> <h2>Course Staff</h2> <article class="teacher"> <div class="teacher-image"> <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alan%20Barnes%20pic.png" align="left" style="margin:0 20 px 0" alt="Alan Barnes"> </div> <h3>Alan Barnes</h3> <p>Alan Barnes is a Senior IBM Information Management Course Developer / Consultant. He has worked in several companies as a Senior Technical Consultant, Database Team Manager, Application Programmer, Systems Programmer, Business Analyst, DB2 Team Lead and more. His career in IT spans more than 35 years.</p> </article> </section>

Analyzing Big Data in R using Apache Spark

Apache Spark is a popular cluster computing framework used for performing large scale data analysis. SparkR provides a distributed data frame API that enables structured data processing with a syntax familiar to R users.

<section class="about">
  <h2>About This Course</h2>
<p>
Data is one of the most critical assets of any business. Data needs a database to store and process data quickly. SQL is a language used for a database to query data.
  </p>
  <p>
In this introductory course, you'll learn the basics of the SQL language and the relational databases. You'll start by learning about the relational model and relational model concepts and constraints. By the end of this course, you will have learned and used the five basic SQL statements, some advanced SQL syntax, and join statements.
  </p>
  <p>
    This isn't your typical textbook introduction. You're not just learning through lectures. At the end of each module there are assignments, hands-on exercises, review questions, and also a final exam. Successfully completing this course earns you a certificate. So let's get started!
  </p>
</section>
  <h2>Course Syllabus</h2>
  <ul>
    <li>Module 1 -SQL and Relational Databases 101</li>
<ul>
  <li>Introduction to SQL and Relational Databases</li>
  <li>Information and Data Models</li>
  <li>Types of Relationships</li>
  <li>Mapping Entities to Tables</li>
  <li>Relational Model Concepts</li>
    </ul>
  <li>
    Module 2 - Relational Model Constraints and Data Objects</li>
    <ul>
      <li>Relational Model Constraints Introduction</li>
  <li>Relational Model Constraints Advanced</li>
    </ul>
    <li>
      Module 3 - Data Definition Language (DDL) and Data Manipulation Language (DML)
    </li>
    <ul>
      <li>CREATE TABLE statement</li>
      <li>INSERT statement</li>
      <li>SELECT statement</li>
      <li>UPDATE and DELETE statements</li>
    </ul>
    <li>Module 4 - Advanced SQL</li>
    <ul>
      <li>String Patterns, Ranges, and Sets</li>
      <li>Sorting Result Sets</li>
      <li>Grouping Result Sets</li>
    </ul>
    <li>Module 5 - Working with multiple tables</li>
    <ul>
      <li>Join Overview</li>
      <li>Inner Join</li>
      <li>Outer Join</li>
    </ul>
</ul> 
	<h2>Recommended skills prior to taking this course</h2>
  <ul>
    <li>None</li>
  </ul>
<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None.</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Rav.png" align="left" style="margin:0 20 px 0" alt="Rav Ahuja">
    </div>

    <h3>Rav Ahuja</h3>
    <p>Rav Ahuja is a Senior Manager with IBM Canada Lab specializing in AI, Data Science and Big Data analytics. He is part of the Emerging Technologies team and is involved in incubating solutions for Data Scientists and Analytics Professionals. Rav is also a co-founder of Cognitive Class (CC) and leads the work on CC partnerships and offerings for businesses. He presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from Richard Ivey School of Business, University of Western Ontario.</p>
  </article>
</section>

SQL and Relational Databases 101

Are you ready to dive into the world of SQL and relational databases? In just a few hours, you'll be able to discuss SQL basics and explain various aspects of the relational database model. Try our hands-on exercises as we guide your first steps into SQL and relational databases.

<section class="about">
    <h2>About This Course</h2>
    <p>
      Command Query Responsibility Segregation (CQRS), and Event Sourcing (ES), are powerful tools. They can make the difference between an application that evolves, and remains competitive, or one that stagnates and falls into obscurity. This course will discuss the reasons we use CQRS/ES, what benefits it provides us, but also what it will cost us to use. We will see how CQRS/ES can impact the scalability, consistency, and availability of our application. Most importantly we will learn about one of the key techniques that is used in building modern Reactive Microservices.
    </p>
</section>
<h2>What will I get after passing this course?</h2>
<ul>
    <li>You will receive a completion certificate.</li>
</ul>
<h2>Course Syllabus</h2>
<ul>
  <li>What is CQRS/ES?</li>
    <ul>
      <li>A brief introduction to CQRS/ES, and what situations it should be applied in.</li>
    </ul>
    <li>Event Sourcing</li>
    <ul>
      <li>An introduction to traditional, state-based persistence techniques, and where they break down.</li>
      <li>A discussion of Event Sourcing, and how it can overcome the limitations of state-based persistence.</li>
      <li>Details around how Event Sourced models can be evolved.</li>
      <li>A short introduction to Command Sourcing, and how it contrasts with Event Sourcing.</li>
    </ul>
    <li>CQRS</li>
    <ul>
        <li>A discussion of the conflict that arises from having a unified model for reads and writes.</li>
        <li>Details of how CQRS can be used to solve the model conflict.</li>
  </ul>
    <li>Consistency, Availability, and Scalability with CQRS</li>
    <ul>
        <li>How CQRS can be used to create fine grained microservices.</li>
        <li>The impact of CQRS on the Consistency, Availability, and Scalability of the system.</li>
      <li>A discussion of the costs of CQRS/ES, both real, and misunderstood.</li>
  </ul>
</ul>
<h2>General Information</h2>
<ul>
    <li>It is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be taken as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Experience in the design of software systems.</li>
</ul>
<h2>Grading scheme</h2>
<ul>
    <li>The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark.</li>
    <li>You have 1 attempt to take the final exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
    <h2>Requirements</h2>
<ul>
<li>
Reactive Architecture: Introduction to Reactive Systems, Reactive Architecture: Domain Driven Design and Reactive Architecture: Reactive Microservices, Reactive Architecture: Building Scalable Systems and Reactive Architecture: Distributed Messaging Patterns</li>

