## Book description

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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

## Publisher resources

## Table of contents

- Preface
- 1. Exploratory Data Analysis
- 2. Data and Sampling Distributions
- 3. Statistical Experiments and Significance Testing
- 4. Regression and Prediction
- 5. Classification
- 6. Statistical Machine Learning
- 7. Unsupervised Learning
- Bibliography
- Index

## Product information

- Title: Practical Statistics for Data Scientists, 2nd Edition
- Author(s):
- Release date: May 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492072942

## You might also like

book

### Data Science from Scratch, 2nd Edition

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …

book

### Practical Statistics for Data Scientists

Statistical methods are a key part of of data science, yet very few data scientists have …

book

### Fundamentals of Data Engineering

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and …

audiobook

### Fundamentals of Data Engineering

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and …