Master Statistics with Python—From Beginner to Advanced
This book is your comprehensive guide to understanding statistics through hands-on coding and real-world application. Designed for aspiring data scientists, this book takes you from foundational concepts to advanced techniques—all using Python.
Written by Professor Chris Kuo—an experienced educator and data science expert—this book brings together years of teaching insight into one cohesive, practical, and easy-to-follow guide. Inspired by countless “aha” moments in the classroom, it demystifies statistics with an approach that's clear, deep, and approachable.
The book contains three parts
Part I: Foundations of Statistics introduces key ideas like descriptive statistics, probability distributions, and multivariate relationships. Ideal for beginners, this section helps you build strong statistical intuition while learning to use Python.
Part II: Core Statistical Methods focuses on practical tools like sampling, confidence intervals, hypothesis testing, t-tests, chi-square tests, and ANOVA. You'll gain the skills to draw valid conclusions from data and design meaningful experiments.
Part III: Advanced Statistical Thinking explores A/B testing, nonparametric methods, bootstrapping, Bayesian inference, and Markov Chain Monte Carlo (MCMC). It concludes with linear regression and model evaluation—essential skills for predictive modeling and modern analytics.
Each chapter combines theory with practical Python code using NumPy, Pandas, Matplotlib, and Seaborn. Whether you’re a student, analyst, or professional making a pivot into data, this book helps you become statistically fluent and Python-proficient.
Why This Book Stands Out
This book follows the C.D.E. philosophy:
Clearer – Concepts explained in plain language with real-life relevance
Deeper – Goes beyond procedures to build your intuition and critical thinking
Easier – Carefully structured chapters with examples, visuals, and Python code to help you learn efficiently
Who This Book Is For
Students taking their first statistics or data science course
Self-taught learners eager to understand how data works
Professionals looking to upskill in data analysis and decision-making
Educators seeking a statistics textbook that integrates Python effectively
Whether you're aiming to pursue a data career or just want to become statistically literate in a data-driven world, this book equips you with the tools and confidence to analyze data with clarity and purpose.
Python in This Book
This book uses Python for its popularity in data science. After completing this statistics course with Python, students will encounter Python again in data science courses, where it has become a cornerstone tool. Installation instructions and GitHub notebooks are included.
From the Author
"Imagine sitting in an antique classroom with your modern laptop in front of you—a blend of tradition and innovation. That’s the atmosphere I hope to recreate in this book."
Chris Kuo, New York City
Learn with Professor Chris Kuo
Chris Kuo is a seasoned data science professional and adjunct professor with over 25 years of experience applying advanced analytics across multiple industries. He has led high-impact data science initiatives in customer analytics, healthcare, fraud detection, and litigation support, and is the inventor of a U.S. patent in data-driven solutions. Throughout his career, he has held leadership roles at several Fortune 500 companies in the insurance and retail sectors.
In addition to his industry work, Chris Kuo also teaches at Columbia University and has previously taught at Boston University, the University of New Hampshire, and Liberty University, covering subjects such as time series forecasting, mathematical finance, economics, and management. He holds a Ph.D. in Economics from the State University of New York at Stony Brook and a B.S. in Nuclear Engineering from National Tsing Hua University in Taiwan. He currently resides in New York City with his wife.