We are excited to announce the launch of our new D-Lab Data Science Fellows Program.

 

About the program

The D-Lab Data Science Fellows program is designed to give outstanding UC Berkeley students and researchers the opportunity to advance their data science training within the D-Lab’s diverse and intellectually vibrant community.  We offer participants a supportive mentorship and peer-learning environment in which they can apply their expertise and knowledge, gain professional teaching and leadership skills and connect with industry and public sector partners.

D-Lab Data Science Fellowships are awarded to facilitate and recognize significant contributions to one or more core areas in service to the D-Lab community and constituencies.  Fellows will be placed in one or both of the following types of activities.

  1. Special Projects fellows work on research that require the application of data science tools and methods. For example, the D-Lab Online Hate Index project is one in which researchers apply methods of survey design research, text analysis and machine learning to identify hate speech patterns in social media posts.

  2. Service fellows contribute to training activities such as curriculum development, instruction, consulting and mentorship in data science-related topic areas. These include development of online learning resources, frequently optimizing the use of Jupyter Project.

D-Lab Data Science Fellows play an important role in our community, contributing to research and instruction that promote and innovate the use of data science tools and methods. Fellows will be expected to devote, on average, 5 - 10% of their time on D-Lab activities. This is a limited, yet continuous time commitment, with Fellow involvement at the D-Lab throughout the semester.  

Eligibility and Funding

The D-Lab invites UC Berkeley graduate students, advanced undergraduate students, current postdocs and visiting scholars and staff to apply to the D-Lab Data Science Fellows Program. We welcome applicants from all academic disciplines and methodological approaches.  Fellowships are for one year and include recognition of contributions to data science, desk space, and for some campus affiliates, a modest award. Moreover, we offer you a valuable co-working experience alongside campus experts in which you will gain access to professional experiences unparalled on campus.

Application Process

Please use this form to apply. Applications will be accepted on a rolling basis. For more information about the program, see the D-Lab Data Science Fellows page.  Applicants are encouraged to visit the D-Lab website and participate in D-Lab workshops or working groups as a way to learn more about our services and community.

 Data Science at the D-Lab

The D-Lab helps Berkeley faculty, staff, and students move forward with world-class research in data-intensive research. We think of data science as an expansive area, one that is constantly changing as the research frontier moves. We offer a venue for methodological exchange from all corners of campus and across its bounds. In one capacity, we provide training and individual consulting on study design, methods and tools such as programming, statistics, machine learning, data collection,  and more. In another capacity, we conduct research on both historical and contemporary issues, with a focus on data science for social good.  Collaborating with other Berkeley institutes, departments and schools, D-Lab services complement and strengthen the breadth and excellence of data science across Berkeley’s academic programs and faculty research. This is exemplified by D-Lab’s close partnerships with the Social Science Matrix, Data Science Education Program, Digital Humanities at Berkeley, the Library, the Berkeley Institute for Data Science, and Research Teaching and Learning.  Since our founding in 2013, we have prioritized keeping D-Lab an inclusive environment that values diversity and open exchange, a culture captured by our motto “It’s OK not to know”. 

Author: 

Patty Frontiera

Dr. Patty Frontiera is the D-Lab geospatial topic area lead. As such, she develops the geospatial workshop curriculum, teaches workshops and consults on geospatial topics.  Patty has been with the D-Lab since 2014 and served as the the Academic Coordinator through Spring 2017. Patty received her Ph.D. in Environmental Planning from UC Berkeley where her dissertation explored the application and effectiveness of generalized spatial representations in geographic information retrieval.