放在最前面的链接：My opinionated selection of books/urls for bioinformatics/data science curriculum

此文已经过原作者同意转载（ 我原封不动的转了~转了~ ），我就不翻译了，欢迎转发大佬博客给想要学习的人。

• 哈哈，真的是我膜拜的对象大佬。
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一些简介

• Bioinformatics scientist at Harvard FAS informatics working on single-cell RNAseq and single-cell ATAC. Care about reproducible research and open science.

• Harvard FAS informatics

• Cambridge, MA

• 邮箱：tangming2005@gmail.com （大佬人真的很好，不过不要遇到啥问题就发邮件请教）

• 博客的自我简介

• 博客链接

所推荐的重点内容：

There was a paper on this topic: A New Online Computational Biology Curriculum.
I am going to provide a biased list below (I have read most of the books if not all). I say it is biased because you will see many books of R are from Hadely Wickham. I now use tidyverse most of the time.

Unix

I suggest people who want to learn bioinformatics starting to learn unix commands first. It is so powerful and also omnipresent in high-performance computing settings (clouding computing etc). You can not survive without knowing it.

• The linux command line
• How Linux works
• Data science at the command line It was a fun reading for me and learned many tricks from this book.
• command line bootcamp interactive online session to learn unix. it is not working anymore unfortunately.

Computational biology

• A Primer for Computational Biology by Shawn T. O’Neil
• Practical computing for biologist by Steven H.D Haddock and Casey W. Dunn This was the first book that I used to learn unix, regex and python.
• Bioinformatics data skills by Vince Buffalo. This is a must have! once you have some experience on bioinformatics.

R programming

• R for data science by Garrett Grolemund and Hadley Wickham.
• Advanced R by Hadley Wickham.
• R packages by Hadley Wickham. If you want to transit from an R user to developer, writing an R package will get you started.

Stats (R focused)

• Data analysis for the life science with R by Micheal Love and Rafael A. Irizarry. I took the course on edx for 3 times! learned a ton! You can buy a paper book at https://www.crcpress.com/Data-Analysis-for-the-Life-Sciences-with-R/Irizarry-Love/p/book/9781498775670
• Computational Genomics with R by Altuna Akalin.
• Mordern statistics for mordern biology by Susan Holmes and Wolfgang Huber.

Python programming

• (Advanced) python for biologist
• Python for data analysis
• Data science from scratch

Machine learning

• An intro to statistical learning
• Practical Data science with R
• Deeping learning with R

Visualization

• Fundamentals of Data Visualization by Claus O.Wilke

• The Visual Display of Quantitative Information by Edward R. Tufte as well.

Those two books are not teaching you how to make figures programmatically (although the book by Claus was generated by Rmarkdown and the codes for all the figures can be found at https://github.com/clauswilke/dataviz). They teach you what kind of figures are informative and pleasant to eyes. From data to viz is a website guiding you to choose the right graph for your data.

I am still using R/ggplot2 for visualization.

• Data Visualization by Kieran Healy.
• R Graphics Cookbook by Winston Chang.
• ggplot2: Elegant Graphics for Data Analysis by Hadely Wickham.

Finally, I have compiled many useful links at https://github.com/crazyhottommy/getting-started-with-genomics-tools-and-resources .