Last Updated on November 23, 2023
16. Data Analysis for the Life Sciences by Rafael A. Irizarry & Michael I. Love
Data Analysis for the Life Sciences covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data.
The authors include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. The book was created using the R markdown language
The book has a suggested price of $39.95 but there’s no minimum price.
17. The Undergraduate Guide to R by Trevor Martin
The Undergraduate Guide to R is a beginner’s introduction to R. This manual is designed so that no prior knowledge of programming is required or assumed but you’ll need basic general computer skills and statistics knowledge.
It teaches you how to install and run R, learn enough R to be able to perform most common data manipulating, analyzing, comparing, and viewing tasks. It gives a solid grounding together with tips and suggestions how to program in R.
The book is released under an open source license.
18. R Programming for Data Science by Roger D. Peng
This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code.
With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
The book has a suggested price of $20, but you can download it for free.
19. Statistical Inference via Data Science by Chester Ismay & Albert Y. Kim
Statistical Inference via Data Science is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would.
This is very much an introductory text. No algebra, no calculus, and no prior programming/coding experience is required.
There’s a GitHub repository page available at https://github.com/moderndive/moderndive_book.
20. Introduction to Probability and Statistics Using R by G. Jay Kerns
Introduction to Probability and Statistics Using R is a textbook written for an undergraduate course in probability and statistics. It’s currently in its third edition.
You’ll need two or three semesters of calculus under your belt, as well as some knowledge of linear algebra.
The book’s home page is at https://www.nongnu.org/ipsur/.
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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|The first high-level language, using the first compiler
|Compiled, statically typed programming language
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|Unique features - excellent to study programming constructs
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|Designed as an embeddable scripting language
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|Ideal for systems, embedded, and other performance critical code
|Modern, object-functional, multi-paradigm, Java-based language
|A general-purpose, functional language descended from Lisp and Algol
|Visual programming language designed for 8-16 year-old children
|Access and manipulate data held in a relational database management system
|General-purpose functional language characterized as "Lisp with types"
|Powerful and intuitive general-purpose programming language
|Dynamic language based on concepts of Lisp, C, and Unix shells
|Markup and programming language - create professional quality typeset text
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