computational social science phd reddit

Stack Exchange Network

Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

How to find suitable PhD programs in Computational Social Science? [closed]

I recently graduated from a master's program in Information Science in the U.S. and want to get into a Ph.D. program in the near future. I am pretty interested in computational social science, specifically, I love doing research about social media and understand online communities and social behaviors.

I'm wondering how to find some suitable Computational Social Science programs I can possible get into. I looked up some network science and social dynamics labs. They are pretty cool. But my concern is a lot of hubs of CSS are in CS/EE departments and the competitions are way much bigger than i-schools (where I finished my degree), and I do not very interested in getting too technical or engineering. A good blend of social focus and DS would be ideal for me.

Any suggestions on how I can make the best of the school searching in this specific filed and how to find some inner information are appreciated. Thanks in advance.

• graduate-admissions
• application
• social-media

• 1 There are quite many and as you've studied Info Sci, I'd expect your professors to have pretty helpful informations on this. In Info Sci in particular UMich and Cornell have top tier PhD programs (School of Info at Michigan and Info Sci at Cornell), and they've some of the best in the world in computational social science. I should say, both of these are extremely competitive as you can imagine (and not any less competitive than CS programs I guess). There are also a number of other departments at umich and cornell that share faculty with those departments, e.g. complex systems, sociology, CS. –  nara Commented Sep 22, 2020 at 17:43
• 1 There are many other places too, I'll name a few: Indiana info sci, USC's and UW's info sci and also communication departments, UPenn communication, Northeastern network sci and political sci, Northwestern's Kellogg probably (I don't know about PhD programs, but they have many top researchers working in this area), Vermont complex systems institute (again, I don't know if they have a PhD program). I'd suggest also taking a look at the Santa Fe Institute and seeing where their external profs work. I strongly advise against going for a PhD because of one or two profs though, so be careful. –  nara Commented Sep 22, 2020 at 17:55
• 1 Some more disciplinary programs that do comp soc sci stuff as well: NYU data sci (very competitive and not focused on this, but still worth looking into), Duke, Penn State, and UNCCH sociology deps, UCLA communication, Carnegie Mellon social & decision sci and also their business school, MIT Sloan, and check out other business schools too, e.g. Fuqua & Wharton. You should be careful if you decide to go with any of these though: 1. You should have a good reason to convince the admission committee that you're a good fit, and 2. You should make sure you actually want to be, e.g., a sociologist! –  nara Commented Sep 22, 2020 at 18:08
• 1 If you consider options outside of the US, there are plenty of places in Europe and a few in Canada as well (and I guess in other places too , but I don't know many except a few people in Korea). These are a few I remember right now: UToronto and UBC sociology, CEU in Budapest/Vienna, Konstanz, Bremen, MPI for human development in Berlin, Leibniz institutes for social science in Mannheim and Cologne, QMU in London, Oxford's OII, Manchester sociology, Amsterdam (they're one of the best), Vienna complexity hub, and a few places in Paris (e.g. the french media lab in Sciences Po). –  nara Commented Sep 22, 2020 at 18:18
• 1 I can't emphasize enough though that if you're not from Europe and have not studied there, make sure you fully familiarize yourself with the education system before committing to a PhD in Europe (ideally through a few month of a research internship or something like that). e.g. in most places in Europe PhD is extremely advisor-centric, you're unlikely to receive much training and your PhD is equivalent to just the phase of the American PhD when you write your thesis (candidacy), and many other differences you should thoroughly research before deciding to go there! –  nara Commented Sep 22, 2020 at 18:23

There are several/many programs in Communication (which may sometimes refer to itself as Mass Communication and some related terms) that would be good places to study social media, online behavior, etc. using computational methods. Just going off the top of my head, I'd look at the PhD programs at University of North Carolina, University of California at Santa Barbara, Ohio State, University of Wisconsin at Madison, Northeastern University. I also know that Northeastern has an interdisciplinary network science program.

Basically every Communication program that I know of is actively trying to enhance their capacities in this area and would probably be attracted to prospective students with such interests as well. You should also know that competition for faculty jobs in this field is also much less fierce than in most other fields that I know about (there are typically more open positions than new PhDs in a given year). Of course, COVID-19 will cause a hopefully temporary downturn in available jobs in all fields.

Not the answer you're looking for? Browse other questions tagged phd graduate-admissions application social-media .

