Training Resources for Data Science
In-Person Training Opportunities Overview
Workshops from the ITCR Training Network (ITN), a cancer informatics training initiative
The ITN will host workshops at the Hutch on April 29th in partnership with the Fred Hutch Data Science Lab!
Free lunch to those who register and participate in one or more workshops at noon!
Location: Weintraub Building B1-072/074
Data Management & Sharing Plans for NIH Proposals (10:30 AM - 12:00 PM)
- NIH data sharing and management policy background and data sharing tips
- Tools and resources to help you write your own data management and sharing plan
AI Considerations for Research (12:30 PM - 2:00 PM)
- Introductory fundamentals about how AI works and different kinds of AI
- Research and day-to-day task possibilities with AI
- Strategies and solutions for integrating AI solutions into a project
- Upcoming regulations
Write Smarter with LaTeX & Overleaf (2:15 PM - 3:30 PM)
- Learn about the power of LaTeX and Overleaf for creating easily reformatted scientific articles
- Get started with a template for arXiv - a repository for preprints
- Please join us for lunch (on us!) from 12-12:30 if you attend one (or more) of our workshops! (registration required)
Register here: https://forms.gle/XDSrcmv4aDcGwpza6
Season 3: Spring 2024
| Beginner | Intermediate | Advanced | |
|---|---|---|---|
| Programming | Intro to R | Intermediate R | |
| Rigorous Science | Introduction to Git and GitHub | ||
| Scalable Computing | Introduction to Command Line | Cluster 101 |
Self-service Training Materials Overview
| Course Name | Description | Link(s) |
|---|---|---|
| Cluster 101 Guide | Intro to using the Fred Hutch HPC cluster for new or experienced users. Provides a certification option. | With Certification or Without Certification |
| WDL 101 | Intro to using Cromwell to run WDL workflows at the Fred Hutch | Course link |
| Code Review | Leading a lab with novice or experienced code writers and users? Either way, our Code Review guidance materials include helpful suggestions for various types of lab members, expertise and group dynamics. | Course Link |
| NIH Data Sharing | We have created and are actively developing a guide that walks you through the process of complying with the new 2023 NIH Data Sharing Policy. | Guide Link |
Course Descriptions and Details
Introduction to R
You will learn the fundamentals of R, a statistical 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. At the end of the class, you will be reproducing analysis from a scientific publication!
Targeted audience: Researchers who want to do more with their data analyses and visualizations. This course is appropriate for those who want to learn coding for the first time, or have explored programming and want to focus on fundamentals in R.
Commitment: 6 weekly 1.5 hour classes, with encouraged 1-2 hours of practice weekly.
Course dates: Noon - 1:30pm PT on April 17, 24, May 1, 8, 15, 22. Register here.
Intermediate R
You will continue to learn the fundamentals of R and programming, and work on a data science project from start to finish. You will learn how to load and clean messy data, use custom R packages and functions, and effectively scale up your analysis.
Targeted audience: This course is appropriate for those who understand the basics of R data analysis and want to expand their knowledge to tackle messy data and use custom tools.
Pre-requisites: Completion of Intro to R, or knowledge of subsetting vectors and dataframes, and performing simple analysis such as summarizing data.
Commitment: 6 weekly 1.5 hour classes, with encouraged 1-2 hours of practice weekly.
Course dates: Noon - 1:30pm PT on April 15, 22, 29, May 6, 13, 20. Register here.
Introduction to Command Line
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.
Targeted audience: Researchers who want to use scientific software launched from the command line, want to use a high-performance cluster computing environment, or want to use a cloud computing environment.
Commitment: A 1.5 hour workshop.
Workshop date: Noon - 1:30pm PT on April 16. Register here.
Cluster 101
Many scientific computing tasks cannot be done locally on a personal computer due to constraints in computation, data, and memory. In this workshop, you will learn how to connect to the Fred Hutch SLURM high performance cluster to transfer files, load scientific software, compute interactively, and launch jobs!
