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Computational Social Science

Online Certificate Program

Study with a Nobel Laureate… from wherever you are. Learn cutting-edge social science skills, statistical techniques and technologies.

We are now accepting applications for Fall 2022. Application deadline: July 15.

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Today’s social scientists must work with massive datasets of unprecedented complexity, process vast amounts of information and create sophisticated computer models and simulations. Whether your interest lies in economics, politics, sociology, psychology, or in another discipline, our online certificate program can help you harness the power to make original and lasting contributions in the social sciences.

Whether you’re just starting out or want to get to the next level, we have what you need.


Designed by a Nobel Laureate

This four-course online certificate program is overseen by New York University economist Thomas J. Sargent, who was awarded a Nobel Prize in 2011. Unlike at many major universities, where courses are often taught by graduate assistants, in this program you’ll be taught by Dr. Sargent along with his handpicked team comprising former students Chase Coleman and Spencer Lyon. They co-designed the program with Dr. Sargent and are lead developers at QuantEcon, an organization that provides open source computational tools for social scientists.

Why Get a Certificate?

The NYU Computational Social Science online certificate program prepares students for either a graduate program in the social sciences or for a career as a data analyst or computational social scientist. Computational social scientists work throughout the public and private sector – at universities, private research institutions and business enterprises such as tech companies like Amazon, Apple, Facebook, Google, and Uber.

What Can You Do With These Skills and Tools?

With access to a dizzying array of social data generated by user interactions on digital devices and platforms, you could analyze online social interactions, chart consumer purchases and credit card transactions, explore income disparity, illuminate race- and gender-based discrimination, or track the higher infectious rates in disadvantaged communities. You could tap into this immense datastream to shed light on the spread of fake news and information, illustrate the shortcomings of facial recognition technologies, or sift through billions of text messages to figure out if there are truly six degrees of separation between people. You could code bots to burrow into social media chatter to figure out what people are really saying and thinking, study the behavior of online trolls, or measure the impact of advertising on consumers. And when you’re done, you can program predictive models to forecast likely future outcomes.

Course Sequence

Dr. Sargent, Dr. Coleman, and Dr. Lyon jointly teach all four courses in the sequence, as well as a required self-paced 12-week summer “pre-class” that introduces you to the Python programming language and elementary software engineering tools. In the fall, you take courses on mathematical foundations and data tools in the social sciences. In the following spring your classes cover dynamic models and machine learning. Each course builds on the preceding course to provide you with the foundations you’ll need to succeed in your chosen academic and/or career path. The only prerequisites are working knowledge of linear algebra and calculus: derivatives.



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Online Foundations

An online, self-paced course to introduce students to Python and elementary software engineering tools.

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Mathematical Foundations for Computational Social Science

Review essential math and dive deeper into random variables, model building, and model estimation (both frequentist and Bayesian).

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Data Tools for Computational Social Science

Arm yourself with cutting-edge data manipulation and management tools and use them on real-world “messy” data sets.

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Dynamic Models for Computational Social Science

Learn dynamic programming, time-series analysis, Markov models, Hidden Markov Models and text analysis. Apply these tools to academic, government and industry research.

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Machine Learning for Computational Social Science

Apply classical and cutting-edge machine learning techniques to real-world problems in the social sciences.

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Thomas Sargent

Thomas Sargent

Dr. Thomas Sargent is co-founder and co-author of the QuantEcon website and past president of the American Economic Association, the Econometric Society, and the Society for Economic Dynamics. He has written several textbooks on macroeconomics and time series analysis. He loves Python and machine learning and teaching. With his good friend, Christopher Sims, he shared the Nobel prize for Economic Sciences in 2011.

Chase Coleman

Chase Coleman

Dr. Chase Coleman is a managing director at Valorum Data, a co-author of the QuantEcon Data Science lectures, and he helped build the data engineering team at CovidActNow. Chase received his PhD in economics from NYU Stern School of Business in 2019. While at Stern, he co-developed the “Data Bootcamp” course and has taught similar courses and workshops around the world. He loves mathematics, economics, and programming, and he compulsively buys books on all three topics.

Spencer Lyon

Spencer Lyon

Spencer Lyon is an economist (PhD NYU Stern, 2018) actively involved in various roles at the intersection of economics, data science, and computer science. Spencer currently serves as managing director of Valorum Data and co-leads the data engineering team at CovidActNow. Spencer is co-author of the QuantEcon Data Science lectures, co-creator of NYU Stern’s “Data Bootcamp” course, primary lecturer and organizer for the ML and big data seminar series for the MS Data Analytics program at the University of Central Florida, and instructor in various graduate-level training engagements on applied deep learning for Fortune 100 companies.

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Advanced Certificate Applications
Fall only admission
Application Deadline: July 15, 2022

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Request more info

If you have further questions about the computational social science online certificate program, please contact Dr. Tom Sargent (thomas.sargent@nyu.edu) for academic queries or program administrator (css-admin@nyu.edu) for administrative questions.


Frequently Asked Questions