GenAI for Data & Analytics Professionals

Name: GenAI for Data & Analytics Professionals
Rating: 4.9 (8 reviews)

Harnessing advanced capabilities to enhance the flow of value from data to insights, decisions, and business impact.

Role Play

New

Created byStarweaver Experts, Paul Siegel, Mark Peco

Last updated 7/2026

English

What you'll learn

Differentiate the building blocks of AI, GenAI, and Data Analytics, and describe how LLMs support the CRISP-DM and Value Chain frameworks.
Apply prompt engineering techniques to frame business problems for data analytics by using tools like ChatGPT, Gemini in Colab, for solutions.
Create and Communicate actionable insights to generate narratives, reports, and visualizations for analytical findings and their business impact to stakeholders
Evaluate data, models, and GenAI outputs for accuracy and clarity, to critique, validate, and refine insights across CRISP-DM Phases and Value Chains.

Course content

12 sections • 51 lectures • 4h 29m total length

Course Welcome and Goals2:56
Welcome learners to the course. This video sets the stage by introducing the instructor, outlining the unique focus of the course. Learners will discover what problems this course will help them solve and what tools they will use. A call to action, encouraging learners to focus, and engage with the activities forms the conclusion.
Section Introduction4:09
Introduces CRISP-DM, Gnerative AI and explains the analyst role.
Introduction to GenAI for Analytics6:53
Explains distinctions between traditional AI, deterministic code, and generative AI.
Understanding Analytics Workflows7:01
Describes CRISP-DM phases and how analysts guide business understanding, data understanding, and beyond. This forms a basis of Analytics Workflows.
Demo – Introducing the software tools used in the course6:50
Shows how GenAI using LLMs can provide examples, suggestions, and support for selected tasks including code generation.

LLM Modes – Exploration, Validation, Execution, Communication6:57
Explains how LLMs can support analysts in different ways beyond code generation.
Demo – Setting Up Gemini in Colab11:13
Walkthrough of how to access and use the tools that will be applied throughout the course.
Section Wrap-Up – LLMs as Analyst Thinking Partners5:26
Recaps Section 1 and emphasizes LLMs as collaborators in problem framing and early CRISP-DM stages.
A Primer on Generative AI0:18
Hands on Learning: From Business Problem → Prompt → First SQL + Python Insight0:06
Getting Started with GenAI: A Conversation with Sam
Tools and Setup for Analyst Workflows

Section Introduction2:33
The GenAI tools course environment.
How LLMs Support Business and Data Understanding6:47
Explains how GenAI helps identify where analytics can add value within a business process from exploring data in combination with domain knowledge.
Demo – ChatGPT for Exploring Data based on Domain Knowledge6:01
Uses ChatGPT to explore, summarize and visualize data based on a business scenario.
Demo – Gemini structures analytics from EDA and domain knowledge8:36
Shows Gemini in Colab turning a business scenario into a structured analytics workflow including feature engineering and predictive modeling.

Profiling and Exploring Data with AI7:28
Explains how GenAI helps with summarization, anomaly detection, and exploration of data quality.
Demo – Gemini in Colab for Data Summaries6:41
Uses Gemini to generate descriptive statistics, text summaries, and quick visuals.
Demo – Gemini in Colab for Communicating Narraitives7:37
Applies Gemini to generate meaningful narratives from the statistics and summaries.
Realising the Promises of Artificial Intelligence in Manufacturing by Enhancing0:21
Hands on Learning: Staff Utilization vs Margin Analysis0:05
From Vague Question to Data Insight: A Working Session with Maya
Generating Actionable Narratives from Analytical Findings

Section Introduction2:05
Overview of Modeling Techniques for Data Analytics.
Principles of Modeling in Analytics Workflows5:52
Explains basic modeling concepts and the analyst’s role in guiding visualization style selection and use.
Demo – Gemini in Colab for Predictive Modeling7:43
Uses Gemini to generate and explain Python code for a predictive model and to visualize the results.
Demo – ChatGPT for Model Interpretation8:04
Demonstrates how ChatGPT helps interpret model outputs and explain them in plain business language.

