Description
Bridge the critical gap between traditional statistical training and modern data science with Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition. Authored by Peter Bruce, Andrew Bruce, and Peter Gedeck, this indispensable guide is designed specifically for data scientists who need to understand the practical applications of statistics without getting bogged down in overly academic mathematics.
The 2nd Edition has been significantly updated to include comprehensive, step-by-step code examples in Python, standing right alongside the original R snippets. It covers over 50 essential concepts, from exploratory data analysis and random sampling to regression, classification, and machine learning techniques. Whether parsing complex server load datasets for Cloud VPS Hosts or modeling predictive customer behavior for an e-commerce platform, applying the right statistical methods is essential for extracting actionable, accurate insights. This book tells you exactly what is important, what is not, and how to avoid the common pitfalls that can derail your data projects.
Key Features:
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Dual Language Support: Features complete, side-by-side code examples in both Python and R, the two most dominant languages in data science.
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Core Concepts: Clearly explains over 50 foundational statistical methods, including A/B testing, resampling, and statistical machine learning.
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Practical Focus: Strips away the dense mathematical proofs in favor of practical, real-world application and code-driven solutions.
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Avoiding Pitfalls: Provides expert guidance on recognizing and avoiding the common misuse of statistics in modern data analysis.
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Target Audience: An essential desk reference for working data scientists, software developers transitioning into data roles, and data analysts.
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