Data Science

Related Faculty

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Assistant Professor
dbamman@ischool.berkeley.edu
Focus: Natural language processing, computational social science, machine learning, digital humanities
(510) 664-7460
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Assistant Professor
Alumni (PhD 2012)
jblumenstock@berkeley.edu
Focus: Development Economics, Data Science, Econometrics, Machine Learning, ICTD
(510) 642-4583
Coye Cheshire
Associate Professor
coye@ischool.berkeley.edu
Focus: Social exchange, social psychology, social networks and information exchange
(510) 643-6388
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Professor
chuang@ischool.berkeley.edu
Focus: Bio-sensory computing; information economics and policy
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Assistant Professor (I School and Graduate School of Education)
zp@ischool.berkeley.edu
Focus: Learning Analytics, Digital Learning Environments, Machine Learning
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Professor (I School and Dept. of Political Science)
stevew@ischool.berkeley.edu
Focus: International politics, international business, and the information economy; Cybersecurity; Behavioral economics within Information Systems
(510) 643-3755

Data Science news

Image from The Big Sleep via The Guardian

Analysis from I School Professor David Bamman finds proportion of female authors and characters fell after 19th century, with male authors remaining ‘remarkably resistant’ to writing women.

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Machine learning and big data don’t intuitively go hand-in-hand with studies of literary fiction; however, new research from Professor David Bamman, using a machine learning algorithm and natural language processing, revealed surprising trends related to gender in novels of the 20th century.  

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Professor David Bamman’s machine-learning algorithm analyzed the presentation of gender in more than 100,000 novels.

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Position will begin July 2016; applications are due December 14.
David Bamman
Bamman’s work applies natural language processing and machine learning techniques to empirical questions in the humanities and social sciences.
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The dataset could help answer whether it’s possible to accurately use consumer-grade devices to interpret attention level in a problem-solving test. The class hopes that other researchers will be able to repeat the experiment with even larger subject pools.
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Capped by a keynote from Obama adviser John Podesta, a day-long workshop brought together the worlds of government, business, the law, and academia for what assistant professor Deirdre Mulligan called “a frank and honest conversation about our values,” and about how to balance those values with the omnipresent, often invisible collection of data about every aspect of our lives.
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The School of Information has officially started its new online Master of Information and Data Science (MIDS) program, preparing students to solve real-world problems using complex and unstructured data.
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A new student research project analyzes the text of Yelp restaurant reviews to automatically reveal the underlying topics discussed by the reviewers — and predict the rating the restaurant would have received based on each individual topic.

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