</ul>
</section> <section> <hr /> <h2>COURSE STAFF</h2> <article> <p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Wade%20Waldron%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p> <h3>Wade Waldron</h3> <p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p> <p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>
        <p>In 2016, Wade released a book "Applied Akka Patterns" along with his co-author Michael Nash</p>
        <p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

        <div><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow @wdwaldron</a>
            <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
        </div>

        <div>
            <script src="//platform.linkedin.com/in.js" type="text/javascript"></script>
            <script type="IN/MemberProfile" data-id="https://www.linkedin.com/in/wade-waldron" data-format="click" data-related="false" data-text="Wade Waldron"></script>
        </div>
    </article>
</section>

Reactive Architecture: CQRS & Event Sourcing

This course will discuss the reasons we use CQRS/ES, what benefits it provides us, but also what it will cost us to use.

<section class="about">
    <h2>About This Course</h2>
    <p>
      Building Reactive Systems requires a balance between Consistency and Availability. This course will explain why that balance exists. We will discuss the CAP theorem as well as the Laws of Scalability that dictate what is possible to achieve when we build distributed systems. We will also introduce some techniques we can use to strike the right balance. All of this will be grounded in a discussion which starts first in the real world.
    </p>
</section>
<h2>What will I get after passing this course?</h2>
<ul>
    <li>You will receive a completion certificate.</li>
</ul>
<h2>Course Syllabus</h2>
<ul>
    <li>Consistency, Availability, and Scalability</li>
    <ul>
      <li>Definitions for Consistency, Availability, and Scalability.</li>
      <li>A discussion of the difference between Performance and Scalability.</li>
    </ul>
    <li>Consistency in Distributed Systems</li>
    <ul>
      <li>An indepth discussion of how physics impacts our ability to provide consistency in a distributed system.</li>
      <li>A definition, and discussion, of Eventual Consistency.</li>
      <li>A definition, and discussion, of Strong Consistency.</li>
    </ul>
    <li>Laws of Scalability</li>
    <ul>
        <li>Contention in Distributed Systems.</li>
        <li>Amdahl's Law.</li>
        <li>Coherency in Distributed Systems./li>
        <li>Gunther's Law of Universal Scalability</li>
  </ul>
    <li>CAP Theorem</li>
    <ul>
      	<li>Defining Partition Tolerance.</li>
        <li>Discussing the balance between Consistency and Availability.</li>
    </ul>
    <li>Sharding</li>
    <ul>
        <li>Using Sharding for Consistency.</li>
        <li>Explanation of how Sharding works.</li>
        <li>Advantages/Disadvantages of Sharding.</li>
  </ul>
  <li>Conflict-Free Replicated Data Types (CRDTs)</li>
    <ul>
        <li>Using CRDTs for Availability.</li>
        <li>Explanation of how CRDTs work.</li>
        <li>Advantages/Disadvantages of CRDTs.</li>
    </ul>
  <li>Consistency or Availability</li>
    <ul>
        <li>How do we decide which one to pick?</li>
    </ul>
</ul>
<h2>General Information</h2>
<ul>
    <li>It is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be taken as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Experience in the design of software systems.</li>
</ul>
<h2>Grading scheme</h2>
<ul>
    <li>The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark.</li>
    <li>You have 1 attempt to take the final exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
    <h2>Requirements</h2>
    <ul>Reactive Architecture: Introduction to Reactive Systems, Reactive Architecture: Domain Driven Design and Reactive Architecture: Reactive Microservices.
    </ul>
</section>

<section class="course-staff">
    <h2>Course Staff</h2>
    <article class="teacher">
        <div class="teacher-image">
            <img src="/static/WadeWaldron512x512.jpeg" align="left" style="margin:0 20 px 0" alt="Wade Waldron">
        </div>

        <h3>Wade Waldron</h3>
        <p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p>
        <p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>
        <p>In 2016, Wade released a book "Applied Akka Patterns" along with his co-author Michael Nash</p>
        <p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

        <div><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow @wdwaldron</a>
            <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
        </div>

        <div>
            <script src="//platform.linkedin.com/in.js" type="text/javascript"></script>
            <script type="IN/MemberProfile" data-id="https://www.linkedin.com/in/wade-waldron" data-format="click" data-related="false" data-text="Wade Waldron"></script>
        </div>
    </article>
</section>

Reactive Architecture: Building Scalable Systems

Building Reactive Systems requires a balance between Consistency and Availability. This course will explain why that balance exists.

