Last Updated on November 23, 2023
26. Modern Statistics for Modern Biology by Susan Holmes & Wolfgang Huber
Modern Statistics for Modern Biology is targeted at biologists wanting to learn powerful methods of modern computational statistics.
It teaches you how to take raw data and turn it into informative and alluring output by learning how to write R scripts and to use advanced statistical packages from CRAN and Bioconductors.
The book covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo.
27. Report Writing for Data Science in R by Roger D. Peng
This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner.
Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them.
The book has a suggested price of $10 but can be downloaded for free.
28. An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
Other useful books include:
29. Introduction to Statistical Thinking (With R, Without Calculus) by Benjamin Yakir
Introduction to Statistical Thinking is designed for college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.
The first part of the book deals with descriptive statistics and provides probability concepts that are required for the interpretation of statistical inference. Statistical inference is the subject of the second part of the book.
Pages in this article:
Page 1 – R for Data Science and more books
Page 2 – R Graphics Cookbook and more books
Page 3 – Fundamentals of Data Visualization and more books
Page 4 – Data Analysis for the Life Sciences and more books
Page 5 – An Introduction To R and more books
Page 6 – Modern Statistics for Modern Biology and more books
Page 7 – A Little Book of R for Biomedical Statistics and more books
All books in this series:
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