This book provides a practical introduction to essential statistical concepts used in data science, combining theory with hands-on examples in R and Python. It covers key topics such as probability, regression, resampling, machine learning techniques, and data visualization. Designed for data scientists, analysts, and machine learning practitioners, the book bridges the gap between statistical theory and practical implementation.

Why Read This Book

• Covers over 50 essential statistical concepts needed for real-world data science applications.
• Includes practical code examples in both R and Python, making it accessible to a wide audience.
• Explains statistical methods in a clear and intuitive manner, avoiding excessive mathematical complexity.
• Provides guidance on best practices, common pitfalls, and how to interpret statistical results correctly.
• Useful for both beginners and experienced practitioners who want to solidify their statistical knowledge.

About the Authors

Peter Bruce is the founder of the Institute for Statistics Education at Statistics.com and has extensive experience in statistical training and data science education.Andrew Bruce is a data scientist and statistician with expertise in applying statistical methods to business and research problems.Peter Gedeck is an experienced data scientist specializing in machine learning, predictive modeling, and computational statistics.