Targeted audience: Researchers who want to use Fred Hutch’s SLURM high performance cluster to run software and analysis at scale.
Pre-requisites: Completion of Intro to Command Line workshop or demonstrating competency.
Commitment: A 1.5 hour workshop.
Workshop date: Noon - 1:30pm PT on April 30. Register here.
Introduction to Git and GitHub
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 collaborate with others on your code using GitHub!
Targeted audience: Researchers who want to keep track the history of their code at a professional standard, and share it with an audience.
Prerequisites: Completion of Intro to Command Line or demonstrating competency.
Commitment: A 1.5 hour workshop.
Workshop date: Noon - 1:30pm PT on May 14. Register here.
Cluster 101 Guide
We collaborated with SciComp and created a course called Cluster 101, to introduce new and experienced high performance computing users to the Fred Hutch cluster. This course can be taken anytime, anywhere, for free (don’t worry just set the Leanpub cost to zero) and it’ll help you check to make sure your account is set up to use the cluster and either help you check to make sure you understand the basics of how to use a cluster, or help you get up to speed. It can even give you a certification for reporting between staff and lab leaders if need be.
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If you need/want to get a certification, please take the course through Leanpub at this link.
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If you do not need the certification or want to bookmark the course for future reference, you can find the material at this link.
If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository.
WDL 101
Our next emerging guide is WDL 101: Using Cromwell at the Fred Hutch which will help users leverage our preconfigured software to allow you to run and manage all of your computing jobs, as well as run workflows written in WDL (a widely used open specification for workflow description), even if they use Docker containers! This is an excellent tool for researchers who do not use large scale computing resources often or who would want a method to simply upload a file and use a website to submit jobs to the cluster. Also, for researchers who do leverage amount of cluster resources, this approach can help you make your computing more efficient, help you to scale up your pleasantly parallel work and shift to using containers and structuring tasks appropriately to facilitate moving to the cloud when needed. This guide is under active development as of Nov 2022.
If you take this guide and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the guide’s GitHub repository.
Code Review
Leading a lab with novice or experienced code writers and users? Either way, see our Code Review materials that include helpful suggestions for various types of lab members, expertise and group dynamics.
If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can file an issue on the course’s GitHub repository.
NIH Data Sharing
We have created and are actively developing a guide you can find here that walks you through the process of complying with the new 2023 NIH Data Sharing Policy. We have also created the DMS Helper App to make filling in and downloading your data sharing plan easier. >If you take this course and want to give us feedback or would like to learn more about it, you can share your thoughts in Slack in the #ask-dasl channel) or you can fill out our Google Feedback Form.
Full List of Self-Service Training Resources
FH DaSL staff have developed many training resources as part of collaborations and efforts. Various resources spanning a wide range of data science and tool-specific topics that we have previously developed are available from the sources listed below.
- For the full list of DaSL supported resources go here.
- For a longer list of recommended resources (not just ones supported by us, go here).
(in alphabetical order)
AnVIL
AnVIL is a computing platform that enables researchers to analyze controlled access data sets in a cloud computing environment. It has loads of training materials to support those using it!
Code Review Guidance for Research Labs
Leading a lab with novice or experienced code writers and users? Either way, see our Code Review materials that include helpful suggestions for various types of lab members, expertise and group dynamics.
DataTrail
The DataTrail courses are free and designed to help those with less familiarity with computers and technology become savvy data scientists. It includes the technological data science fundamentals but also information on how to network and other accompanying and necessary skills for jobs in data science.
ITCR Training Network
The ITCR Training Network is an effort to catalyze cancer informatics research through training opportunities. It has online courses that are available for free and/or for certification, but also hosts synchronous training events and workshops related to data science in cancer research. Links to all the current ITCR courses can be found here
Johns Hopkins Data Science Courses
There are a lot of helpful resources for data science that we made as a part of Johns Hopkins. These courses cover various applications and tools of data science, mostly focused on using R and the Tidyverse.
Open Case studies
The Open Case Studies project can be used by educators and learners alike to help people learn how apply data science to real-life data.