Get started coding in Python in this 2-part workshop series taught by UC Davis graduate students with support from DataLab and the Davis Python Users Group on 10/23 and 10/24 from 5-8pm!

Registration link.

Description

Many graduate programs and research projects require proficiency in coding and working with data. This 2-session intensive workshop is intended to introduce graduate students who have little or no computational background to the open-source Python programming language. This series is organized and instructed by graduate students, with support from the Davis Python Users Group and UC Davis DataLab. This is a great opportunity to learn a new skill and meet other members of the graduate community! Each session will begin with demonstrations on fundamental Python topics, followed by Q&A and open practice where learners can work on challenge assignments together and ask questions to the volunteer instructors. Each challenge assignment builds on the previous sessions, so by the end of the series learners will have a complete Python project for their portfolios. This workshop is open to all UC Davis graduate students and postdoctoral scholars. Attendance at both sessions is required. Instruction is in-person and seats are limited. A Zoom link (e.g., broadcast) will be available for those who are offsite and would like to watch the demonstrations.

After this workshop, learners will be able to:

– Use basic Python programming syntax;

– Use packages such as NumPy and Pandas;

– Use visualization tools;

– Write reusable functions;

– Identify where to go to learn more!

Prerequisites

This introductory series is intended for graduate students and postdoctoral researchers and does not require prior programming experience. Participants will need to bring a laptop on which they can install software and access the UC Davis wireless network (eduroam).

Software

No specialized software is required.

Instructors

Maggie Berrens, Frank Cerasoli, Heejune Park, Panyue Wang

Instructor Bios

Maggie Berrens is a physical chemistry Ph.D. candidate, working in Davide Donadio’s lab. She received a B.S. from University of Puget Sound in mathematics and chemistry. Her research involves using computational tools to investigate the effect of adsorbed species on the structure and dynamics of ice surface and ice surface’s effect on photodegradation of pollutants in clouds and snow-pack.

Frank Cerasoli is a Postdoc working in Davide Donadio’s lab. He received his Ph.D. in Computational Materials Science, and M.S. in Physics from University of North Texas and a B.S. in Physics from The University of Texas at Austin.

Heejune Park is a physical chemistry Ph.D. candidate, working in Lee-Ping Wang’s lab. He received his M.S. in physical chemistry from the University of San Francisco and his B.S. in chemistry and physics from California State University, Chico. He is currently working on developing software tools, geomeTRIC and QCFractal, for studying molecular structures.

Can’t make it to this training? Check out upcoming workshop schedule. Recordings of prior workshops are also available in DataLab’s training archive.