Programming Languages

Christopher Paciorek, Ph.D.

Research Computing Consultant, Adjunct Professor
Department of Statistics
Research IT

Chris Paciorek is an adjunct professor in the Department of Statistics, as well as the Statistical Computing Consultant in the Department's Statistical Computing Facility (SCF) and in the Econometrics Laboratory (EML) of the Economics Department. He is also a user support consultant for Berkeley Research Computing. He teaches and presents workshops on statistical computing topics, with a focus on R.

Swetha Pola

Research Fellow
School of Information

Swetha (she/her) is a 5th Year Master of Information and Data Science student at the School of Information, with experience in Cognitive Science, Psychology research, and product management. Her research interests include building ethical, transparent AI and the impacts of technologies (specifically, mass media, surveillance, and algorithms of bias) on longitudinal behavioral health. She is happy to help with questions on Python, R, SQL, machine learning, neural networks, statistical analysis, and research design!

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Ash Tan

Consultant
School of Information

Ash is a Masters of Information and Data Science student at the Berkeley School of Information. He currently studies data collection, analysis, and visualization, as well as research design and machine learning techniques. His interests include cognitive science, Wikipedia data, and privacy research.

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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...

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-...

Projects as a Learning Tool

April 6, 2021
Emily Grabowski

Let’s say you’re new to programming, or maybe you’ve coded before but you’re tackling a new concept. You’ve read a blog post or taken a workshop, and have a general sense of what is going on. But how do you take this to the next level? One of my favorite ways to dive into a new technique is to simply try it out.

With coding, learning by doing is one of the best ways to improve. When I started learning Python, I took a class where I did homework assignments involving coding small games and algorithms. While these were useful for general coding, I wanted to dig in to the...

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...

Visuals for Everyone: An Exercise on the Importance of Intuitive Data Visualization

March 30, 2021
Daphne Yang

A couple years ago, I took an undergraduate biostatistics course here at UC Berkeley and vividly remember one of the first discussion section activities on interpreting data and visualizations. From this activity, I learned about why, as data consumers, we must always be aware of not only what visualizations are really representing but also understanding where the data is really coming from. While this might seem obvious, this has been one of the most valuable lessons as an aspiring data scientist/enthusiast. I learned the importance of analyzing and understanding data with...