Resources - sparklabnyc/resources GitHub Wiki

This page contains helpful information and recommended resources for learning
Use these guides, courses, and readings to strengthen your work in R, Python, academic writing, and other related skills
If you’ve found a helpful article, website, video, or online course that other lab members could benefit from, feel free to share it on this page.

Reading

Epidemiology and Biostatistics

Best N, Richardson S, Thomson A. 2005. A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research 14:35–59. doi:10.1191/0962280205sm388oa
Elliott P, Wakefield J, Best N, Briggs D. 2001b. Spatial epidemiology: Methods and applications. Oxford University Press.

Statistical

McElreath R. 2020. Statistical Rethinking, 2nd ed. CRC Press.
Gelman A, Vehtari, A et al. 2020. Bayesian workflow

Statistics and machine learning

Programming and data analysis

Coding

Learn the command line.
Learn git.
Learn what a code formatter is and what a linter is. Use them (ideally in pre-commit. Learn what precommit is.).
Calmcode has video tutorials for modern ideas and open source tools.

R

R for Data Science great for beginner introduction to R
Video on the basics of R usage: Learning R in 39 Minutes
Learn Tidyverse (the basic fundamental library for R data science). Check out more on Tidyverse on their website
CS50's Introduction to programming with R
Reproducible R

Python

Python Data Science Handbook - covers basic Python, NumPy, Pandas, Matplotlib, and beginner Machine Learning.
Github's Python Resources/Python for Everybody (PY4E)
CS50's Introduction to programming with Python

Research methods

An Introduction to Statistical Learning
Any stats textbook by Andy Field/Discovr
Practical Statistics for Data Scientists
The Craft of Research
Understanding Research Methods(Coursera)
The Turing Way (I Highly Recommend This for Beginners)

Academic writing

Recommended software