"Although many of the techniques are relevant to molecular bioinformatics, the motivation for the text is much broader, focusing on topics and techniques that are applicable to a range of scientific endeavors."

Clearly written guide to using R for data science, visualization, tidying data, and more, by the author of "tidyverse" package. Highly recommended!

"For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. "

"Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. "

An extensive guide to R code for a wide range of statistical topics.

This book is written for S but can be used for R with minimal modifications. Available as an ebook or physical copy. "A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets."

"Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos."

"This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic."

"This Handbook summarizes, explains, and demonstrates the nature of current models, methods, and techniques particularly designed for the analysis of spatial data. The book is designed to be a desk reference for all researchers just getting into the field of spatial data analysis as well as for seasoned spatial analysts. "