Our Courses

About our Courses

Our courses are hosted online to enable learners to obtain certification when they successfully complete a course! Our courses are published on Leanpub and Coursera!

You can also find the material for our courses on the Bookdown websites. These are nice for referring back to the material. They also do not require making an account like Leanpub and Coursera.

Our courses are open source, so you can find all the source material on GitHub through our source material links above.

Courses are Pay What you Want on Leanpub (including Free!)

If you want to take a course for free on Leanpub, make sure you slide the blue bar to the left!

We give learners the option to support the authors.

Course Materials are Free on Bookdown

If you want to refer back to the content or just link to a particular chapter, this is the best option.

Course Materials are available on coursera

If you want a certificate and have a Coursera account, this is the best option for you. The Coursera interface is very polished and intuitive.

Course Materials are Developed on GitHuB

Check out our repositories if you want to see details about how we created our particular course, reuse our content (please give us attribution), or send us an issue or pull request. All content is licensed with CC-BY 4.0.

You can find all of our repositories here.

CurRent Courses

This course covers the pitfalls of informatics research and discusses best practices and tools to overcome the challenges of working with and managing multidisciplinary teams. It also covers guidelines to promote diversity and inclusion in your lab and research. Click the arrows that appear when you hover over the image to see more about the course.

This course covers the basics of creating documentation and tutorials to maximize the usability of informatics tools. It is meant for individuals developing tools for informatics. Click the arrows that appear when you hover over the image to see more about the course.

This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It is the first course in a two part course on reproducibility. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. Click the arrows that appear when you hover over the image to see more about the course.


This course introduces more advanced tools to increase the reproducibility of data analyses; building upon the Intro to Reproducibility course. GitHub, Docker, Code Review, and GitHub actions are discussed. Click the arrows that appear when you hover over the image to see more about the course.


This course is designed to help investigators understand more about computing basics, as well as familiarize researchers with various computing platform options. Click the arrows that appear when you hover over the image to see more about the course.


Future Course Materials!

Ethical Data Handling for Cancer Research

This course is designed to help researchers and investigators understand the key principles of data management from an ethics, privacy, security, usability and discoverability perspective.

Guide to Choosing Genomics Tools

Based on their genomic data types and goals, this course will help learners find educational resources and tools to help them process and interpret data.

Cancer Imaging Informatics

The course will show reproducible pipelines and analyses that can be done with medical imaging and pathological images, from raw data to statistical analysis.

Cancer Clinical Informatics

It can be difficult to organize and keep track of all the various clinical data that a researcher may collect or attempt to explore. Learning how to automate data extraction from clinical documents and how to summarize across documents can save valuable time and decrease the chance for errors when transcribing data from one format to another.

Cancer Informatics Data Visualization

This course will cover the key concepts behind data visualization including the grammar of graphics. It will also cover how to create effective visualizations for exploration, exposition, and validation. Finally it will cover how to use specific tools to make visualizations for omics, imaging, and clinical data.

Machine Learning for Cancer Informatics

As the size and scope of data sets dramatically increases the opportunity to use these data sets to develop prognostic and predictive algorithms in cancer research also grows. This course will cover the key concepts and applications of machine learning in cancer research.

Dissemination and Engagement

Even the most useful and best developed tools need to be put into the hands of relevant users to have an impact. There are a variety of modern approaches for disseminating cancer informatics software including Github, Social Media, workflow tools, and workflow publications. There is also a growing ecosystem for engaging users of software in everything from Github issues, to massive online open courses, to social media discussions. This course is designed to introduce effective ways of disseminating software and engaging with users to maximize impact.