In this comprehensive course, you will dive deep into the methods and practices behind data science, focusing on problem-solving techniques that ensure data relevance and proper manipulation for various business scenarios. This course emphasizes the importance of following a structured approach to data science problems, showcasing two notable methodologies: Foundational Data Science Methodology and the six-stage Cross-Industry Process for Data Mining (CRISP-DM).

You will start by learning how to frame a business or research problem, a crucial first step in any data science project. Moving forward, you will explore how data scientists gather, prepare, and analyze data, ensuring the data used is relevant and correctly handled to address the core question. Next, you will dive into building data models, deploying those models, and refining your insights through data storytelling and feedback.

Throughout the course, you will apply these concepts using real-world inspired scenarios. Through hands-on labs in Jupyter Notebooks and Python, you will practice every data science methodology stage, honing your data preparation, modeling, and communication skills. You will earn a skill badge upon successful completion, verifying your acquired knowledge and ability to apply data science methodologies in practice.

You’ll learn about:

The importance of using a structured methodology for solving data science problems.

The key steps involved in addressing a data science problem.

How to identify and choose appropriate data sources for your analysis.

The six stages of the CRISP-DM methodology through a practical case study.

Applying data science methods to real-world scenarios.

Gaining hands-on experience with Jupyter Notebooks and Python in data science labs.

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

PREREQUISITES: 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.

IBM Data Analyst Professional Certificate

This course is part of the IBM Data Analyst Professional Certificate. If you’re keen to kickstart a career in data analytics, we recommend you enroll for the full Professional Certificate program and work through the courses in order. Within just a few months, you’ll have job-ready skills and practical experience on your resume that will catch the eye of an employer!

Course Syllabus

Module 1: From Problem to Approach and From Requirements to Collection

Course Introduction
Data Science Methodology Overview
Business Understanding
Analytic Approach
Data Requirements
Data Collection

Module 2: From Understanding to Preparation and From Modeling to Evaluation

Data Understanding
Data Preparation-Concepts
Data Preparation-Case Study
Modeling-Concepts
Modeling-Case Study
Evaluation

Module 3: From Deployment to Feedback and Final Evaluation

Deployment
Feedback
Storytelling
Course Summary

Module 4: Final Project and Assessment

Introduction to CRISP-DM

What You'll Learn

Discuss what a data science methodology is and the importance of a structured approach for data scientists.
List the major steps involved in tackling a data science problem, ensuring a systematic approach.
Determine appropriate data sources for data science analysis, ensuring relevance and quality.
Describe the six stages in the Cross-Industry Process of Data Mining (CRISP-DM) methodology, using a case study to demonstrate each stage.
Apply the CRISP-DM methodology to analyze a case study, showing how each stage contributes to problem-solving.
Evaluate which analytical model is appropriate based on the case study requirements.
Demonstrate an understanding of data science methodology by applying it to a problem, using hands-on examples and practical applications.

Recommended Skills Prior to Taking this Course

To get the most out of this course, you need to have basic computer skills, foundational mathematics and statistics, and familiarity with spreadsheets.

Methodology for Data Science

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