Data Science

TEST: Python Data Wrangling and Manipulation with Pandas

August 22, 2024, 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.

TEST: R Fundamentals: Parts 1-4

August 20, 2024, 9:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

TEST: Python Fundamentals: Parts 1-3

August 19, 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.

Sahiba Chopra

Data Science Fellow 2024-2025
Haas

I'm a PhD student in the Management and Organizations (Macro) group at Berkeley Haas. I have a diverse professional background, primarily as a data scientist across numerous industries, including fintech, cleantech, and media. I hold a BA in Economics from the University of Maryland, an MS in Applied Economics from the University of San Francisco, and an MS in Business Administration from UC Berkeley.

My research focuses on the intersection of inequality, technology, and the labor market. I am particularly interested in understanding how to reduce inequality in...

Mingyu Yuan

Data Science for Social Justice Senior Fellow 2024
Linguistics

I am a Ph.D. candidate in Linguistics, with a focus on phonetics and phonology, specifically speech production in neuro-atypical populations. I use methods from Natural Language Processing in my day-to-day research.

Hellina Hailu Nigatu

Data Science for Social Justice Senior Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Violet Davis

Data Science for Social Justice Senior Fellow 2024
MIDS

I am a Masters student studying Data Science with the School of Information. My research involves computational social science projects focused on social justice and equity.

Minding the Gaps: Pay Equity in California

July 9, 2024
Tonya D. Lindsey, Ph.D.
by Tonya D. Lindsey, Ph.D. The gender pay gap continues to reflect that, on average, men outearn women. California is among the states with the smallest pay gaps (outpacing the national number at 13%) and is unique in that it enacted legislation aimed at eliminating pay gaps by sex and race categories. This blog post reflects on California’s pay gap as students study it in an undergraduate social statistics course. Independent variables indicate three theoretical frameworks: 1) human capital, 2) occupational segregation, and 3) discrimination. While the work students do is rigorous using a representative sample of full-time year-round California workers, there remains work to be done and caveats to the data and analyses.

Python Introduction to Machine Learning: Parts 1-2

December 7, 2021, 1:00pm
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.

Stata Fundamentals: Parts 1-3

August 14, 2023, 10:00am
This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.