The demand for data analysts is projected to grow by over 30 percent (US Bureau of Labor Statistics). Businesses need data analysis skills! In this course, you’ll explore the fundamentals of data analysis and kickstart your data analytics journey.

During this course, which is also part of the IBM Data Analyst Professional Certificate, you’ll explore the modern data ecosystem and the roles of data engineers, data analysts, data scientists, business analysts, and business intelligence analysts. You’ll learn about a data analyst's daily tasks and responsibilities, looking at techniques for gathering, cleaning, and preparing data for analysis. Plus, you'll gain a comprehensive understanding of the tools and technologies data professionals use.

You will learn about:

Various data storage solutions like RDBMS and NoSQL databases.
Important concepts like data marts, data lakes, ETL, and big data processing tools.
Techniques for identifying valuable data sources, including collecting and importing data, and cleaning it for accurate analysis.
Foundational knowledge of statistical analysis and data mining techniques.
How to create compelling data visualizations and dashboards to communicate your findings to both technical and non-technical audiences.

As you progress through the course, you will also gain insights into various data analyst career paths that will help you envision your roadmap to becoming a successful data analyst.

You will then apply your new knowledge in a final project that challenges you with a real-world scenario in data analysis so you can showcase your skills to prospective employers.

Enroll today and kickstart your data analytics career... You have a lot to look forward to!

Course Syllabus

Module 1: What is Data Analytics

Modern Data Ecosystem
Key Players in the Data Ecosystem
Defining Data Analysis
What is Data Analytics?
Data Analytics vs. Data Analysis

Module 2: The Data Analyst Role

Responsibilities of a Data Analyst
Qualities and Skills to be a Data Analyst
A Day in the Life of a Data Analyst
Applications of Data Analytics

Module 3: The Data Ecosystem and Languages for Data Professionals

Overview of Data Repositories
Types of Data
Understanding Different Types of File Formats
Sources of Data
Languages for Data Professionals

Module 4: Understanding Data Repositories and Big Data Platforms

RDBMS
NoSQL
Data Marts, Data Lakes, ETL, and Data Pipelines
Foundations of Big Data
Big Data Processing Tools

Module 5: Gathering Data

Identifying Data for Analysis
Data Sources
How to Gather and Import Data

Module 6: Wrangling Data

What is Data Wrangling?
Tools for Data Wrangling
Data Cleaning
Data Preparation and Reliability

Module 7: Analyzing and Mining Data

Overview of Statistical Analysis
What is Data Mining?
Tools for Data Mining

Module 8: Communicating Data Analysis Findings

Overview of Communicating and Sharing Data Analysis Findings
Introduction to Data Visualization
Introduction to Visualization and Dashboarding Software

Module 9: Opportunities and Learning Paths

Career Opportunities in Data Analysis
Get into Data Profession
The Many Paths to Data Analysis
Career Options for Data Professionals

Learning Objectives:

Explain what Data Analytics is and the key steps in the Data Analytics process.
Differentiate between data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst.
Describe the different types of data structures, file formats, and data sources.
Explain the use of different types of data repositories, the ETL process, and Big Data platforms.
List the different career opportunities in data analysis and the resources needed to become skilled in this domain.
Demonstrate your understanding of gathering, wrangling, mining, analyzing, and visualizing data.

Recommended Skills Prior to Taking this Course

This is a beginner-friendly introduction to data analysis; therefore, no prior experience is necessary. However, basic knowledge of using a computer, navigating files and folders, and using basic software applications is recommended. To get the most out of this course, you need to have basic computer skills, foundational mathematics and statistics, and familiarity with spreadsheets.

Data Analytics: An Introduction

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