Statistics in Data Science: A Practical and Intuitive Introduction

Learn statistics the way data scientists actually use it — through intuition, interpretation, real-world examples, and practical Python applications.

Perfect for students, analysts, aspiring data scientists, and professionals who want to build statistical intuition without getting lost in mathematical formalism.

Included with the Book

• 340+ pages of explanations, examples, and exercises
• Companion GitHub repository
• Python and R Jupyter notebooks
• Practical exercises and solutions
• Intuition-focused explanations throughout

Topics Covered

• Descriptive statistics and data visualization
• Probability and uncertainty
• Important probability distributions
• Sampling and the Central Limit Theorem
• Confidence intervals and hypothesis testing
• Type I and Type II errors
• Statistical power and effect size
• Correlation and regression
• Classification and model evaluation
• ANOVA and practical data science concepts
• Monte Carlo simulation (Appendix)

About the book

Statistics is one of the most important foundations of data science, yet many learners struggle because concepts are often introduced through formulas before intuition.

This book takes a different approach.

Statistics in Data Science is an intuition-first introduction designed to help students, analysts, aspiring data scientists, and professionals develop a practical understanding of statistical thinking without getting lost in excessive mathematical formalism.

Through clear explanations, real-world examples, visual illustrations, and hands-on Python and R applications, readers learn not only how statistical methods work, but why they matter and how to interpret their results in practice.

Who This Book Is For

This book is ideal for:

• Students studying statistics or data science
• Aspiring data scientists and analysts
• Professionals seeking a stronger statistical foundation
• Educators looking for intuitive teaching materials
• Anyone who wants to understand data more confidently

Author: Monireh Rezai Rad, Ph.D. in Mathematics