Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

About the Book – Practical Statistics for Data Scientists

Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck is a comprehensive guide to understanding and applying essential statistical concepts in data science. The book bridges the gap between statistical theory and practical applications through clear explanations, real-world examples, and hands-on techniques using R and Python.

Covering more than 50 important statistical concepts, it explores exploratory data analysis, sampling, statistical experiments, regression, classification, machine learning, and data-driven decision-making.

Designed for both beginners and experienced professionals, this book helps readers develop analytical thinking, interpret complex datasets, avoid common statistical mistakes, and make more informed decisions.

An excellent resource for data scientists, business analysts, programmers, researchers, and students looking to strengthen their statistical and analytical skills.

About the Authors – Peter Bruce, Andrew Bruce & Peter Gedeck

Peter Bruce is the founder of the Institute for Statistics Education at Statistics.com and an experienced author and educator specialising in statistics, analytics, and data science.

Andrew Bruce has over 30 years of experience in statistics and data science across academia, government, and business. He holds a Ph.D. in Statistics from the University of Washington and has published numerous research papers and developed statistical solutions for various industries.

Peter Gedeck is an experienced data scientist and scientific computing specialist. He previously worked as a computational chemist at Novartis and specialises in developing machine learning algorithms for scientific research and drug discovery. He holds a Ph.D. in Chemistry and has also studied Mathematics.

Together, the authors bring extensive academic, research, and industry expertise to the fields of statistics, data analytics, and machine learning, making their work valuable for students and practising data science professionals.