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
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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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Former Adjunct Professor
glushko@ischool.berkeley.edu
Focus: Information-intensive systems and services, semantic standards, information policy, business innovation and entrepreneurship
(510) 643-2754
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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

Recent Publications

Diagram of a timeline of events for generating a recommendation for a sample learner
Mar 21, 2017

The path towards a more democratized learner success model for MOOCs has been hampered by a lack of capabilities to provide a personalized experienced to the varied demographics MOOCs aim to serve.  Primary obstacles to this end have been insufficient support of real-time learner data across platforms and a lack of maturity of recommendation models that accommodate the learning context and breadth and complexity of subject matter material in MOOCs. In this paper, we address both shortfalls with a framework for augmenting a MOOC platform with real-time logging and dynamic content presentation capabilities as well as a novel course-general recommendation model geared towards increasing learner navigational efficiency. We piloted this intervention in a portion of a live course as a proof-of-concept of the framework. The necessary augmentation of platform functionality was all made without changes to the open-edX codebase, our target platform, and instead only requires access to modify course content via an instructor role account.

The organization of the paper begins with related work, followed by technical details on augmentation of the platform’s functionality, a description of the recommendation model and its back-tested prediction results, and finally an articulation of the design decisions that went into deploying the recommendation framework in a live course.

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Data Science news

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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.
Zach Pardos
Pardos is an expert in the emerging field of educational data mining — applying data science methodologies to online learning environments to understand student learning.