<section class="about">
<section>
<h2>ABOUT THIS COURSE</h2>

<p>This course teaches the core principles behind Reactive Architecture. It introduces students to why we need Reactive Systems, and what problems they are trying to solve. It also contrasts Reactive Architectures with Reactive Programming, showing how they relate, and how they are different.</p>
</section>

<hr />
<h2>COURSE SYLLABUS</h2>

<ul>
	<li><strong>Why Reactive</strong>

	<ul>
		<li>What is the problem that Reactive Architecture is attempting to solve?</li>
		<li>How does unresponsive software impact it&#39;s users?</li>
		<li>What is the goal of Reactive Architecture?</li>
	</ul>
	</li>
	<li><strong>Reactive Principles</strong>
	<ul>
		<li>An introduction to the Reactive Manifesto</li>
		<li>An explanation of the Reactive Principles</li>
	</ul>
	</li>
	<li><strong>Reactive Systems vs Reactive Programming</strong>
	<ul>
		<li>What are Reactive Systems?</li>
		<li>What is Reactive Programming?</li>
		<li>How are Reactive Systems related to Reactive Programming?</li>
		<li>The Actor Model and it&#39;s relationship to Reactive Systems.</li>
	</ul>
	</li>
	<li><strong>Case Study: Reactive BBQ</strong>
	<ul>
		<li>An analysis of a specific case study</li>
	</ul>
	</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>Free.</li>
	<li>Self-paced.</li>
</ul>

<hr />
<h2>WHAT WILL I GET AFTER PASSING THIS COURSE?</h2>

<ul>
	<li>You will receive a certificate of course completion.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>Some experience in the design of software systems is ideal but not required</li>
</ul>

<section>
<hr />
<h2>REQUIREMENTS</h2>

<ul>
	<li>None.</li>
</ul>
</section>

<section>
<hr />
<h2>COURSE STAFF</h2>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Wade%20Waldron%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Wade Waldron</h3>

<p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p>

<p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>

<p>In 2016, Wade released a book &quot;Applied Akka Patterns&quot; along with his co-author Michael Nash</p>

<p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

<p><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw">Follow @wdwaldron</a></p>
</article>
</section>

Reactive Architecture: Introduction to Reactive Systems

This course teaches the core principles behind Reactive Architecture. It introduces students to why we need Reactive Systems, and what problems they are trying to solve. It also contrasts Reactive Architectures with Reactive Programming, showing how they relate, and how they are different.

<section class="about">
  <h2>About This Course</h2>
  <p>Writing MapReduce programs to analyze your Big Data can get complex. Hive can help make querying your data much easier. Apache Hive, first created at Facebook, is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. This course will get you started so that you can use Hive for Data Warehousing tasks on your Big Data projects. </p>
    <h2>What will I get after passing this course?</h2>
  <ul>
    <li>
      You will receive a completion certificate.
    </li>
    </ul>
  <h2>Course Syllabus</h2>
  <ul>
    <li><b>Lesson 1 - Introduction to Hive</b></li>
<ul>
  <li>Describe what Hive is, what it’s used for and how it compares to other similar technologies</li>
  <li>Describe the Hive architecture</li>
<li>Describe the main components of Hive</li>
<li>List interesting ways others are using Hive</li>
    </ul>
  <li><b>Lesson 2 - Hive DDL</b></li>
    <ul>
      <li>Create databases and tables in Hive, while using a variety of different Data Types</li>
  <li>Run a variety of different DDL commands</li>
  <li>Use Partitioning to improve performance of Hive queries</li>
  <li>Create Managed and External tables in Hive</li>
    </ul>
    <li><b>Lesson 3 - Hive DM</b>L</li>
    <ul>
      <li>Load data into Hive</li>
  <li>Export data out of Hive</li>
<li>Run a variety of different HiveQL DML queries</li>
    </ul>
    <li><b>Lesson 4 - Hive Operators and Functions</b></li>
    <ul>
      <li>Use a variety of Hive Operators in your queries</li>
      <li>Utilize Hive’s Built-in Functions</li>
      <li>Explain ways to extend Hive functionality</li>
    </ul>
</ul> 
  
  <p>
    <h2>General information</h2>
    <ol>
  <li>This course is free.</li>
  <li>It is self-paced.</li>
  <li>It can be taken at any time.</li>
  <li>It can be taken as many times as you wish.</li>
  </ol>
</section>

<section class="prerequisites">
  <h2>Recommended skills prior to taking this course</h2>
    <p>
    <ul>
     <li>Have taken the Hadoop Foundations I course</li>
    <li>Basic understanding of Apache Hadoop and BigData.</li>
    <li>Working knowledge of SQL</li>
    <li>Basic Linux Operating System knowledge</li>
    </ul>
	</p>
  
</section>

<section class="grading scheme">

  <h2>Grading scheme</h2>
  <ul>
    <li>The minimum passing mark for the course is 70%, where the review questions are worth 50% and the final exam is worth 50% of the course mark. </li>
    <li>You have 1 attempt to take the exam with multiple attempts per question.</li>
  </ul>

</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None.</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Aaron%20Ritchie%20pic.png" align="left" style="margin:0 20 px 0" alt="Course Staff Image #1">
    </div>

    <h3>Aaron Ritchie</h3>
    <p>Aaron Ritchie has worked in the Information Management
division of IBM for over 11 years and has held a variety of roles
within the Center of Excellence and Education groups. Aaron has
worked as an IT Specialist, Learning Developer, and Project
Manager. He is certified in multiple IBM products and enjoys
working with an assortment of open-source technologies. Aaron
holds a Bachelor of Science in Computer Science degree from
Clarkson University and a Master of Science in Information
Technology degree from WPI.</p>
  </article>
</section>

Accessing Hadoop Data Using Hive

Hive is a data warehousing tool built on top of Hadoop. Learn how to easily query and analyze your Big Data projects with this free Apache Hive course.