• Featured on Meta
• Bringing clarity to status tag usage on meta sites
• Announcing a change to the data-dump process

Hot Network Questions

• What does "if you ever get up this way" mean?
• Do you have to tell your friend if you have hate for him in your heart?
• Beta function of a marginal operator
• Who owns code contributed to a license-free repository?
• What is Zion's depth in the Matrix?
• Why is GParted distributed as an ISO image? Is it to accommodate Linux needs as well as Windows needs?
• Unclear point in definition of advantage function in PPO
• Does an airplane fly less or more efficiently after an mid-flight engine failure?
• Invest smaller lump sum vs investing (larger) monthly amount
• Expensive constructors. Should they exist? Should they be replaced?
• Did Gandalf know he was a Maia?
• Word to describe telling yourself that you are not, and will never be, good enough
• Soldering RG179 coaxial cable directly to PCB
• What did Horace say about combining Latin and Greek roots?
• Sitecore form is not redirecting after implemented Content Security Policies
• How to prevent my frozen dessert from going solid?
• Flight delayed, risk of missing connection, can I cancel and get refund?
• Is the Oath Formula "By the Life of Pharaoh" Attested Anywhere outside of Biblical Literature?
• What should be the affiliation of PhD student who submitted thesis but yet to defend, in a conference talk slides?
• How many ways can you make change?
• What happens to entropy during compression?
• Avoiding USA "gambling tax"
• decode the pipe
• Why is the wiper fluid hose on the Mk7 Golf covered in cloth tape?

