Data Science

Related Faculty

Morgan G. Ames
Assistant Adjunct Professor
Alumni (MIMS 2006)
Science and technology studies; education; anthropology; youth technocultures; ideology and inequity; critical data science
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Assistant Professor
Natural language processing, computational social science, machine learning, digital humanities
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Assistant Professor
Alumni (PhD 2012)
Development Economics, Data Science, Machine Learning, Econometrics, ICTD
Coye Cheshire
Professor
Trust, social exchange, social psychology, and information exchange
John Chuang
Head of School, Associate Dean, and Professor
Bio-sensory computing; brainwave authentication; information economics and policy
Photo of Aditya Parameswaran
Assistant Professor (I School and EECS)
Data management, interactive or human-in-the-loop data analytics, information visualization, crowdsourcing, data science
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Assistant Professor (I School and Graduate School of Education)
Learning Analytics, Digital Learning Environments, Machine Learning
Steve Weber
Faculty Director, Center for Long-Term Cybersecurity and Professor (I School and Dept. of Political Science)
International politics, international business, and the information economy; Cybersecurity; Behavioral economics within Information Systems

Recent Publications

Feb 13, 2018

This essay explores the changing significance of gender in fiction, asking especially whether its prominence in characterization has varied from the end of the eighteenth century to the beginning of the twenty-first. The authors found that while gender roles were becoming more flexible, the space actually allotted to (real, and fictional) women on the shelves of libraries was contracting sharply.

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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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.
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.

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