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NORC at the University Chicago
Using advanced techniques to make sense of ever-increasing amounts and varieties of data.

NORC builds efficient computational workflows for solutions that span the data lifecycle. Our study design and data collection strategies start with a deep understanding of substantive project objectives. We formulate solutions for each project stage, from ingesting raw data to displaying analytic results that are readily accessible to end users, researchers, and the public. We have experience with all types of data, from administrative records and survey data to social media, images, and sensor data.

NORC data scientists are experts in machine learning and other approaches relevant to artificial intelligence, including regression trees, random forests, and boosting. We advance survey data science and improve data quality by applying machine learning techniques to data sources, creating classifiers that predict individual and household characteristics and the likelihood of response to a survey and response by mode. Our research on natural language processing includes evaluating and applying topic modeling, entity recognition, sentiment coding, and speech tagging approaches. We’ve presented our research at subject matter-focused and data science conferences and published our work in related journals.

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Data Science & Machine Learning Experts

Highlighted Projects

Enhancing the Admitted Class Evaluation Service™

Improving services that validate college admissions and placement policies

Client:

College Board

Survey of Doctorate Recipients

The only comprehensive data on U.S.-trained doctorate holders in science, engineering, and health

Client:

National Center for Science and Engineering Statistics at the National Science Foundation (NCSES/NSF)

Graduate Research Fellowship Program Pilot Project

Innovating data collection methods to track National Science Foundation research fellowship outcomes

Client:

National Science Foundation