GenAI for Data & Analytics

This course helps professionals harness generative AI to transform data analytics into a value-driven capability. Learn to integrate people, processes, and technology to turn insights into impactful, adaptive business decisions.

Interested in this course? Email us at [email protected]

Course curriculum

1. Segment 00 - Welcome to the Course: Course Overview
2. Segment 01 - Intro Video to Course
3. Segment 02 - Chapter Introduction
4. Segment 03 - Introduction to GenAI for Analytics
5. Segment 04 - Understanding Analytics Workflows
6. Segment 05 - Demo – Introducing the software tools used in the course
1. Segment 06 - Why Prompt Design Matters
2. Segment 07 - Demo – ChatGPT for Problem Framing
3. Segment 08 - Demo – Gemini for Code Generation
1. Segment 09 - LLM Modes – Exploration, Validation, Execution, Communication
2. Segment 10 - Demo – Setting Up Gemini in Colab
3. Segment 11 - Module Wrap-Up – LLMs as Analyst Thinking Partners
4. Segment 12 - Reading: A Primer on Generative AI
5. Segment 13 - Hands-on-Lab: From Business Problem → Prompt → First SQL + Python Insights
6. Segment 14 - Quiz - Tools and Setup for Analyst Workflows
1. Segment 15 - Chapter Introduction
2. Segment 16 - How LLMs Support Business and Data Understanding
3. Segment 17 - Demo – ChatGPT for Exploring Data based on Domain Knowledge
4. Segment 18 - Demo – Gemini for Structuring Analytics Opportunities based on EDA results combined with Domain Knowledge
1. Segment 19 - Linking Business Goals to Data and Models
2. Segment 20 - Demo – ChatGPT for Prompt Iteration
3. Segment 21 - Demo – Gemini for Generating SQL queries and Python code to discover and visualize patterns in the data
1. Segment 22 - Profiling and Exploring Data with AI
2. Segment 23 - Demo – Gemini in Colab for Data Summaries
3. Segment 24 - Demo – Gemini in Colab for Communicating Narratives
4. Segment 25 - Reading: Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM
5. Segment 26 - Hands-on-Lab: Staff Utilization vs Margin Analysis
6. Segment 27 - Quiz - Generating Actionable Narratives from Analytical Findings
1. Segment 28 - Chapter Introduction
2. Segment 29 - Principles of Modeling in Analytics Workflows
3. Segment 30 - Demo – Gemini in Colab for Predictive Modeling
4. Segment 31 - Demo – ChatGPT for Model Interpretation
1. Segment 32 - Accuracy, Explainability, and Trust in GenAI Outputs
2. Segment 33 - Demo – Gemini for Model Validation
3. Segment 34 - Demo – ChatGPT for Critiquing Outputs
1. Segment 35 - From Models to Insights – The Analyst’s Role
2. Segment 36 - Demo – Google Colab for Automated Reports
3. Segment 37 - Demo – ChatGPT and Google Colab for Stakeholder Communication
4. Segment 38 - The Future Of Data Storytelling Is Augmented, Not Automated
5. Segment 39 - Hands-on-Lab: Build a Baseline Model, Validate the Story, and Produce a Mini Insight Report
6. Segment 40 - Quiz - Generating Actionable Insights
1. Segment 41 - Chapter Introduction
2. Segment 42 - Real-World Applications of GenAI in Analytics
3. Segment 43 - Demo – Gemini Industry Case Simulation
4. Segment 44 - Demo – ChatGPT as an Analyst Advisor
1. Segment 45 - Segment 45 - GenAI Governance Across CRISP-DM
2. Segment 46 - Demo – Gemini for Audit and Controls
3. Segment 47 - Demo – ChatGPT for Auditing Prompts & Outputs
1. Segment 48 - Embedding GenAI Across CRISP-DM
2. Segment 49 - Demo – End-to-End Workflow (Gemini)
3. Segment 50 - Module Wrap-Up: Best Practices for Scaling and Future Readines
4. Segment 51 - Reading: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
5. Segment 52 - Hands-on-Lab: Apply GenAI Tools End-to-End: Execute, Govern, and Critique
6. Segment 53 - Course Wrap-up Video
7. Segment 54 - Quiz - Integrating GenAI into the Modern Analytics Stack
8. Segment 55 - Project: Driving Successful Data Analytics with Generative AI

About this course

Free
56 lessons
4.5 hours of video content

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GenAI for Data & Analytics

Course curriculum

Chapter 1: Understand how GenAI transforms data analytics workflows

Chapter 2: Prompting Techniques to Generate SQL Queries and Python Code

Chapter 3: Tools and Setup for Analyst Workflows

Chapter 4: Automate EDA Tasks using GenAI to Accelerate Insight Discovery

Chapter 5: Extracting Meaningful Patterns and Insights from Data

Chapter 6: Generating Actionable Narratives from Analytical Findings

Chapter 7: Modeling with GenAI

Chapter 8: Evaluating Models and Outputs

Chapter 9: Generating Actionable Insights

Chapter 10: Applying GenAI in Industry Use Cases

Chapter 11: Implementing Governance Practices Enhanced by GenAI

Chapter 12: Integrating GenAI into the Modern Analytics Stack

About this course

Discover your potential, starting today