Suggested Resources

Training & Learning Skills

Basic

SQL

• SQLBolt

  A series of interactive lessons and exercises designed to help you quickly learn SQL right in your browser.

• Best Practices in Working with Databases, by Posit

R

• R for Data Science (2e), by Hadley Wickham, Mine Cetinkaya-Rundel, Garrett Grolemund

  Learn how to get your data into R, get it into the most useful structure, transform it and visualize.

• Reproducible Medical Research with R by Peter Higgins

  For anyone in the medical field interested in analyzing the data available to them to better understand health, disease, or the delivery of care.

• Advanced R, by Hadley Wickham

  Primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help you to understand why R works the way it does.

• Modern Data Science with R, by Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton

  For readers who want to develop the appropriate skills to tackle complex data science projects and “think with data”

Python

• [Add more tools, and their links]

Julia

• [Add more tools, and their links]

GitHub

• Happy Git and GitHub for the useR

  The use of Git/GitHub in data science has a slightly different vibe from that of pure software development, due to differences in the user’s context and objective. Happy Git aims to complement existing, general Git resources by highlighting the most rewarding usage patterns for data science. This perspective on the Git landscape is presented in Basic Git Concepts and Daily Workflows.

Genomics

• Bioconductor
• [Add more tools, and their links]

AI

• [Add tools, and their links]

EHR

• OHDSI/OMOP
• Epic (likely restricted via institution)
• Cerner (restricted via institution?)

Training & Learning Platforms

• Coursera
• Data Carpentry
• AI Institute at South Carolina
• Data Camp

This is an example on GitHub of a shared R code and Jupyter notebook that student can download and run locally to learn how to preprocess and analyze microarray data, perform differential expression analysis, and generate visualization plots (https://github.com/ARINBRE/microarray)

Data Sources (Publicly Available)

Internal Temporary Notes

Examples of attributes to describe for each resource

• one-time cohort vs any-time/on-demand access.
• free vs paid.
• dates each resource was introduced and last updated.
• TRUST & FAIR Principles _suggested by Etienne Gnimpieba Z.