ITCR Training Network Courses

Goal: To catalyze cancer informatics research through training opportunities. Go to itcrtraining.org for more information.

GitHub Automation for Scientists

This course walks through why’s and the how’s for using automation to boost scientific software development process. It’s meant for folks who already have a basic familiarity with GitHub but would like to automate more of their software dev work.

Containers for Scientists

Currently under development

This course walks through why’s and the how’s for using containers (e.g. Docker) to boost reproducibility of scientific analysis.

AI for Decision Makers

This course is designed for leaders and decision makers to have the high level overview of AI: it’s ethical implications, technical aspects to consider and more.

Documentation and Usability in Cancer Informatics

A course to cover the basics of creating documentation and tutorials to maximize the usability of informatics tools.

Introduction to Reproducibility in Cancer Informatics

Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an “ease into it” approach; teaching “better practices”.

Advanced Reproducibility in Cancer Informatics

To equip learners with a deeper knowledge of the capabilities of reproducibility tools and how they can apply to their existing analyses scripts and projects.

Choosing Genomics Tools

To equip learners with the basic understanding and resources to handle their genomic data. The course is intended for students in the biomedical sciences and researchers who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

Looking for collaborators who’d like to write about their data type specialty See here for more info.

DataTrail Curriculum

Goal: DataTrail aims to equip individuals with the tools they need to enter the booming field of data science.

Alex’s Lemonade Stand Foundation Courses

Goal: To equip pediatric researchers with bioinformatics skills to fuel their research. See the Childhood Cancer Data Lab’s website for more information.