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GenAI for Data & Analytics

Starweaver via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Generative AI is reshaping data analytics, enabling organizations to move beyond traditional reporting toward faster, more accurate, and actionable business insights. This hands-on course explores how ChatGPT, Google Gemini, Python, and Google Colab can be integrated into the CRISP-DM framework to improve 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, the course demonstrates how large language models (LLMs) accelerate analytics workflows while supporting responsible AI adoption and measurable business outcomes. Through practical demonstrations, hands-on exercises, and real-world business scenarios, you will learn how to apply prompt engineering, AI-assisted data analysis, Python for data analytics, and data storytelling to solve business problems and communicate insights effectively. You will also explore how Generative AI enhances collaboration across technical and business teams, improves productivity, and strengthens data-driven decision-making. By the end of the course, you will be equipped to use AI-powered analytics tools to transform data into meaningful insights that create lasting business 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.