Description
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd edition
Peter Bruce, Andrew Bruce and Peter Gedeck wrote Practical Statistics for Data Scientists for people who need statistics they can apply, not academic proofs. The second edition adds complete Python code examples alongside the original R code, covering over 50 concepts from exploratory data analysis and random sampling through regression, classification and machine learning techniques.
The book strips out dense mathematical proofs in favor of code-driven, real-world application, and spends time on the common ways statistics gets misused in data analysis so a reader can recognize and avoid the same mistakes.
Fifty concepts in two languages
Every concept gets side-by-side code in Python and R, the two most used languages in data science, including A/B testing, resampling and statistical machine learning.
Who the book is written for
The authors aim the book at working data scientists, software developers moving into data roles, and data analysts who want a desk reference rather than a math textbook.
Book details
- Title: Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Author: Peter Bruce, Andrew Bruce, Peter Gedeck
- Edition: 2nd edition
- ISBN-13: 9781492072942
- Genre: Data Science & Statistics
Questions buyers ask
Who is this book for?
Working data scientists, developers moving into data roles, and data analysts who want practical statistics rather than proofs.
Does it use Python or R?
Both. The second edition added complete Python code examples alongside the original R code.
How many concepts does it cover?
Over 50, from exploratory data analysis and random sampling through regression, classification and machine learning techniques.
Does it require heavy math?
No. It strips out dense mathematical proofs in favor of practical, code-driven explanations.
What is new in the 2nd edition?
Complete, side-by-side Python code examples were added next to the existing R code.
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