Overview

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Generative AI is transforming modern data analytics, enabling organizations to move beyond traditional reporting toward faster, more accurate, and actionable business insights. This hands-on course, GenAI for Data & Analytics explores how leading AI tools for data analytics, including ChatGPT, Google Gemini, Python, and Google Colab, can be integrated into the CRISP-DM framework to enhance every stage of the analytics lifecycle - from business understanding and data preparation to predictive modeling, visualization, and stakeholder communication. Designed for data analysts, business analysts, BI developers, data scientists, and decision-makers, this AI for Data Analytics course demonstrates how large language models (LLMs) accelerate analytics workflows, improve productivity, and support responsible AI adoption while delivering measurable business outcomes. Through practical demonstrations, hands-on exercises, and real-world business scenarios, you will learn how to use AI for data analytics by applying prompt engineering, AI-assisted data analysis, Python for data analytics, and data storytelling to solve complex business problems and communicate insights effectively. You'll discover how using Generative AI for data analytics strengthens collaboration between technical and business teams, streamlines decision-making, and enables more efficient, data-driven strategies. By the end of the course, you'll be equipped to confidently use AI-powered analytics tools to transform data into meaningful insights, support business decisions, and create lasting organizational value.

Syllabus

Foundations of GenAI in Data Analytics
- This module introduces and describes AI compared with GenAI. It explains how LLMs support analysts across the CRISP-DM framework, how prompt engineering helps clarify problems, guide analysis and generate code to carry out the analysis and publish results. Learners will explore tools (ChatGPT, Gemini in Colab) and see how to use them as thinking partners, code generators and workflow accelerators.
Automated Data Analysis and Insight Generation
- This module focuses on the early phases of CRISP-DM. Learners will see how LLMs (ChatGPT, Gemini) act as partners in clarifying business needs, reframing problems into analytics tasks, and exploring data to uncover opportunities.
Data Visualization and Communication with GenAI
- This module focuses on the modeling, evaluation, and insight-generation phases of CRISP-DM. Learners will use LLMs not only to generate models and outputs but also to interpret, refine, and communicate insights through narratives, reports, and visualizations.
Real-World Applications and Advanced Techniques
- This module focuses on the later phases of CRISP-DM framework. Learners will explore real-world applications of GenAI, practice evaluating outputs for trust and governance, and learn how to integrate LLMs into analytics workflows responsibly and effectively.