Git or Github

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...

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.

Grazia Rovelli, Ph.D.

Data Science Fellow
Chemical Science Division (LBL)

Grazia is a postdoctoral scholar at the Chemical Science Division at Berkeley Lab and a Data Science Fellow at D-Lab. Her research has focused on several different aspects of atmospheric chemistry and she is now interested in data science and machine learning tools applied to atmospheric pollution problems.

Why Teaching Social Scientists How To Code Like A Professional Is Important

September 23, 2020
Jae Yeon Kim, Ph.D.

I use data science to study political learning, organization, and mobilization among marginalized populations. I have always loved programming and want to serve people lacking voice and representation in a society. I am blessed to have found and chosen computational social science—a field situated between social science and data science—as my main research area.

I also love teaching people how to code, especially social scientists, and I take that mission seriously. I have taught computational tools and techniques at both graduate and undergraduate levels in semester-...

Manuscript Workflow with R Markdown and GIT

March 16, 2021
Lawrence Y. Tello

As part of my Masters of Public Health program I needed to complete a capstone. Working on a manuscript is a lot of back and forth: You need to make edits, fix your words and figures, and sometimes re-work entire sections. If you are like me, the thought of doing this process over a long period of time in Word makes me nauseous. Two main issues that cause this nausea for me are:

I frequently forget to make a record of my writing and often overwrite work

Copying and pasting figures while arguing with Word’s formatting...

Brooks Jessup, Ph.D.

Data Science Fellow
History

Brooks received his Ph.D. in History from UC Berkeley and was trained in Data Science at General Assembly. His work applies digital tools and methods to the study of modern cities and urban issues. At D-Lab, he teaches and consults on data analytics, machine learning, geospatial analysis, and natural language processing with Python and SQL.

Organized Code Repositories Accelerate Science and Facilitate Reproducubility

March 2, 2021
Pratik Sachdeva, Ph.D.

Computational and data-driven research increasingly requires developing complex codebases. At the same time, many scientists don’t receive training in software engineering practices, resulting in, for some, the perception that scientists write terrible software. As scientists, good software should accelerate our work and facilitate its reproducibility. While building good coding practices takes some time and experience, it doesn’t require a...