Python

Maddie Taylor

Data Science for Social Justice Fellow 2024
Environmental Science, Policy and Management

Maddie is a third year Ph.D. Student in Environmental Science, Policy, & Management. Their research brings together disability studies and environmental justice to explore how disabled and chronically ill communities are affected by climate change.

They graduated from Barnard College in 2017, and their experiences navigating ableism while working in the environmental field led them to their current research interest at UC Berkeley. In their free time they enjoy knitting, playing music, and exploring the Bay Area with their dog, Winnie.

Python Fundamentals: Parts 1-4

October 11, 2022, 3:00pm
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.

Python Machine Learning Fundamentals: Parts 1-2

October 2, 2023, 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.

Python Data Wrangling and Manipulation with Pandas

November 15, 2021, 2:00pm
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.

Python Fundamentals: Parts 1-4

January 10, 2023, 10:00am
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.

Python Intermediate: Parts 1-3

February 12, 2024, 9:00am
This three-part interactive workshop series teaches you intermediate programming Python for people with previous programming experience equivalent to our Python Fundamentals workshop. 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.

Python Fundamentals: Parts 1-3

April 29, 2024, 2:00pm
This three-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.

Python Text Analysis: Word Embeddings

April 6, 2022, 3:00pm
How can we use neural networks to create meaningful representations of words? The bag-of-words is limited in its ability to characterize text, because it does not utilize word context.

Python Data Wrangling and Manipulation with Pandas

August 17, 2023, 2:00pm
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.

Python Data Wrangling and Manipulation with Pandas

September 28, 2022, 3:00pm
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.