<section class="about">
<section>
<h2>ABOUT THIS COURSE</h2>

<p>Domain Driven Design is a technique commonly used to build Reactive Systems. This course will introduce the core elements of Domain Driven Design. It will also explain how those elements relate to Reactive Systems.</p>
</section>

<hr />
<h2>COURSE SYLLABUS</h2>

<ul>
	<li>Domain Driven Design
	<ul>
		<li>An introduction to core Domain Driven Design concepts</li>
		<li>What is a domain?</li>
		<li>What is a ubiquitous language?</li>
		<li>Who are the domain experts?</li>
	</ul>
	</li>
	<li>Decomposing the Domain
	<ul>
		<li>Identifying activities within the domain</li>
		<li>Initial categorizing of activities</li>
		<li>Identifying Bounded Contexts</li>
	</ul>
	</li>
	<li>Domain Building Blocks
	<ul>
		<li>Domain Activities: Commands, Events, and Queries</li>
		<li>Domain Objects: Value Objects, Entities, and Aggregate Roots</li>
		<li>Domain Abstractions: Services, Factories, and Repositories</li>
	</ul>
	</li>
	<li>Hexagonal Architecture
	<ul>
		<li>What is it and how does it relate to Domain Driven Design</li>
		<li>Ports and Adapters</li>
		<li>Infrastructure, APIs and Domains</li>
	</ul>
	</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	<li>Free.</li>
	<li>Self-paced.</li>
</ul>

<hr />
<h2>WHAT WILL I GET AFTER PASSING THIS COURSE?</h2>

<ul>
	<li>You will receive a certificate of course completion.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>Some experience in the design of software systems is ideal but not required</li>
</ul>

<section>
<hr />
<h2>REQUIREMENTS</h2>

<ul>
	<li>None.</li>
</ul>
</section>

<section>
<hr />
<h2>COURSE STAFF</h2>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Wade%20Waldron%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Wade Waldron</h3>

<p>Wade Waldron is the Microservices Practice Lead, Senior Training Developer, and Senior Consultant at Lightbend. He is the primary author of the Lightbend Reactive Architecture training course.</p>

<p>Wade started his career as a developer in 2005 building games for the Playstation 2, Xbox, etc. In 2011 he joined Point2 Homes where he began working on an Akka based microservices platform to provide realtime updates to real estate websites. In 2014 Wade joined BoldRadius where he consulted with a variety of companies on how to use Akka, and related technologies. He also began teaching courses on Akka and Scala.</p>

<p>In 2016, Wade released a book &quot;Applied Akka Patterns&quot; along with his co-author Michael Nash</p>

<p>Today Wade works with Lightbend where he consults with clients and builds training materials. He focuses on helping people make a smooth transition from a monolithic application to Reactive microservices.</p>

<p><a href="https://twitter.com/wdwaldron?ref_src=twsrc%5Etfw">Follow @wdwaldron</a></p>
</article>
</section>

Reactive Architecture: Domain Driven Design

Domain Driven Design is a technique commonly used to build Reactive Systems. This course will introduce the core elements of Domain Driven Design. It will also explain how those elements relate to Reactive Systems.

<div class="trix-content">
  <div>Have you ever wanted to create an image detector that would tell you whether or not a picture is of a certain object? Look no further because we have you covered!<br><br>
</div><div>In this project, we will be utilising  simple learning rules to "teach" a neural network how to recognize hotdogs an image and solve the big question: hotdog or not hotdog?<br><br>
</div><div>Let's find out!<br><br>
</div><h1><strong>A Look At the Project Ahead</strong></h1><div>Once you have completed, this project, you’ll be able to:</div><ul>
<li>Create Python functions with libraries to preview, load and train your model to recognize hotdogs with PyTorch</li>
<li>Transform a dataset of images to prepare it for training</li>
<li>Train a state of the art image classifier by using transfer learning: training a model to solve one problem and applying that training to a related problem</li>
</ul><h1>What You'll Need</h1><div>Before starting this lab, it'll be helpful to be familiar with the following:</div><ul>
<li>Basic Python knowledge</li>
<li>Basic knowledge about image classification and neural networks, provided in optional readings</li>
</ul><div>If you're interested in learning more about image classification and neural networks you can take a look at the following courses: Computer Vision and Image Processing Fundamentals course on <a href="https://www.coursera.org/learn/introduction-computer-vision-watson-opencv?utm_medium=Exinfluencer&amp;utm_source=Exinfluencer&amp;utm_content=000026UJ&amp;utm_term=10006555&amp;utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsbuildahotdognothotdogclassifierguidedproject28954781-2021-01-01">Coursera</a> or <a href="https://www.edx.org/course/computer-vision-and-image-processing-fundamentals">edX</a>.<br><br>
</div><h1>Instructor</h1><div>Kathy An, IBM<br>Weiqing Wang, IBM<br><br>
</div><h1>Other Contributors</h1><div>Joseph Santarcangelo, IBM</div>
</div>


Train a Hotdog Image Recognition Model with Python

We all know that machines can do a lot these days, including recognizing whether or not an image has a certain object in it. But did you know that you, too, can train a model to do just that? In this guided project, you’ll learn how to train a model in Python with PyTorch, a machine learning library, to detect if a picture has a hotdog in it. This process can be repeated with any object, whether it is a bird, a plane or even Superman!

