All the Resources!

Reading about good code in general

• For tips for writing good code and creating reproducible projects: Reproducibility in Cancer Informatics
• For tips for writing pull requests: Engaging in Code Review as an Author by Candace Savonen.
• For tips for reviewing pull requests: Engaging in Code Review as an Reviewer by Candace Savonen.
• For more on effective code review: A zen manifesto for effective code reviews by Jean-Charles Fabre.
• About Good scientific coding practices by the Childhood Cancer Data Lab.
• Tips for debugging code from the Childhood Cancer Data Lab
• For good practices on code review: Best practices for Code Review from SmartBear.

Training Networks

These places have workshops, online and in person groups, and/or tutorials that can help you get plugged into training opportunities and connect others who are learning informatics.

• ITCR Training Network - hosts events, workshops and creates content for cancer informatics.
• The Carpentries, with lessons from Data Carpentry and Software Carpentry
• Childhood Cancer Data Lab - more specific to childhood cancer research. Has great workshops, materials, and software tools.
• Gene Pattern - has an online ‘point and click’ website with a large library of tutorials to reference. They also host workshops.
• Galaxy Training Network - has an online server you can work from and lots of tutorials for different data types.
• Cold Spring Harbor offers a variety of courses for genomics.
• If you specifically work on Childhood Cancer Research, we recommend getting plugged in with the Childhood Cancer Data Lab. They have workshops and their own Slack tailored to the specifics of childhood cancer data science research.

Groups for discussing code

These are completely free groups that you can join anytime that are also filled with people also tackling coding and informatics research.

• R Ladies: A group that has local meetups to discuss R and R topics. (Not exclusive to women-identifying individuals). See here for the R Ladies Seattle.
• Fred Hutch Slack R User Group: Discusses programming, data analysis, and troubleshooting in R for users of a wide range of expertise and research topic, as well as tools based in R such as Shiny applications. See the #r-user-comm channel on FH-BCR Slack for more information. Currently an online Slack channel only.
• Fred Hutch Slack Python User Group: Discusses programming, data analysis, and troubleshooting in Python and related topics in general software development. See the #python-user-comm channel on FH-BCR Slack for more information. Currently an online Slack channel only.
• The Childhood Cancer Data Lab has their own Slack tailored to the specifics of childhood cancer data science research.

Free online resources

There are a lot of resources online that you can work through at your own pace. Most are completely free. If they are not free, this is noted in the list.

R and Tidyverse

• Swirl, an interactive tutorial
  
• R for Data Science
  
• Tidyverse skills for Data Science by Carrie Wright.
• Handy R cheatsheets
  
• R Cookbook Second Edition
• Advanced R
• R for Epidemiology - has generally good R advice
• O’Reilly books available through Seattle Public Library

R notebooks

• R Markdown
  
• Tutorial on R, RStudio and R Markdown
• Handy R cheatsheets
  
• R Notebooks tutorial

R and Genomics

• Intro to R and Tidyverse course and exercises from the Childhood Cancer Data Lab.
• Refine.bio examples from the Childhood Cancer Data Lab.
• Biostar Handbook: A Beginner’s Guide to Bioinformatics

Python

• Python Data Science Handbook
• Python for Biologists

Learn on your own courses

If you want to learn on your own, but would like a more formal course set up and schedule, these courses can offer you more structure as well as certification.

ITN Courses and course material

See the full listing of ITN courses here.

• Introduction to Reproducibility in Cancer Informatics - a hands-on course that takes you through good practices for creating reproducible data analyses. Can be be followed along using R or Python.
  • The material for this course can be viewed without login requirement on this Bookdown website. This format might be most appropriate for you if you rely on screen-reader technology.
  • This course can be taken for free certification through Leanpub.
  • This course can be taken on Coursera for certification here (but it is not available for free on Coursera).
• Advanced Reproducibility for Cancer Informatics - the sequel course to the Intro to Reproducibility course.
  • The material for this course can be viewed without login requirement on this Bookdown website. This format might be most appropriate for you if you rely on screen-reader technology.
  • This course can be taken for free certification through Leanpub.
  • This course can be taken on Coursera for certification here (but it is not available for free on Coursera).
• Documentation and Usability in Cancer Informatics - Discusses best practices for creating usable software and thorough documentation.
  • The material for this course can be viewed without login requirement on this Bookdown website. This format might be most appropriate for you if you rely on screen-reader technology.
  • This course can be taken for free certification through Leanpub.
  • This course can be taken on Coursera for certification here (but it is not available for free on Coursera).

Other online courses

• MCB517A: Tools for Computational Biology: A graduate-level course taught for UW by Fred Hutch CompBio faculty. This links to a GitHub repository that includes all lectures and homework.
  • How to install necessary software for this course
  • Ask questions about this course
  • Course materials all available for free
• edX: Offers a collection of courses for Data Analysis and Statistics and Bioinformatics
  • Rafael Irizarry of Dana Farber has online programs available through edX:
    • Introduction to Data Science
    • Data Analysis for Life Sciences
    • Data Analysis for Genomics
  • Generally speaking, edX courses are all free to audit for a limited period of time. Unlimited access and the ability to earn a course Certificate will require payment
• Coursera: Offers a collection of courses for Data Science and Bioinformatics
  • R Programming: A beginner-level program has five mini-courses. It takes about 4 months to complete.
  • Statistics with R: A beginner-level program with five mini-courses. It takes about 7 months to complete.
  • Genomic Data Science: An intermediate-level program for those who are already acquainted with R. It has eight mini-courses. It takes about 6 months to complete.
  • Python Programming: An intermediate-level program that takes about 4 months to complete.
  • Coursera offers a 7-day free trial, and is a paid subscription service after
• Udacity: Offers a collection of courses for Data Science
  • Udacity is a paid subscription service
  • Currently offering one month free for their Nanodegree programs.
• Udemy: Offers a collection of courses for Data Science
  • Udemy offers courses at various price points.
  • Keep an eye out for sales which happen regularly and can drastically reduce the cost.
• CognitiveClass.ai Offers a collection of courses for data science, AI, and cloud computing.
  • All courses are free
• The Open Source Data Science Masters: An open-source curriculum for learning data science. This is a mixed media course made up of videos, books, and slides.
  • Some content is free, some is paid
• CalTech Learning from Data
  • A free YouTube series
• DataQuest: A subscription service that offers programs and courses focused on data analysis and engineering in Python and R.
  • Tiered payment system with basic and premium plans
• CodeAcademy: A subscription service that offers coding programs and courses in many different languages.
  • Tiered payment system with limited content available for free

* Completely overwhelmed? Don’t know where to start? Fill out this form and let us know what’s going on.

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