• Skip to Content
• Bulletin Home

• Interdisciplinary Programs >
• Graduate Programs >

Social and Engineering Systems

• Around Campus
• Academic Program
• Administration
• Arts at MIT
• Campus Media
• Fraternities, Sororities, and Independent Living Groups
• Health Services
• Priscilla King Gray Public Service Center
• Religious Organizations
• Student Government
• Work-​Life and Family Resources
• Advising and Support
• Digital Learning
• Disability and Access Services
• Information Systems and Technology
• Student Financial Services
• Writing and Communication Center
• Major Course of Study
• General Institute Requirements
• Independent Activites Period
• Undergraduate Research Opportunities Program
• First-​Year Advising Seminars
• Interphase EDGE/​x
• Edgerton Center
• Grading Options
• Study at Other Universities
• Internships Abroad
• Career Advising and Professional Development
• Teacher Licensure and Education
• ROTC Programs
• Financial Aid
• Medical Requirements
• Graduate Study at MIT
• General Degree Requirements
• Other Institutions
• Registration
• Term Regulations and Examination Policies
• Academic Performance and Grades
• Policies and Procedures
• Privacy of Student Records
• Abdul Latif Jameel Clinic for Machine Learning in Health
• Abdul Latif Jameel Poverty Action Lab
• Art, Culture, and Technology Program
• Broad Institute of MIT and Harvard
• Center for Bits and Atoms
• Center for Clinical and Translational Research
• Center for Collective Intelligence
• Center for Computational Science and Engineering
• Center for Constructive Communication
• Center for Energy and Environmental Policy Research
• Center for Environmental Health Sciences
• Center for Global Change Science
• Center for International Studies
• Center for Real Estate
• Center for Transportation &​ Logistics
• Computer Science and Artificial Intelligence Laboratory
• Concrete Sustainability Hub
• D-​Lab
• Deshpande Center for Technological Innovation
• Division of Comparative Medicine
• Haystack Observatory
• Initiative on the Digital Economy
• Institute for Medical Engineering and Science
• Institute for Soldier Nanotechnologies
• Institute for Work and Employment Research
• Internet Policy Research Initiative
• Joint Program on the Science and Policy of Global Change
• Knight Science Journalism Program
• Koch Institute for Integrative Cancer Research
• Laboratory for Financial Engineering
• Laboratory for Information and Decision Systems
• Laboratory for Manufacturing and Productivity
• Laboratory for Nuclear Science
• Legatum Center for Development and Entrepreneurship
• Lincoln Laboratory
• Martin Trust Center for MIT Entrepreneurship
• Materials Research Laboratory
• McGovern Institute for Brain Research
• Microsystems Technology Laboratories
• MIT Center for Art, Science &​ Technology
• MIT Energy Initiative
• MIT Environmental Solutions Initiative
• MIT Kavli Institute for Astrophysics and Space Research
• MIT Media Lab
• MIT Office of Innovation
• MIT Open Learning
• MIT Portugal Program
• MIT Professional Education
• MIT Sea Grant College Program
• Nuclear Reactor Laboratory
• Operations Research Center
• Picower Institute for Learning and Memory
• Plasma Science and Fusion Center
• Research Laboratory of Electronics
• Simons Center for the Social Brain
• Singapore-​MIT Alliance for Research and Technology Centre
• Sociotechnical Systems Research Center
• Whitehead Institute for Biomedical Research
• Women's and Gender Studies Program
• Architecture (SB, Course 4)
• Architecture (MArch)
• Art and Design (SB, Course 4-​B)
• Art, Culture, and Technology (SM)
• Architecture Studies (SMArchS)
• Media Arts and Sciences
• Planning (SB, Course 11)
• Urban Science and Planning with Computer Science (SB, Course 11-​6)
• Aeronautics and Astronautics Fields (PhD)
• Aerospace Engineering (SB, Course 16)
• Engineering (SB, Course 16-​ENG)
• Biological Engineering (SB, Course 20)
• Biological Engineering (PhD)
• Chemical Engineering (Course 10)
• Chemical-​Biological Engineering (Course 10-​B)
• Chemical Engineering (Course 10-​C)
• Engineering (Course 10-​ENG)
• Engineering (Course 1-​ENG)
• Computation and Cognition (Course 6-​9)
• Computer Science and Engineering (Course 6-​3)
• Computer Science and Molecular Biology (Course 6-​7)
• Electrical Engineering with Computing (Course 6-​5)
• Electrical Engineering and Computer Science (MEng)
• Computer Science and Molecular Biology (MEng)
• Health Sciences and Technology
• Archaeology and Materials (Course 3-​C)
• Materials Science and Engineering (Course 3)
• Materials Science and Engineering (Course 3-​A)
• Materials Science and Engineering (PhD)
• Mechanical Engineering (Course 2)
• Mechanical and Ocean Engineering (Course 2-​OE)
• Engineering (Course 2-​A)
• Nuclear Science and Engineering (Course 22)
• Engineering (Course 22-​ENG)
• Anthropology (Course 21A)
• Comparative Media Studies (CMS)
• Writing (Course 21W)
• Data, Economics, and Design of Policy (MASc)
• Economics (Course 14-​1)
• Economics (PhD)
• Mathematical Economics (Course 14-​2)
• Global Studies and Languages (Course 21G)
• History (Course 21H)
• Linguistics and Philosophy (Course 24-​2)
• Philosophy (Course 24-​1)
• Linguistics (SM)
• Literature (Course 21L)
• Music (Course 21M-​1)
• Theater Arts (Course 21M-​2)
• Political Science (Course 17)
• Science, Technology, and Society/​Second Major (STS)
• Business Analytics (Course 15-​2)
• Finance (Course 15-​3)
• Management (Course 15-​1)
• Biology (Course 7)
• Chemistry and Biology (Course 5-​7)
• Brain and Cognitive Sciences (Course 9)
• Chemistry (Course 5)
• Earth, Atmospheric and Planetary Sciences (Course 12)
• Mathematics (Course 18)
• Mathematics (PhD)
• Mathematics with Computer Science (Course 18-​C)
• Physics (Course 8)
• Department of Electrical Engineering and Computer Science
• Institute for Data, Systems, and Society
• Chemistry and Biology
• Climate System Science and Engineering
• Computation and Cognition
• Computer Science and Molecular Biology
• Computer Science, Economics, and Data Science
• Humanities and Engineering
• Humanities and Science
• Urban Science and Planning with Computer Science
• African and African Diaspora Studies
• American Studies
• Ancient and Medieval Studies
• Applied International Studies
• Asian and Asian Diaspora Studies
• Biomedical Engineering
• Energy Studies
• Entrepreneurship and Innovation
• Environment and Sustainability
• Latin American and Latino/​a Studies
• Middle Eastern Studies
• Polymers and Soft Matter
• Public Policy
• Russian and Eurasian Studies
• Statistics and Data Science
• Women's and Gender Studies
• Advanced Urbanism
• Computational and Systems Biology
• Computational Science and Engineering
• Design and Management (IDM &​ SDM)
• Joint Program with Woods Hole Oceanographic Institution
• Leaders for Global Operations
• Microbiology
• Music Technology and Computation
• Operations Research
• Real Estate Development
• Supply Chain Management
• Technology and Policy
• Transportation
• School of Architecture and Planning
• School of Engineering
• Artificial Intelligence and Decision Making (Course 6-​4)
• Nuclear Science and Engineering (PhD)
• School of Humanities, Arts, and Social Sciences
• Humanities (Course 21)
• Humanities and Engineering (Course 21E)
• Humanities and Science (Course 21S)
• Sloan School of Management
• School of Science
• Brain and Cognitive Sciences (PhD)
• Earth, Atmospheric and Planetary Sciences Fields (PhD)
• Interdisciplinary Programs (SB)
• Climate System Science and Engineering (Course 1-​12)
• Computer Science, Economics, and Data Science (Course 6-​14)
• Interdisciplinary Programs (Graduate)
• Biological Oceanography (PhD)
• Computation and Cognition (MEng)
• Computational Science and Engineering (SM)
• Computational Science and Engineering (PhD)
• Computer Science, Economics, and Data Science (MEng)
• Engineering and Management (SM)
• Leaders for Global Operations (MBA/​SM and SM)
• Music Technology and Computation (SM and MASc)
• Real Estate Development (SM)
• Statistics (PhD)
• Supply Chain Management (MEng and MASc)
• Technology and Policy (SM)
• Transportation (SM)
• Aeronautics and Astronautics (Course 16)
• Aerospace Studies (AS)
• Architecture (Course 4)
• Biological Engineering (Course 20)
• Civil and Environmental Engineering (Course 1)
• Comparative Media Studies /​ Writing (CMS)
• Comparative Media Studies /​ Writing (Course 21W)
• Computational and Systems Biology (CSB)
• Computational Science and Engineering (CSE)
• Concourse (CC)
• Data, Systems, and Society (IDS)
• Earth, Atmospheric, and Planetary Sciences (Course 12)
• Economics (Course 14)
• Edgerton Center (EC)
• Electrical Engineering and Computer Science (Course 6)
• Engineering Management (EM)
• Experimental Study Group (ES)
• Global Languages (Course 21G)
• Health Sciences and Technology (HST)
• Linguistics and Philosophy (Course 24)
• Management (Course 15)
• Media Arts and Sciences (MAS)
• Military Science (MS)
• Music (Course 21M)
• Naval Science (NS)
• Science, Technology, and Society (STS)
• Special Programs
• Supply Chain Management (SCM)
• Theater Arts (21T)
• Urban Studies and Planning (Course 11)
• Women's and Gender Studies (WGS)

