Training Resources for Data Science

DaSL 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.

(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.

SciWiki

The Fred Hutch Biomedical Data Science Wiki is a resource we support that aims to create a community curated knowledge base for biomedical data science and research computing including guidance on policies, resources and tools supporting data intensive research by researchers both at the Fred Hutch and beyond.