Description
Practical Statistics for Data Scientists (2nd Edition) is an essential guide for understanding the statistical foundations behind real-world data science applications. Written by Peter Bruce, Andrew Bruce, and Peter Gedeck, this book bridges the gap between academic statistics and practical data science workflows.
The second edition expands and updates coverage with both R and Python code examples, making it easier for readers to apply statistical techniques directly in their projects. Topics include data exploration, probability, estimation, regression, classification, dimension reduction, clustering, sampling, and machine learning essentials.
With its concise explanations, visual examples, and hands-on approach, this book is perfect for data analysts, machine learning engineers, statisticians, business intelligence professionals, and students seeking a strong foundation in applied statistics.
⭐ Key Features:
-
Covers core statistics for data science with practical examples
-
Includes R and Python code snippets
-
Updated and expanded 2nd edition with modern techniques
-
Covers regression, classification, resampling, and model evaluation
-
Clear explanations suitable for beginners and intermediate learners
-
Real-world applications for data analysis and predictive modeling
-
Learn how statistics power machine learning algorithms
-
Ideal for self-study and professional use