<div class="trix-content">
  <div><br></div><div>Open Liberty is an open application framework designed for the cloud. It’s small, lightweight, and designed with modern cloud-native application development in mind. It supports the full MicroProfile and Jakarta EE APIs and is composable, meaning that you can use only the features that you need.<br><br>
</div><div>Maven is an automation build tool that provides an efficient way to develop Java applications. Using Maven, you will build a simple microservice, called <strong>system</strong>, that collects basic system properties from your laptop and displays them on an endpoint that you can access in your web browser.</div><div>
<br><br>
</div><h1><strong>What you'll learn</strong></h1><div>You will learn how to run and update a simple REST microservice on Open Liberty. You will use Maven to build and deploy the microservice as well as to interact with the running Liberty instance.</div><div><br></div><div>You'll also explore how to package your application with Open Liberty so that it can be deployed anywhere in one go. You will then make Liberty configuration and code changes and see how they are immediately picked up by a running instance.<br><br>
</div><div>Finally, you will package the application along with the server configuration into a Docker image and run that image as a container.<br><br>
</div><h1>What You’ll Need  </h1><div>Just a web browser!  Everything else is provided to you via the IBM Skills Network Labs environment.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div><div><br></div>
</div>


Getting started with Open Liberty

Learn how to develop a Java application on Open Liberty with Maven and Docker.

<div class="trix-content">
  <div>With a microservices-based architecture, you need robust testing to ensure that microservices that depend on one another are able to communicate effectively. Typically, to prevent multiple points of failure at different integration points, a combination of unit, integration, and end-to-end tests are used. While unit tests are fast, they are less trustworthy because they run in isolation and usually rely on mock data.</div><div>Integration tests address this issue by testing against real running services. However, they tend to be slow as the tests depend on other microservices and are less reliable because they are prone to external changes.<br><br>
</div><div>Usually, end-to-end tests are more trustworthy because they verify functionality from the perspective of a user. However, a graphical user interface (GUI) component is often required to perform end-to-end tests, and GUI components rely on third-party software, such as Selenium, which requires heavy computation time and resources.<br><br>
</div><div><strong>What is contract testing?</strong></div><div>Contract testing bridges the gaps among the shortcomings of these different testing methodologies. Contract testing is a technique for testing an integration point by isolating each microservice and checking whether the HTTP requests and responses that the microservice transmits conform to a shared understanding that is documented in a contract. This way, contract testing ensures that microservices can communicate with each other.</div><div>
<a href="https://docs.pact.io/">Pact</a> is an open source contract testing tool for testing HTTP requests, responses, and message integrations by using contract tests.</div><div>The <a href="https://docs.pact.io/pact_broker/docker_images">Pact Broker</a> is an application for sharing Pact contracts and verification results. The Pact Broker is also an important piece for integrating Pact into continuous integration and continuous delivery (CI/CD) pipelines.</div><div>The two microservices you will interact with are called system and inventory. The system microservice returns the JVM system properties of its host. The inventory microservice retrieves specific properties from the system microservice.</div><div>You will learn how to use the Pact framework to write contract tests for the inventory microservice that will then be verified by the system microservice.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Implement consumer-driven contract testing for Java microservices using the Pact framework

Learn how to test Java microservices with consumer-driven contracts in Open Liberty.

<div class="trix-content">
  <div>You will learn how to access a REST service, serialize a Java object that contains a list of artists and their albums, and use two different approaches to deserialize the returned JSON resources. The first approach consists of using the Java API for JSON Binding (JSON-B) to directly convert JSON messages into Java objects. The second approach consists of using the Java API for JSON Processing (JSON-P) to process the JSON.<br><br>
</div><div>The REST service that provides the artists and albums resources is already written for you. When the server is running, this service is accessible at the http://localhost:9080/artists endpoint, which responds with the artists.json file.<br><br>You will implement the following two endpoints using the two deserialization approaches:</div><ul>
<li>…​/artists/total to return the total number of artists in the JSON</li>
<li>…​/artists/total/&lt;artist&gt; to return the total number of albums in the JSON for the particular artist</li>
</ul><div>If you are interested in learning more about REST services and how you can write them, read <a href="https://openliberty.io/guides/rest-intro.html">Creating a RESTful web service</a>.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Consuming a RESTful Java web service using JSON-B and JSON-P

Explore how to access a simple RESTful web service and consume its resources in Java using JSON-B and JSON-P.

