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Mastering Generative AI for Data Analytics

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IntermediateCourse

Enhance your data analytics career by mastering generative AI for data analysis. Learn intermediate-level skills in artificial intelligence and data augmenting techniques. Explore querying databases to improve data insights. Unlock new potential in your analytics projects. Unlock Generative AI data analytics skills: prompting, visualization, storytelling, and querying. Explore concepts, industry uses, and ethics. Designed for analysts with Python & AI basics, complete a shareable project and earn an industry certificate.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Generative AI, Artificial Intelligence, Data Analysis, Data Augmenting, Querying Databases

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 13 Hours

Platform

  • edX

Last Update

  • March 20, 2025
About this Course
Generative AI for Data Analytics demonstrates the integration of Generative Artificial Intelligence (Generative AI) in Data Analytics. This course demonstrates how to use generative AI tools/frameworks/platforms to increase the effectiveness of various processes involved in the data analytics life cycle.  
You will learn how to use and apply generative models for tasks such as data generation, data augmentation, data preparation and finding insights, querying databases, generating visualizations, and storytelling. You will also explore challenges and ethical considerations associated with using Generative AI in data analytics.

Course Syllabus

Module 1: Data Analytics and Generative AI   
    • Module Introduction and Learning Objectives 
    • Video: Generative AI for Data Analytics  
    • Reading: Generative AI Tools for Data Analytics  
    • Video: Examples of Generative AI in Data Analytics  
    • Reading: Case Study on Successful Implementations of Generative AI in Data Analytics  
    • Lab: Explore a Simple Generative Tool 
    • Discussion Prompt: Discuss How Generative AI Can Be Used in Data Analytics
    • Reading: Leveraging Generative AI in Data Analytics Process  
    • Demo Video: Generative AI for Data Generation and Augmentation  
    • Lab: Generative AI for Data Generation and Augmentation  
    • Demo Video: Generative AI for Data Preparation  
    • Lab: Generative AI for Data Preparation  
    • Demo Video: Generative AI for Querying Databases  
    • Lab: Generative AI for Querying Databases  
    • Video: Generative AI for Q and A Model Insights  
    • Lab: Generative AI for Q and A Model Insights  
    • Video: Successful Implementations of Generative AI for Data Preparation and Insights   
    • Reading: Using Microsoft Copilot   
    • Module 1 Practice Quiz: Data Analytics and Generative AI 
    • Module 1 Summary: Data Analytics and Generative AI   
    • Module 1 Cheat Sheet: Data Analytics and Generative AI 
    • Module 1 Graded Quiz: Data Analytics and Generative AI  
Module 2: Use of Generative AI for Data Analytics   
    • Module Introduction and Learning Objectives 
    • Demo Video: Generative AI for Data Insights  
    • Lab: Generative AI for Data Insights  
    • Demo Video: Generative AI for Data Visualization  
    • Lab: Generative AI for Data Visualization  
    • Video: Generative AI for Creating Dashboards  
    • Lab: Generative AI for Creating Dashboards 
    • Video: Generative AI for Storytelling  
    • Lab: Generative AI for Storytelling  
    • Discussion Prompt: Dashboarding and Storytelling with Generative AI  
    • Video: Successful Implementations of Generative AI for Data Visualization and Storytelling  
    • Video: Considerations While Using Generative AI in Industries  
    • Video: Challenges While Using Generative AI  
    • Reading: Responsible Generative AI for Data Professionals 
    • Case Study: Considerations While Using Generative AI in Healthcare 
    • Lab: Considerations for Data Professionals Using Gen AI 
    • Module 2 Practice Quiz: Use of Generative AI for Data Analytics 
    • Module 2 Summary: Generative AI for Data Visualization and Storytelling  
    • Module 2 Cheat Sheet: Use of Generative AI for Data Analytics 
    • Module 2 Graded Quiz: Use of Generative AI for Data Analytics  
Final Project and Exam    
    • Reading: Guided Practice Project Overview  
    • Lab: Guided Practice Project 
    • Final Exam  
Course prerequisites:  
An introductory knowledge of data analytics, generative AI, and prompt engineering would be beneficial.  You can check these courses to get a basic understanding of these topics:

Learning Objectives

  • Describe how you can use Generative AI tools and techniques in the context of data analytics across industries
  • Implement various data analytic processes such as data preparation, analysis, visualization, and storytelling using Generative AI tools
  • Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 
  • Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Instructors

Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav 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 University of Western Ontario.

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Abhishek Gagneja

Freelance Subject Matter Expert

I am a lifelong learner with more than a decade of teaching experience at university level. I am an AI enthusiast and love learning new things.

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IBM Skills Network Team

Administrator

IBM Skills Network

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