A repository with tips about code review and implementing it in a lab

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.

Fluency in programming and data science requires using computer software from the Command Line, a text-based way of controlling the computer. You will go on a guided under-the-hood tour behind the graphical interface we typically use: you will learn how to interact and manipulate files, folders, and software via the Command Line.

This set of four mini courses helps leaders make strategic decisions, drive innovation, enhance efficiency, and foster a culture that embraces the transformative power of these technologies.

An introduction to using Cromwell and WDL at the Fred Hutch

The course covers fundamentals of R, a high-level programming language, and use it to wrangle data for analysis and visualization. The programming skills you will learn are transferable to learn more about R independently and other high-level languages such as Python.

You will learn how to use Git, a version control system that is the primary means of doing reproducible and collaborative research. You will use Git from the command line to document the history of your code, create different versions of your code, and share your code with an audience via GitHub!

The course continues building programming fundamentals in R programming and data analysis. You will learn how to make use of complex data structures, use custom functions built by other R users, creating your own functions, and how to iterate repeated tasks that scales naturally.

A DaSL training "snack" covering the benefits and basics of using Git and GitHub to support your biomedical data science work.

This course introduces users to the Fred Hutch Cluster. It will lead users through account creation, using the terminal, connecting to the cluster, submitting jobs, and transferring files. Available in both web and Leanpub formats.

"Developing WDL Workflows" shows a bioinformatics workflow developer how to strategically develop and scale up a WDL workflow that is iterative, reproducible, and efficient in terms of time and resource used. This guide is flexible regardless of where the data is, what computing resources are being used, and what software is being used.

The course covers fundamentals of Python, a high-level programming language, and use it to wrangle data for analysis and visualization.

Learn about the new NIH data sharing policy, places where you might want to share your particular kind of data, and how to deal with possible challenges associated with the policy.

You will expand your current knowledge of Git and GitHub to help your research be more collaborative, reproducible, and transparent.