<div class="trix-content">
  <div>You will add token-based authentication mechanisms to authenticate, authorize, and verify users by implementing MicroProfile JWT in the system microservice.</div><div>A JSON Web Token (JWT) is a self-contained token that is designed to securely transmit information as a JSON object. The information in this JSON object is digitally signed and can be trusted and verified by the recipient.</div><div>For microservices, a token-based authentication mechanism offers a lightweight way for security controls and security tokens to propagate user identities across different services. JSON Web Token is becoming the most common token format because it follows well-defined and known standards.</div><div>MicroProfile JWT standards define the required format of JWT for authentication and authorization. The standards also map JWT claims to various Jakarta EE container APIs and make the set of claims available through getter methods.</div><div>In this guide, the application uses JWTs to authenticate a user, allowing them to make authorized requests to a secure backend service.</div><div>You will be working with two services, a frontend service and a secure system backend service. The frontend service logs a user in, builds a JWT, and makes authorized requests to the secure system service for JVM system properties. The following diagram depicts the application that is used in this guide:<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/microprofile-jwt/JWT_Diagram.png" width="490" height="654"><figure class="attachment attachment--preview">
  <img width="490" height="654" src="https://openliberty.io/img/guide/microprofile-jwt/JWT_Diagram.png">
</figure></action-text-attachment>
</div><div>The user signs in to the frontend service with a username and a password, at which point a JWT is created. The frontend service then makes requests, with the JWT included, to the system backend service. The secure system service verifies the JWT to ensure that the request came from the authorized frontend service. After the JWT is validated, the information in the claims, such as the user’s role, can be trusted and used to determine which system properties the user has access to.</div><div>To learn more about JSON Web Tokens, check out the <a href="https://jwt.io/introduction/">jwt.io website</a>. If you want to learn more about how JWTs can be used for user authentication and authorization, check out the Open Liberty <a href="https://openliberty.io/docs/latest/single-sign-on.html">Single Sign-on documentation</a>.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Securing Java microservices with Eclipse MicroProfile JSON Web Token (MicroProfile JWT)

You’ll explore how to control user and role access to microservices with MicroProfile JSON Web Token (MicroProfile JWT).

<div class="trix-content">
  <div>You will learn how to build a MicroProfile Rest Client to access remote RESTful services using asynchronous method calls. You’ll update the template interface for a MicroProfile Rest Client to use the CompletionStage return type. The template interface maps to the remote service that you want to call. A CompletionStage interface allows you to work with the result of your remote service call asynchronously.<br><br>
</div><div><strong>What is asynchronous programming?</strong></div><div>Imagine asynchronous programming as a restaurant. After you’re seated, a waiter takes your order. Then, you must wait a few minutes for your food to be prepared. While your food is being prepared, your waiter may take more orders or serve other tables. After your food is ready, your waiter brings out the food to your table. However, in a synchronous model, the waiter must wait for your food to be prepared before serving any other customers. This method blocks other customers from placing orders or receiving their food.</div><div>You can perform lengthy operations, such as input/output (I/O), without blocking with asynchronous methods. The I/O operation can occur in the background and a callback notifies the caller to continue its computation when the original request is complete. As a result, the original thread frees up so it can handle other work rather than wait for the I/O to complete. Revisiting the restaurant analogy, food is prepared asynchronously in the kitchen and your waiter is freed up to attend to other tables.</div><div>In the context of REST clients, HTTP request calls can be time consuming. The network might be slow, or maybe the upstream service is overwhelmed and can’t respond quickly. These lengthy operations can block the execution of your thread when it’s in use and prevent other work from being completed.</div><div>The application in this guide consists of three microservices, system, inventory, and query. Every 15 seconds the system microservice calculates and publishes an event that contains its average system load. The inventory microservice subscribes to that information so that it can keep an updated list of all the systems and their current system loads.<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/microprofile-rest-client-async/QueryService.png" width="253" height="327"><figure class="attachment attachment--preview">
  <img width="253" height="327" src="https://openliberty.io/img/guide/microprofile-rest-client-async/QueryService.png">
</figure></action-text-attachment>
</div><div>The microservice that you will modify is the query service. It communicates with the inventory service to determine which system has the highest system load and which system has the lowest system load.</div><div>The system and inventory microservices use MicroProfile Reactive Messaging to send and receive the system load events. If you want to learn more about reactive messaging, see the <a href="https://openliberty.io/guides/microprofile-reactive-messaging.html">Creating Reactive Java Microservices</a> guide.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Consuming RESTful Java microservices asynchronously using Eclipse MicroProfile Rest Client

Learn how to use MicroProfile Rest Client to invoke RESTful microservices asynchronously over HTTP.

<div class="trix-content">
  <div>You will learn how to integrate RESTful Java microservices with a reactive system by using MicroProfile Reactive Messaging. RESTful Java microservices don’t use reactive concepts, so you will learn how to bridge the gap between the two using the RxJava library. In this guide, you will modify two microservices in an application so that when a user hits the RESTful endpoint, the microservice generates producer events.</div><div>The application in this guide consists of two microservices, system and inventory. The following diagram illustrates the application:<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/microprofile-reactive-messaging-rest-integration/reactive-messaging-system-inventory-rest.png" width="720" height="961"><figure class="attachment attachment--preview">
  <img width="720" height="961" src="https://openliberty.io/img/guide/microprofile-reactive-messaging-rest-integration/reactive-messaging-system-inventory-rest.png">
</figure></action-text-attachment>Every 15 seconds, the system microservice calculates and publishes events that contain its current average system load. The inventory microservice subscribes to that information so that it can keep an updated list of all the systems and their current system loads. The current inventory of systems can be accessed via the /systems REST endpoint.</div><div>You will update the inventory microservice to subscribe to a PUT request response. This PUT request response accepts a specific system property in the request body, queries that system property on the system microservice, and provides the response. You will also update the system microservice to handle receiving and sending events that are produced by the new endpoint. You will configure new channels to handle the events that are sent and received by the new endpoint. To learn more about how the reactive Java services that are used in this guide work, check out the <a href="https://openliberty.io/guides/microprofile-reactive-messaging.html">Creating reactive Java microservices</a> guide.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Integrating RESTful services with a reactive system

Learn how to integrate RESTful Java microservices with a reactive system by using MicroProfile Reactive Messaging.