The Doctoral Program in Social and Engineering Systems (SES) offered by the Institute for Data, Systems, and Society is a unique research program focused on addressing concrete and societally significant problems by combining methods from computing, data science and statistics, engineering, and the social sciences. Core classes provide students with a grounding in probability, statistics, microeconomic theory, and empirical research in the social sciences. Students then build on that foundation with coursework in information, systems, and decision science; social sciences; and classes in their particular area of applied research.

Additional information about this degree program is available under the section on the Institute for Data, Systems, and Society's graduate academic programs.

Print this page.

The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

The Computational Social Science Lab is an interdisciplinary research group in the Department of Computer Science at the University of Toronto. We work at the intersection of AI, data, and society .

The Computational Social Science Lab is an interdisciplinary research group that works at the intersection of AI, data, and society . We are part of the Department of Computer Science at the University of Toronto.

About us Explore our research

Selected Research

Search NYU Steinhardt

Doctor of Philosophy Statistics and Computational Social Science

This interdisciplinary doctoral program combines statistical, computational, and social science theory and practice to address problems of social importance in public health, education, criminal justice, and other domains. Graduates will be prepared for academic careers, as well as research positions in nonprofit organizations, government agencies, and industry.

Degree Details

Official degree title.

PhD in Statistics and Computational Social Science

Statistics & Computational Social Science PhD Online Info Session

Join us online to learn more about our new doctoral program in Statistics and Computational Social Science (SCSS), launching this fall.

Academic Experience

Careers and outcomes.

Your academic experience in this program will begin with coursework in the broad areas of statistics, computational methods, and disciplines such as sociology, economics, psychology, and education. At the same time, you will work with your faculty advisor to develop your own research agenda, while learning from and collaborating with researchers in the Steinhardt School, the broader NYU academic community, and beyond. 

The ultimate goal of the doctoral program is to produce a dissertation of original research that unites technical methodology with substantive knowledge to further an understanding of important social science questions. You will share your research with broader communities via academic publications, and will have opportunities to hone your pedagogical skills through teaching and assisting with courses in applied statistics.

Graduates of this program will be equipped for postdoctoral and faculty positions in university departments such as statistics, data science, and political science, as well as for positions in schools of communication, information, public policy, and education.

