Python

Geospatial Data and Mapping in Python: Parts 1-2

October 5, 2021, 2:00pm
Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research.

Python Visualization

September 30, 2021, 10:00am
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.
See event details for participation information.

Python Fundamentals: Parts 1-4

October 4, 2021, 12:00pm
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.
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Priscila Amorim

Changemaker Technology Project
Data Science
Digital Health Social Justice

Priscila Amorim is a recent graduate of UC Berkeley's Bachelor's of Arts in Data Science program, and is currently attending Northwestern Univerisity for a Master's of Science in Data Science. Priscila is passionate about the intersection of technology and social justice, and in particular, health justice. Their goal is to work on climate justice through database management or data engineering to support data scientists and analysts in their work through the availability of ubiquitous data. Priscila is currently working on the Changemaker's Digital Health Project to help create...

Python Introduction to Machine Learning: Parts 1-2

September 27, 2021, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.
See event details for participation information.

Python Text Analysis Fundamentals: Parts 1-3

September 21, 2021, 10:00am
This three-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.

Chun Ho Chow

Data Science Fellow
City and Regional Planning
Civil and Environmental Engineering

I'm a dual-degree MCP City Planning / MS Transportation Engineering student. My background is in physics, and I'm interested in understanding and modelling urban and regional systems, including their morphology/form, interactions, and fundamental dynamics, using complex systems and computational methods. I'm also interested in the emergence and evolution of social complexity, urbanism, and regional networks of cities.

Daphne Yang

Data Science Fellow
School of Information

Daphne is a current 5th-year graduate student at the School of Information with a keen interest in the intersection between healthcare and data science. She has prior work experience in the realm of public health, consulting, and research. Currently, she is a data science research intern at a DC consumer experience startup. She is particularly interested in how data can be used to power insights and help move society towards a more equitable future.

Python Visualization

September 30, 2021, 10:00am
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.

Python Data Wrangling and Manipulation with Pandas

September 20, 2021, 1:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.