Data Science

Stephanie Andrews

Consultant
Info & Data Science MIDS

Stephanie Andrews is currently studying data science in the MIDS program, having previously majored in Social Welfare as an undergraduate at Cal. After graduating, she worked as an advocate for survivors of gender-based violence, as a public policy analyst focusing on anti-trafficking initiatives, and as a software engineer for progressive and social impact organizations. She is now conducting research with the Human Rights Center's Investigations Lab, using OSINT and data science methods to investigate human rights violations.

Yue Lin

Data Science Fellow 2024-2025
Political Science

Yue is a Ph.D. student in Political Science at the University of California, Berkeley, with a Designated Emphasis on Political Economy. Using mixed methods, she studies foreign lobbying, geopolitical risk, and economic security to understand when, how, and why multinational corporations become the targets and weapons of state power rivalry.

Amanda Glazer

Instructor
Statistics

Amanda is a PhD candidate in the statistics department at Berkeley. Her research focuses on causal inference with applications in education, political science and sports. Previously she earned her Bachelor’s degree in mathematics and statistics, with a secondary in computer science, from Harvard.

Chirag Manghani

Consultant
School of Information

Chirag is a 2nd year graduate at the I-School. Proficient in Python, Java, R, and SQL, he navigates software application development, machine learning and data science. His keen interest lies in data analysis and statistical methods, driving him to bridge theory and practice seamlessly. Chirag's dedication to excellence, adaptable mindset, and innate curiosity define him as a dynamic problem solver in the ever-evolving tech landscape.

Data for a Just U.S. - Using Data Science to Empower Marginalized Communities

September 3, 2024
by Elijah Mercer. In this blog post, I share how working with marginalized communities through data science has transformed my understanding of the field. My journey from crime analysis to founding Data for Just US reveals the profound impact data can have when used to empower and uplift underserved populations. I explore the challenges and rewards of this work, illustrating how data science can drive social change and foster a more equitable future.

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.

AI Ethics in Action: UC Berkeley’s Data Science for Social Justice Workshop

August 28, 2024, 5:00pm
Claudia von Vacano, Ph.D., Founding Executive Director of D-Lab, introduces the Data Science for Social Justice Workshop, highlighting its goals, structure, and outcomes. Three students who have participated in the workshop present lightning talks on their experience with DSSJ, highlighting their personal journeys, the projects they worked on, and what they gained from the workshop.

R Copilot Assisted Coding Workshop

September 23, 2024, 3:00pm
This workshop provides a beginner-friendly introduction to coding with GitHub Copilot, a popular AI coding assistant. We will start from the basics so you can take advantage of AI assistants to improve your coding and avoid common pitfalls. First, we’ll cover how to install and set-up Visual Studio Code, a free code editor through which we will use GitHub Copilot. Then, we will go through the different features of GitHub Copilot and how to use them to help us code in R.

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.

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.