Outside academia, graduates will be ready for careers as research scientists or research directors at nonprofit organizations (e.g., RAND or MDRC), in the public sector (in the U.S. or abroad), program officer positions at foundations (e.g., Simons or MacArthur), and a variety of research-oriented positions in industry.

If you have any additional questions about our degree, please feel free to contact us at [email protected] .

Joseph Cimpian

Professor of economics and education policy.

Daphna Harel

Associate professor of applied statistics; director of a3sr ms program.

Jennifer Hill

Professor of applied statistics; co-department chair; co-director of priism.

Klint Kanopka

Assistant professor of applied statistics.

Associate Professor

Co-Department Chair, Professor of Applied Statistics; Co-Director of PRIISM

Ravi Shroff

Associate professor of applied statistics, anne l. washington, assistant professor of data policy, take the next step.

Advance your personal and professional journey – apply to join our community of students.

Designated Emphasis CSS

Since Fall 2019, the DE in Computational Social Science has been offered to PhD students from selected graduate programs/groups, and since 2024, it is open to PhD students from any graduate program/group. Completion of the requirements for the DE and an affiliated Ph.D. are reflected in the notation on the Doctoral Diploma: “ Ph.D. in X with Emphasis in Computational Social Science ”.

Computational Social Science is about developing and applying computational tools to deepen our understanding of long–standing questions in the social sciences, as well as exploring new ones. As suggested by the term itself, the approach is per definition interdisciplinary in terms of requiring foundations in computational methods and social science theory, and it is multidisciplinary in terms of blurring the traditional boundaries between disciplines in the social sciences. Some 30 faculty members from 10 Departments of UC Davis are part of the DE.

More on CSS                 People                 Requirements

The DE CSS allows students with computational and mathematical skills to deepen their understanding of social science theory and it allows students from the social sciences to improve their analytical skills in areas like big data analysis, computer simulations, network analysis, and machine learning. The DE requires at least 4 courses, consisting of one required course and 3 electives, with one elective required from each of  the following 3 different categories:

Computational Social Science

With the rapid increase in the availability and use of computers, and their capacity to process information rapidly, the value of knowledge associated with computational resources has increased substantially. The purpose of this area is to encourage students to learn a set of methods that are associated with various computational orientations to social science. This includes such things as social network analysis, social sequence analysis, topic modeling, and other “Big Data” approaches to data analysis. This is largely a methods-oriented area, but it also addresses emerging theories about sampling and the nature of social relationships within a highly networked society.

Related people

Tisch University Professor of Computer Science and Information Science and Interim Dean of Computing and Information Science

Associate Professor of Information Sciences

Associate Professor

All research areas

Academic Programs

• CSE PhD Overview
• Dept-CSE PhD Overview
• CSE Doctoral Theses
• Program Overview and Curriculum
• For New CCSE Students
• Terms of Reference

MIT Doctoral Programs in Computational Science and Engineering

The Center for Computational Science and Engineering (CCSE) offers two doctoral programs in computational science and engineering (CSE) – one leading to a standalone PhD degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an interdisciplinary PhD degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD).

While both programs enable students to specialize at the doctoral level in a computation-related field via focused coursework and a thesis, they differ in essential ways. The standalone CSE PhD program is intended for students who intend to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the the Schwarzman College of Computing. In contrast, the interdisciplinary CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline.

For more information about CCSE’s doctoral programs, please explore the links on the left. Information about our application and admission process is available via the ‘ Admissions ‘ tab in our menu. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

Announcing the One-Year Master of Computational Social Science (MaCSS)

Do you have a social science degree? Make it count.

Do you have a degree in economics, history, political science, psychology, sociology, or another social science? UC Berkeley is launching MaCSS because we believe social scientists make good data analysts.

The MaCSS program will teach you to integrate social science theories and findings with statistical training and computational skills to solve real-world social problems.  We will give you practical skills in the analysis and interpretation of social data – data about people, communities, organizations, and their interactions – and prepare you for a job as a data analyst in business, government, or the nonprofit world.

Download our brochure for an overview of the MaCSS program.

Data Matters—Make Sure Everyone Counts

In recent years, we have experienced an explosion of data. In the past, large data sets were only available from government  agencies and academic institutions. Today, we have access to a whole range of data from both the public and private sectors.  We can know many things about people, like how many lattes they drink each month, how long they sit in traffic, and how  many miles they run each week.

