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
