Introduction 1

Part1: Getting Started with Data Science in the Age of AI 7
CHAPTER 1: Doing Data Science in an AI World 9
CHAPTER 2: Understanding Data and Thinking Like an Analyst 23
CHAPTER 3: Getting and Cleaning Data without Coding 39
CHAPTER 4: AI Tool Literacy - When to Trust, When to Verify 59


Part2: Extracting Insights without Code 75
CHAPTER 5: Prompt Engineering for Analytics 77
CHAPTER 6: Exploring Your Data with AI 99
CHAPTER 7: Building Machine Learning Models without Code 117
CHAPTER 8: Understanding Essential Math and Statistics 137
CHAPTER 9: Clustering and Segmentation with AI 157
CHAPTER 10: Getting Predictive Analytics with Regression Analysis 173
CHAPTER 11: Building Dashboards with AI 193
CHAPTER 12: Data Storytelling and Visualization in the Age of AI 213


Part3: Driving Business Value with AI and Analytics 235
CHAPTER 13: Developing Your Business Acumen 237
CHAPTER 14: Improving Operations with Data and AI 251
CHAPTER 15: Making Marketing Improvements with AI 265
CHAPTER 16: Decision Support and BI Copilots 283
CHAPTER 17: Risk and Fraud Detection with AI 299


Part4: Implementing Data Science in Your Organization 311
CHAPTER 18: Gathering Important Information about Your Company 313
CHAPTER 19: Narrowing In on the Optimal Data Science Use Case 333
CHAPTER 20: Planning for Future Data Science Project Success 349
CHAPTER 21: Becoming an AI-Driven Data Professional 363


Part5: The Part of Tens 381
CHAPTER 22: Ten Prompts Every Analytics Power User Should Master 383
CHAPTER 23: Ten Reliable AI and Data Science Tools 393

Index 401