<div class="trix-content">
  <div>You will learn how to set up, run, and iteratively develop a simple REST application in a container with Open Liberty and Docker.</div><div>Open Liberty is an application server designed for the cloud. It’s small, lightweight, and designed with modern cloud-native application development in mind. Open Liberty simplifies the development process for these applications by automating the repetitive actions associated with running applications inside containers, like rebuilding the image and stopping and starting the container.</div><div>You’ll also learn how to create and run automated tests for your application and container.</div><div>The implementation of the REST application can be found in the start/src directory. To learn more about this application and how to build it, check out the <a href="https://openliberty.io/guides/rest-intro.html">Creating a RESTful web service</a> guide.</div><div>
<br>What is Docker?</div><div>Docker is a tool that you can use to deploy and run applications with containers. You can think of Docker like a virtual machine that runs various applications. However, unlike a typical virtual machine, you can run these applications simultaneously on a single system and independent of one another.</div><div>Learn more about Docker on the <a href="https://www.docker.com/what-docker">official Docker website</a>.</div><div>
<br>What is a container?</div><div>A container is a lightweight, stand-alone package that contains a piece of software that is bundled together with the entire environment that it needs to run. Containers are small compared to regular images and can run on any environment where Docker is set up. Moreover, you can run multiple containers on a single machine at the same time in isolation from each other.</div><div>Learn more about containers on the <a href="https://www.docker.com/what-container">official Docker website</a>.</div><div>
<br>Why use a container to develop?</div><div>Consider a scenario where you need to deploy your application on another environment. Your application works on your local machine, but when you try to run it on your cloud production environment, it breaks. You do some debugging and discover that you built your application with Java 8, but this cloud production environment has only Java 11 installed. Although this issue is generally easy to fix, you don’t want your application to be missing dozens of version-specific dependencies. You can develop your application in this cloud environment, but that requires you to rebuild and repackage your application every time you update your code and wish to test it.</div><div>To avoid this kind of problem, you can instead choose to develop your application in a container locally, bundled together with the entire environment that it needs to run. By doing this, you know that at any point in your iterative development process, the application can run inside that container. This helps avoid any unpleasant surprises when you go to test or deploy your application down the road. Containers run quickly and do not have a major impact on the speed of your iterative development.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Learn how to use Docker containers for iterative development

Learn how to use Docker containers for iterative development.

<div class="trix-content">
  <div>You’ll start with an existing REST application that runs on Open Liberty and use <a href="https://microshed.org/microshed-testing/">MicroShed Testing</a> to write tests for the application that exercise the application in a Docker container.</div><div>Sometimes tests might pass in development and testing (dev/test) environments, but fail in production because the application runs differently in production than in dev/test. Fortunately, you can minimize these differences between dev/test and production by testing your application in the same Docker container that you’ll use in production.</div><div><br></div><h1>What is Docker?</h1><div>Docker is a tool that you can use to deploy and run applications with containers. You can think of Docker as a virtual machine that runs various applications. However, unlike with a typical virtual machine, you can run these applications simultaneously on a single system and independent of one another.</div><div>Learn more about Docker on the <a href="https://www.docker.com/what-docker">official Docker website</a>.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Testing a MicroProfile or Jakarta EE application using MicroShed Testing with an Open Liberty docker container

Learn how to use MicroShed Testing to test a MicroProfile or Jakarta EE application.

<div class="trix-content">
  <div>You will learn how to write integration tests for reactive Java microservices and to run the tests in true-to-production environments by using containers with <a href="https://microshed.org/microshed-testing/">MicroShed Testing</a>. MicroShed Testing tests your containerized application from outside the container so that you are testing the exact same image that runs in production. The reactive application in this guide sends and receives messages between services by using an external message broker, <a href="https://kafka.apache.org/">Apache Kafka</a>. Using an external message broker enables asynchronous communications between services so that requests are non-blocking and decoupled from responses. You can learn more about reactive Java services that use an external message broker to manage communications in the <a href="https://openliberty.io/guides/microprofile-reactive-messaging.html">Creating reactive Java microservices</a> guide.<action-text-attachment content-type="image" url="https://openliberty.io/img/guide/reactive-service-testing/reactive-messaging-system-inventory.png" width="1160" height="1234"><figure class="attachment attachment--preview">
  <img width="1160" height="1234" src="https://openliberty.io/img/guide/reactive-service-testing/reactive-messaging-system-inventory.png">
</figure></action-text-attachment>
</div><div><strong>True-to-production integration testing with MicroShed Testing</strong></div><div>Tests sometimes pass during the development and testing stages of an application’s lifecycle but then fail in production because of differences between your development and production environments. While you can create mock objects and custom setups to minimize differences between environments, it is difficult to mimic a production system for an application that uses an external messaging system. MicroShed Testing addresses this problem by enabling the testing of applications in the same Docker containers that you’ll use in production. As a result, your environment remains the same throughout the application’s lifecycle – from development, through testing, and into production. You can learn more about MicroShed Testing in the <a href="https://openliberty.io/guides/microshed-testing.html">Testing a MicroProfile or Jakarta EE application</a> guide.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Testing reactive Java microservices using MicroShed Testing framework

Learn how to test reactive Java microservices in true-to-production environments using MicroShed Testing.

