Intermediate Python: Next-level Data Visualization

May 4, 2023, 10 a.m. - May 4, 2023, noon

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DataLab: Data Science and Informatics

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DataLab Classroom - Shields 360

Description: Take your data visualization skills to the next level in Python! In this intermediate Python workshop we'll cover the fundamentals of Matplotlib, which is the foundation for most Python visualization packages. Then we'll discuss how to choose appropriate visualizations for your data and how to critically assess your visualizations for potential problems. We'll then put it all together to practice using Matplotlib to fix and improve visualizations made with Python packages including Plotnine, Seaborn, and Pandas.

This workshop is NOT an introduction to Python and is intended for motivated intermediate to advanced learners from all domains at UC Davis who want to hone their Python skills. Please make sure you meet the prerequisites before registering as we will be unable to answer introductory Python questions during this session. This workshop builds upon your existing knowledge of working in Python and upon the concept of tidy data. Learners should also attend the Principles of Data Visualization from Perception to Statistical Graphics workshop in advance of this session to ensure a solid foundation in design principles. (Want to brush up on Python? Check out our Python Basics 4-part introductory series.)

Learning Objectives

After completing this workshop, learners should be able to:

Prerequisites: Participants must have taken DataLab’s “Python Basics” workshop series and/or have prior experience using Python, be comfortable with basic Python syntax, and have it pre-installed and running on their laptops. Learners should also take the Principles of Data Visualization from Perception to Statistical Graphics workshop in advance of this session.

Software: A recent version of Python 3 and these packages:

Instructor: Nick Ulle

Instructor Bio: Nick Ulle is a statistician and computer scientist. Prior to DataLab he was a visiting assistant professor of Statistics at UC Berkeley, where he designed and taught courses in data science. During his PhD in Statistics at UC Davis, he developed source code analysis techniques for the R programming language. His research interests include statistical computing, programming languages, data visualization, and pedagogy.

Registration is closed for this event