Most recent IDSC workshops (GitHub repositories):
Courses related to data science relevant to life sciences:
- BOT 4700/5700 Ecological Modelling.
Daniel Laughlin - BOT 4790/5790 Data Wrangling and Visualization in R
Lauren Shoemaker - COMP 3000 Basic Computing.
Ben Koger, unsure coding language(s) - ECOL 5500 Quantitative Analysis of Field Data.
Patrick Kelley, fall - ENR 4525/5525 Environmental data analysis.
Cliff Riebe, fall,
Unsure of what coding language used, focus is on statistical testing - ENR 4890-04 Data-driven storytelling (intro to data science with R)
Ellen Aikens, spring even years - ENR 5530 Data Viz in Environmental Science (GGplot with R)
Joe Holbrook - PHYS 3000 Methods of Physics.
Adam Myers, “A sophomore-level course aimed at physicists, but, honestly, any student could take it as a starter course in Python and NumPy. The examples just happen to be drawn from earlier physics classes.” - SOIL 4540/5540 Microbial Diversity and Ecology.
Linda van Diepen, fall - STATS 4880 Intro data wrangling in R.
Jared Studyvin - ZOO 4530/5530 Intro to R for wildlife ecology.
Jerod Merkle, fall - ZOO 4890-13 Essential Bioinformatics.
Ram Shukla - ZOO 5890 Animal movement & habitat selection modeling in R
Jerod Merkle, spring - ZOO 5890-08 Foundations of biological programming (Intro to Python / coding principles)
Ben Koger - Additionally, there are various training opportunities that are offered on a shorter
time scale through ARCC:
- https://arccwiki.atlassian.net/wiki/spaces/DOCUMENTAT/pages/2156527636/Workshops+and+Tutorials
- https://uwyo.libcal.com/ Filter by category "ARCC + DSC"
- Finally, other ideas can be found on the SoC suggested courses Many courses are cross-listed grad/undergrad courses across departments.
