Description
Understand the foundations of data science and modern AI tools with this fully updated beginner's guide
Whether you're a business analyst who's never coded, a manager who’s making data-driven decisions, or a career changer eyeing one of the fastest-growing professions, data science skills are vital to success. In the fourth edition of Data Science For Dummies, Lillian Pierson, a fractional CMO and GTM engineer who has trained over two million learners, delivers a hands-on introduction to the key concepts and tools such as AI-enabled shortcuts that make advanced analytics accessible to everyone.
This updated edition covers generative AI, prompt engineering, and predictive analytics right alongside evergreen fundamentals like Python programming, statistics, and data wrangling. Brand-new chapters explain practical applications using ChatGPT and Claude, while expanded no-code workflows make AI-powered analysis accessible to readers who have never written a single line of code.
Data Science For Dummies walks you through:
- Foundational skills in Python, statistics, and machine learning for beginners and career changers
- Hands-on generative AI coverage with practical demos using ChatGPT and Claude
- No-code workflows that show non-programmers how to perform AI-powered data analysis tasks
- Updated guidance on ethical AI practices, data regulations, and responsible compliance
- Career-focused advice on emerging roles, portfolio building, and industry preparation
Perfect for professionals, students, career changers, and business leaders looking for an accessible launching pad into an exciting and rewarding discipline, Data Science For Dummies gives you the foundational skills and modern AI tools you need to extract insights, create visualizations, and make smarter, data-driven decisions.
Table of Contents
Introduction 1
Part 1: 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
Part 2: 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
Part 3: 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
Part 4: 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
Part 5: 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
