Leverage AI in Data Analytics: From Automation to Storytelling

Course details

Learn how AI is transforming the field of data analysis. Instructor Alex Freberg gets started by comparing traditional analysis methods with AI-augmented approaches, then dives into practical use cases such as automation, hypothesis testing, and code generation. Find out how to communicate AI-driven insights with confidence, using storytelling strategies to reach nontechnical stakeholders. Along the way, Alex covers the limitations of AI in real-world scenarios, including accuracy concerns and the need for human oversight. Finally, explore case studies from leading organizations such as Salesforce, Microsoft, and Walmart to see AI in action. Whether you’re an analyst looking to save time or a business leader curious to learn more about AI’s potential, this course equips you with the skills you need to leverage AI effectively and responsibly.

Learning objectives
Differentiate between traditional and AI-augmented analysis methods.
Identify and apply practical AI use cases in analytics workflows.
Communicate AI-driven insights effectively to technical and nontechnical audiences.
Recognize and mitigate limitations of AI, including hallucinations and biases.
Evaluate real-world case studies to see how leading organizations leverage AI for data analysis.

Concepts

Introduction

Traditional analytics vs. AI-powered analysis
Levels of AI in analytics - Assistance to automation
The benefits of AI in workflows

Identifying Use Cases for AI

Automating repetitive analytics tasks with AI
Hypothesis testing using AI and synthetic data
AI-driven code generation for data and analytics
AI as an idea partner - Using AI as a sounding board

Communicating AI-Driven Insights

Framing AI insights for non-technical stakeholders
AI-powered storytelling - Turning data into narratives
Building confidence when presenting AI-assisted findings

Limitations of AI for Real-World Use

AI accuracy vs. hallucinations - What analysts need to know
Data bias and blind spots in AI analytics
The role of human oversight in AI-driven analytics

Real-World Case Studies

Insurance company AI for dashboards
Giving managers AI to ask data directly
Synthetic data for privacy HIPAA