Python Basics: Introduction to Programming for Researchers (4-part series)

Description

This 4-part workshop series (October 5, October 7, October 12, October 14, 12:00 PM – 2:00 PM) provides an introduction to using the Python programming language for reproducible data analysis and scientific computing. The focus of this workshop is programming basics and working with tabular data. Along the way, we'll discuss how to break down programming problems and organize computational code and projects for clarity and reproducibility.

Learning Objectives

After this workshop, learners will be able to load tabular datasets into Python, compute simple summaries and visualizations, perform common data-tidying tasks, write reusable functions, and identify where to go to learn more.

Prerequisites

No prior programming experience is necessary. All learners will need access to an internet-connected computer and the latest version of Anaconda and JupyterLab installed on their local machine prior to the start of the first workshop. Learners should commit to attending all four sessions of this series.

Software

Before the first workshop, learners must install the latest versions of the Anaconda and JupyterLab software. Follow the DataLab Python Install Guide at https://ucdavisdatalab.github.io/install_guides/python-and-python-tools.html#python-and-python-tools. If you need help with or want to check your installation, please stop by our drop-in office hours prior to the workshop.

Instructors: Nick Ulle and Arthur Koehl

Instructors’ Biographies

Nick Ulle is a statistician and computer scientist. He was previously a visiting assistant professor of Statistics at UC Berkeley, where he designed and taught courses in data science. During his graduate studies at UC Davis, he developed source code analysis techniques for the R programming language. He also received a BS in Mathematics from UC San Diego. His research interests include statistical computing, programming languages, data visualization, and pedagogy.

Arthur Koehl is a research data scientist. He graduated from UC Davis with degrees in history and economics and a minor in computer science. During his undergraduate studies, he worked for several years as a scientific computing intern at the Center for BioImaging Sciences at the National University of Singapore where he learned the basics of Linux system administration. His interests include natural language processing, computer vision, and web programming. At the DataLab he develops tools and provides technical expertise on interdisciplinary research projects in the humanities and social sciences.