Practical statistics for data scientists : 50 essential concepts
Peter C. Bruce (Author), Andrew Bruce (Author)
"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"--Provided by publisher
eBook, English, 2017
First edition View all formats and editions
O'Reilly Media, Inc., Sebastopol, CA, 2017
1 online resource (298 pages) : illustrations
9781491952931, 9781491952917, 9781491952955, 1491952938, 1491952911, 1491952954
987251007
Exploratory data analysis
Data and sampling distributions
Statistical experiments and significance testing
Regression and prediction
Classification
Statistical machine learning
Unsupervised learning