**Book Title:** Practical Statistics for Data Scientists: 50 Essential Concepts

**Publisher:** O'Reilly Media

**ISBN:** 1491952962

**Author:** Peter Bruce

*** You need to enable Javascript in order to proceed through the registration flow.**

**Book Title:** Practical Statistics for Data Scientists: 50 Essential Concepts

**Publisher:** O'Reilly Media

**ISBN:** 1491952962

**Author:** Peter Bruce

Primary: Practical Statistics for Data Scientists: 50 Essential Concepts.pdf - 38,452 KB/Sec

Mirror [#1]: Practical Statistics for Data Scientists: 50 Essential Concepts.pdf - 36,209 KB/Sec

Mirror [#2]: Practical Statistics for Data Scientists: 50 Essential Concepts.pdf - 35,433 KB/Sec

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Data Science from Scratch: First Principles with Python
- Python Data Science Handbook: Essential Tools for Working with Data
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Think Stats: Exploratory Data Analysis
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Innumeracy: Mathematical Illiteracy and Its Consequences
- Text Mining with R: A Tidy Approach
- Doing Data Science: Straight Talk from the Frontline
- Introduction to Machine Learning with Python: A Guide for Data Scientists

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data