Practical Statistics for Data Scientists. 2nd Ed. Peter Bruce, Andrew Bruce, Peter Gedeck (english)

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.\n\nMany 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.\n\nWith this book, you'll learn:\n\nWhy exploratory data analysis is a key preliminary step in data science\nHow random sampling can reduce bias and yield a higher-quality dataset, even with big data\nHow the principles of experimental design yield definitive answers to questions\nHow to use regression to estimate outcomes and detect anomalies\nKey classification techniques for predicting which categories a record belongs to\nStatistical machine learning methods that "learn" from data\nUnsupervised learning methods for extracting meaning from unlabeled data