Summer 2022 Schedule
We offer workshops from our own team, as well as our colleagues in Research Computing. Workshops are listed below, grouped by series. Click on the date link to register; registration is free. Workshops (except where noted) are offered via Zoom.
You may find it more convenient to view a list of our workshops in date order.
Be sure to look at the data workshops being offered by our colleagues in the Health Sciences Library.
Check back Fall 2022 for workshops on High Performance Computing, Information and Publishing, Qualitative Research, R, Python, Reproducibility, and Tableau.
|Workshop Topic (Instructor)||Day (Click date to register)||Time||Location|
|Intro to Python for Scientists (Katherine Holcomb)||Tues, June 7 and Weds, June 8||9:00am-5:00pm (both days)||Online|
|This two-day short course is an introduction to programming in Python. No prior programming experience is required. Participants will gain skills in using the most popular packages such as matplotlib and Pandas. Programming with Jupyter notebooks and text-based scripts will be covered.
|High Performance Python (Katherine Holcomb)||Thurs, June 9||9:00am – 12:00pm||Online|
|Like most interpreted languages, Python can be very slow.This workshop will show attendees tips and tricks for improving the performance of their Python scripts.
Prerequisites: Proficiency in Python. Experience with Rivanna desirable but not required.
|Machine Learning with Python (Jacalyn Huband, Alois D’Uston de Villereglan, Gladys Andino)||Thurs, June 9||1:00 – 5:00pm||Online|
|This workshop will provide an overview of Machine Learning techniques using Python. From Random Forest to PyTorch, you will receive an introduction to the topics and sample codes that you can use to test out the techniques.
Prerequisites: Proficiency in Python; some experience with Rivanna.
|Deep Learning Matlab (Ed Hall)||Fri, June 10||Noon – 2:00pm||Online|
|This worshop will provide a comprehensive introduction to deep learning using Matlab. Attendees will learn how to create, train, and evaluate different kinds of deep neural networks using Rivanna GPUs to accelerate network training.
Prerequisites: Experience with Matlab using Rivanna