Introduction to Data Analysis & Visualization in Python

BIDS Data Carpentry Workshop


June 13, 2018 to June 14, 2018
9:00am to 5:00pm
202 South Hall
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Full details, the schedule and syllabus, and a link to register are available at the workshop website.

Data Carpentry workshops help participants develop fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.  This course is aimed at graduate students, post docs, and other researchers, in particular those affiliated with the Algorithmic Fairness and Opacity Group at Berkeley. This workshop is open only to UC Berkeley affiliates. You don't need to have any previous knowledge of the tools that will be presented at the workshop. 

BIDS is co-hosting this event with the UC Berkeley Algorithmic Fairness and Opacity Group.

Instructors: Sarah Brown, Stuart Geiger, Geoffrey Boushey
Helpers: Josh Kroll, Camille Harris, Scott Peterson, Erin Becker, Jayashree Raman

Contact: Sarah Brown (, Daniel Kluttz (


R. Stuart Geiger

Ethnographer, Berkeley Institute for Data Science

I’m an ethnographer of science and technology, and I study the infrastructures and institutions that support the production of knowledge. Most of my previous work has been on Wikipedia, where I’ve studied the community of volunteer editors who produce and maintain an open encyclopedia. I’ve also studied distributed scientific research networks and projects, including the Long-Term Ecological Research Network and the Open Science Grid. In Wikipedia and scientific research, I’ve studied topics including newcomer socialization, community governance, specialization and professionalization, quality control and verification, cooperation and conflict, the roles of support staff and technicians, and diversity and inclusion. And, as these communities are made possible through software systems, I’m very interested in how the design of software tools and systems intersect with all of these issues.

I’m an interdisciplinary nomad who loves collaborating with people who use other kinds of methods and approaches. I began college as a computer science major at UT-Austin but switched to philosophy halfway through and got a degree in humanities. I got my MA in the Communication, Culture, and Technology program at Georgetown University, where I began empirically studying communities using qualitative and ethnographic methods. Then, I went to the UC-Berkeley School of Information for my Ph.D and worked with anthropologists, sociologists, psychologists, historians, organizational and management scholars, designers, and computer scientists. In terms of academic fields, I spend much of my time in science and technology studies, computer-supported cooperative work, and new media studies. I’m very excited to be bringing these approaches and methods to the challenges and opportunities of data science.

Sarah Brown

Chancellor’s Postdoctoral Fellow in Computer Science
University of California, Berkeley

I am a Chancellor’s Postdoctoral Fellow in Computer Science at the University of California, Berkeley. My faculty mentor is Professor Mike Jordan. I completed a BS in Electrical Engineering with a minor in Biomedical Engineering in 2011 a MS in Electrical and Computer Engineering and a PhD in Electrical Engineering in 2016 advised by Jennifer Dy. My graduate studies were supported by a Draper Laboratory Fellowship and a National Science Foundation Graduate Research Fellowship.

I build machine learning tools that bridge from data-agnostic methods to systems that fuel data driven discovery in historically qualitative domains. I approach this from two fronts: building interfaces that enable domain scientists to communicate their qualitative expertise to algorithms and developing context-appropriate machine learning solutions through close collaboration with domain scientists. When I the teach, I aim to engage learners in a conversation about the material. I take care to practice the strategies for creating an inclusive computer science learning environment I learned in the Carpentries Instructor Training.