Accuracy, Explainability, and Trust in GenAI Outputs6:08
Covers evaluation criteria for models and outputs, including clarity, fairness, and reliability.
Demo – Gemini for Model Validation7:52
Shows Gemini generating validation tests and metrics for a predictive model.
Demo – ChatGPT for Critiquing Outputs6:07
Uses ChatGPT to critique model outputs, reports, or visuals for clarity and business relevance.

From Models to Insights – The Analyst’s Role4:44
Explains how to move from technical results to business-ready insights and recommendations.
Demo – Google Colab for Automated Reports5:17
Uses Google Colab to create reports, charts, and narratives summarizing analysis results.
Demo – ChatGPT and Google Colab for Stakeholder Communication12:35
Demonstrates how ChatGPT and Google Colab helps craft concise, audience-appropriate narratives for decision makers.
The Future of Data Storytelling0:18
Hands on Learning: Build, Validate, and Report Model Insights0:06
Beyond the Accuracy Score: Preparing Your Findings for the Boardroom
Generating Actionable Insights

Section Introduction2:20
Introduction to the section, key topics to be covered, and call to action.
Real-World Applications of GenAI in Analytics5:27
Explains how industries apply GenAI to solve real problems (finance, healthcare, retail).
Demo – Gemini Industry Case Simulation9:22
Uses Gemini in Colab to simulate an analysis task (e.g., forecasting, patient data summarization).
Demo – ChatGPT as an Analyst Advisor10:35
Shows ChatGPT helping interpret results and generate recommendations in industry scenarios.

Requirements

Basic understanding of data analytics concepts, such as datasets, charts, reports, and data visualization. Familiarity with Microsoft Excel and an interest in solving business problems using data. Prior Python experience is helpful but not required. Access to a computer with an internet connection and free accounts for ChatGPT and Google Colab (Gemini-enabled) to complete the hands-on exercises.

Description

Data analytics is no longer just about creating reports and dashboards. Modern organizations expect professionals to uncover meaningful insights, predict trends, and communicate findings that drive better business decisions. This course, Gen AI for Data & Analytics Professionals, teaches you how to use Generative AI to transform traditional analytics workflows into faster, smarter, and more impactful processes.

Designed for business analysts, data analysts, BI developers, data scientists, and decision-makers, this hands-on course explores how AI-powered tools can support every stage of the analytics lifecycle. You will learn how to frame business problems, explore and prepare data, generate code, build analytical models, validate results, and present actionable insights using ChatGPT, Google Gemini, Python, and Microsoft Excel. Rather than replacing analytical thinking, these tools act as intelligent assistants that improve productivity, reduce repetitive work, and enable more strategic decision-making.

The course introduces industry-recognized analytics frameworks, including CRISP-DM and the Analytics Value Chain, to demonstrate how Generative AI enhances structured problem-solving and creates measurable business value. Through practical demonstrations, guided activities, and real-world business scenarios, you will gain experience using AI to automate routine tasks, improve collaboration, and communicate insights effectively to both technical and non-technical stakeholders.

By the end of the course, you will be able to integrate Generative AI into your analytics workflows, create meaningful data-driven insights, evaluate AI-generated outputs for quality and accuracy, and apply responsible AI practices. Whether you are looking to increase productivity, strengthen your analytical capabilities, or stay ahead in the rapidly evolving world of AI-powered analytics, this course provides practical, job-ready skills that you can apply immediately.

Who this course is for:

Designed for data analysts, business professionals, BI developers, data scientists, and decision-makers who want to apply Generative AI to improve analytics and business outcomes.

GenAI for Data & Analytics Professionals

What you'll learn

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Course content

Understanding how GenAI transforms data analytics workflows5 lectures • 28min

Prompting Techniques to Generate SQL Queries and Python Code3 lectures • 22min

Tools and Setup for Analyst Workflows6 lectures • 24min

Automate EDA Tasks using GenAI to Accelerate Insight Discovery4 lectures • 24min

Extracting Meaningful Patterns and Insights from Data Sets3 lectures • 19min

Generating Actionable Narratives from Analytical Findings6 lectures • 22min

Modeling with GenAI4 lectures • 24min

Evaluating Models and Outputs3 lectures • 20min

Generating Actionable Insights6 lectures • 23min

Applying GenAI in Industry Use Cases4 lectures • 28min

Requirements

Description

Who this course is for: