This workshop will introduce participants to incorporating data science into the undergraduate physics curriculum. The materials were developed by a dedicated team of postdocs and PhD students who were fellows of the Data Science Education Community of Practice (DSECOP). We will do a hands-on walk-through of getting started with Cloud-based tools such as Google Colab before leading an interactive session on two lessons that can be used in different physics courses. One lesson will focus on how Monte Carlo methods in physics naturally extend to machine learning algorithms such as deep neural networks using the Ising model as an example. The other lesson will provide an introduction to histograms as a tool for exploratory and more in-depth data analysis, providing instruction on both constructing and analyzing histograms. We will also have faculty who have taught using these tools provide their insights on what’s required for a successful experience. All of the materials are freely available. The modules are hosted at:https://github.com/GDS-Education-Community-of-Practice/DSECOP Please bring a laptop and make sure you have a Google account. This workshop is targeted toward those who have some knowledge of Python. The material was designed with postsecondary education physics faculty in mind. No prior experience in data science is required.

Organizers: Alexis Knaub, Casey Berger, Ashley Dale, Radha Mastandrea, Mohammad Soltanieh-ha, William Ratcliff