Practical Statistics for Data Scientists: 50 Essential Concepts

Statistical methods are a key part 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

• Naked Statistics: Stripping the Dread from the Data
• Doing Data Science: Straight Talk from the Frontline
• Hands-On Programming with R: Write Your Own Functions And Simulations
• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Python Data Science Handbook: Essential Tools for Working with Data
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Data Science from Scratch: First Principles with Python