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.
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.
This course on AI for software development explores the use of AI large language models (ChatGPT, Bard, etc) and their potential benefits and challenges. Hands-on activities show the ways in which AI can speed up software development tasks and free up time for more creative and strategic work, maximizing benefits/efficiency while limiting harm.
This course is designed to help investigators understand more about computing basics, as well as familiarize researchers with various computing platform options.
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.
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.
This course is designed to help researchers and trainees write scientific articles using LaTeX and Overleaf.
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.
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