<div class="trix-content">
  <div>You’ll learn how to enable automatic tracing for JAX-RS methods and how to create custom tracers for non-JAX-RS methods by using MicroProfile OpenTracing.<br><br>
</div><div>OpenTracing is a standard API for instrumenting microservices for distributed tracing. Distributed tracing helps troubleshoot microservices by examining and logging requests as they propagate through a distributed system. Distributed tracing allows developers to tackle the otherwise difficult task of debugging these requests. Without a distributed tracing system in place, analyzing the workflows of operations becomes difficult. Pinpointing when and where a request is received and when responses are sent becomes difficult.<br><br>
</div><div>MicroProfile OpenTracing enables distributed tracing in microservices without adding any explicit distributed tracing code to the application. Note that the MicroProfile OpenTracing specification does not address the problem of defining, implementing, or configuring the underlying distributed tracing system. Rather, the specification makes it easier to instrument services with distributed tracing given an existing distributed tracing system.<br><br>
</div><div>You’ll configure the provided inventory and system services to use distributed tracing with MicroProfile OpenTracing. You’ll run these services in two separate JVMs made of two server instances to demonstrate tracing in a distributed environment. If all the components were to run on a single server, then any logging software would do the trick.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Enabling distributed tracing in microservices with Zipkin

Explore how to enable and customize tracing of JAX-RS and non-JAX-RS methods by using MicroProfile OpenTracing and the Zipkin tracing system.

<div class="trix-content">
  <div>You will learn how to externalize and inject both static and dynamic configuration properties for microservices using MicroProfile Config.</div><div>You will learn to aggregate multiple configuration sources, assign prioritization values to these sources, merge configuration values, and create custom configuration sources.<br><br>
</div><div>The application that you will be working with is an inventory service which stores the information about various JVMs running on different hosts. Whenever a request is made to the inventory service to retrieve the JVM system properties of a particular host, the inventory service will communicate with the system service on that host to get these system properties. You will add configuration properties to simulate if a service is down for maintenance.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Externalizing configuration for Java microservices using Eclipse MicroProfile Config

Learn how to provide external configuration to microservices using MicroProfile Config.

<div class="trix-content">
  <div>You will learn how to use MicroProfile Health to report the health status of microservices and take appropriate actions based on this report.</div><div>MicroProfile Health allows services to report their health, and it publishes the overall health status to a defined endpoint. A service reports UP if it is available and reports DOWN if it is unavailable. MicroProfile Health reports an individual service status at the endpoint and indicates the overall status as UP if all the services are UP. A service orchestrator can then use the health statuses to make decisions.<br><br>
</div><div>A service checks its own health by performing necessary self-checks and then reports its overall status by implementing the API provided by MicroProfile Health. A self-check can be a check on anything that the service needs, such as a dependency, a successful connection to an endpoint, a system property, a database connection, or the availability of required resources. MicroProfile offers checks for both liveness and readiness.<br><br>
</div><div>You will add liveness and readiness checks to the system and inventory services, which are provided for you, and implement what is necessary to report health status by using MicroProfile Health.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Checking the health of Java microservices by using Eclipse MicroProfile Health Check

Explore how to report and check the health of a microservice with MicroProfile Health.

<div class="trix-content">
  <div>You will learn how to create health check endpoints for your microservices. Then, you will configure Kubernetes to use these endpoints to keep your microservices running smoothly.<br><br>
</div><div>MicroProfile Health allows services to report their health, and it publishes the overall health status to defined endpoints. If a service reports UP, then it’s available. If the service reports DOWN, then it’s unavailable. MicroProfile Health reports an individual service status at the endpoint and indicates the overall status as UP if all the services are UP. A service orchestrator can then use the health statuses to make decisions.</div><div>Kubernetes provides liveness and readiness probes that are used to check the health of your containers. These probes can check certain files in your containers, check a TCP socket, or make HTTP requests. MicroProfile Health exposes readiness and liveness endpoints on your microservices. Kubernetes polls these endpoints as specified by the probes to react appropriately to any change in the microservice’s status. Read the <a href="https://openliberty.io/guides/microprofile-health.html">Adding health reports to microservices</a> guide to learn more about MicroProfile Health.<br><br>
</div><div>The two microservices you will work with are called system and inventory. The system microservice returns the JVM system properties of the running container and it returns the pod’s name in the HTTP header making replicas easy to distinguish from each other. The inventory microservice adds the properties from the system microservice to the inventory. This demonstrates how communication can be established between pods inside a cluster.<br><br>
</div><h1>Created by </h1><div>The Open Liberty Project team</div>
</div>


Checking the health of Java microservices by using Kubernetes readiness and liveness probes

Learn how to check the health of microservices on Kubernetes by setting up readiness and liveness probes to inspect MicroProfile Health Check endpoints.

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