Practical Statistics for Data Science Specialists

Statistical methods are a key part of data science. However, very few data analysts are properly trained in statistics, as there are no statistics books specifically written for data analysts.
On the other hand, many resources devoted to data science contain statistical methods but do not explore the application of these methods deeply enough.
The proposed book, written in an accessible language, fills this gap. If you are somewhat familiar with the R programming language and mathematical statistics, you will easily master the material and significantly enhance your professional level.
The second edition includes examples in Python, which expands the practical application of the book.
By reading this book, you will learn:
Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and lead to a higher quality data set, even in big data conditions
How the principles of experimental design help to obtain the most complete answers to questions
How to use regression to evaluate outcomes and identify anomalies