Recommended Resources
Good introductions to coding for biologists:
Software Carpentry (intro to UNIX, Python, R, Git) https://software-carpentry.org/lessons/
Data Carpentry (genomic data wrangling) https://datacarpentry.org/lessons/
Overview of scientific computing with Python: https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-0-Scientific-Computing-with-Python.ipynb
Recommended Python Tutorials
A Byte of Python (great for beginners) https://python.swaroopch.com
Dive into Python (for those who know another language) https://diveintopython3.problemsolving.io
The Official Python Tutorial (late beginner/intermediate): https://docs.python.org/3/tutorial/index.html
Automate the Boring Stuff (do useful stuff with Python): https://automatetheboringstuff.com
A full Python course: https://www.python-course.eu/python3_interactive.php
Statistics resources:
Nature Methods Points of Significance series: https://www.nature.com/collections/qghhqm/pointsofsignificance
Each piece is a great 1.5 page introduction to a stats concept in a biological context. Start with the first articles in order, since they cover the basics. The series gets to many interesting topics like model selection, regularization, machine learning, etc.
Modern Statistics for Biologists, Susan Holmes and Wolfgang Huber (free online: https://web.stanford.edu/class/bios221/book/)
Outstanding, accessible but rigorous introduction to statistics and R. Covers basic statistics, but selected topics are most relevant for genetics/genomics/bioinformatics, anything related to sequence analysis.
Probability Theory: A Concise Course (Dover books), Y.A. Rozanov
A short, mathematical introduction to probability. Recommended by Rob Mitra for students who had probability/stats in college but need a refresher.
A First Course in Probability, Sheldon Ross.
This is a good undergraduate level introductory text that is very suitable for self-teaching.
Programming books:
Bioinformatics Data Skills, Vince Buffalo (available online through WUSTL library via O’Reilly Online Learning)
Basic skills for coding bioinformatics pipelines. Includes quick introductions to UNIX, Git, and R
O’Reilly Online Learning (free access for students through WUSTL library) https://learning.oreilly.com/home/
Access to O’Reilly’s very large collection of programming books. Includes relevant books like Mastering Python for Bioinformatics, Bioinformatics with Python Cookbook, R bioinformatics Cookbook, Numerical Python: Scientific Computing and Data Science Applications, Scientific Computing with Python.