Many of these newly accessible data categories are used to drive consumerism, but what if  we thought more creatively about their application to solving some of the world’s stickiest problems? Our access to data today presents a range of untapped opportunities to improve the quality of life in both the public and private sectors.

Do Good with Data

If you are intellectually curious, eager to learn new cutting-edge data-analysis methods, and interested in answering questions with data, then MaCSS is the program for you. MaCSS graduates will be prepared for positions as data analysts with a healthy skepticism regarding data quality, along with a toolkit for working with imperfect data.

Data Analytics and Computational Social Science

Tell stories with data by bridging the gap between qualitative and quantitative..

Request more information about DACSS

The Data Analytics and Computational Social Science (DACSS) program in the School of Public Policy at UMass Amherst will help you manage complex information and learn to tell compelling stories with data.

Here, you'll strike a unique balance between computer science and social science, gaining intensive training in advanced data science skills while also seeking to understand the underlying social, political, and economic processes that generate those data.

Our courses are designed to be flexible—take a few classes in your spare time or pursue your master's degree in person or online. Anyone who has completed an undergraduate degree can pursue introductory and advanced training in computational social science.

Both private-sector business and public-sector organizations are searching for professionals with a background in data analytics and computational social science. Whether you are just finishing your undergraduate degree, looking to change jobs, or hoping to enhance a valuable skill set, DACSS can help you prepare for your future.

Explore our programs

Benefits list.

Have questions about DACSS?

Don't know if you are prepared to complete an intensive data analytics program? Wondering how a DACSS degree or certificate could help boost your career? We've compiled a selection of the most frequently asked questions by prospective students to help you learn more about our program.

Student Profile: Alex Genovese

Meet Alex Genovese, a current graduate student in the Data Analytics and Computational Social Science (DACSS) program. Alex earned a bachelor's in political science and plans to use the skills learned in DACSS in pursuit of a PhD. 

In the news

Dacss student part of umass group advocating for science.

Andjella Jean-Jacques, data analytics and computational social science, is among those who traveled to Washington, D.C. to advocate for science. 

Stapleton's Research Highlighted in Washington Post Op-Ed

The research of Carey E. Stapleton, data analytics and computational social science (DACSS), was recently mentioned in an op-ed for The Washington Post. 

Stapleton Co-Authors Paper for Public Opinion Quarterly

Data analytics and computational social science (DACSS) lecturer and teaching fellow, Carey Stapleton has co-authored a new paper for Public Opinion Quarterly. 

At UMass Amherst, you can undertake intensive courses in advanced data science taught from a social science perspective.

• DACSS at the UMass School of Public Policy
• DACSS Admission Requirements
• DACSS Curriculum

Global footer

• ©2024 University of Massachusetts Amherst
• Site policies
• Non-discrimination notice
• Accessibility
• Terms of use

• Departments and Units
• Majors and Minors
• LSA Course Guide
• LSA Gateway

Search: {{$root.lsaSearchQuery.q}}, Page {{$root.page}}

• CSS Initiative
• News and Events
• Undergraduate Students
• Graduate Students
• Alumni and Friends

• Complex Systems Minor
• Featured CSCS Minor Alumni
• Complex Systems Undergrad Courses
• Undergraduate Awards
• Transfer Credit Policies and Procedures
• Complex Systems Graduate Certificate
• Graduate Courses
• Gifts to CSCS
• Featured CSCS Certificate Alumni

Computational Social Science (CSS) Initiative

The Center for the Study of Complex Systems announced the Computational Social Science (CSS) initiative in the fall of 2017. Under the leadership of core faculty member Elizabeth Bruch, the CSS Initiative aim is to develop new courses in computational social science and to build an interdisciplinary community of Michigan students and faculty who work and collaborate in this area. Vast streams of activity data from electronic sources make it possible to study human behavior with an unparalleled richness of detail. Social scientists can, for the first time, avail themselves of granular, disintermediated data to assemble individual narratives, motivations, and behavioral arcs as people go about living their lives.   

The plan is for this community to be able to discuss topics of mutual interest, learn new skills, and create interdisciplinary collaborations. As part of this effort, we are organizing a series of methods workshops designed to provide introductions to important CSS-related models and techniques (see below).

Core faculty member Daniel Romero is co-organizing several initiative activities.

CSS Course Development

The CSS Initiative will develop new courses at both the undergraduate and graduate levels including hands-on labs for social science undergraduates without a strong programming background. These labs will provide an opportunity for undergraduates to collect and analyze online data from places like Reddit and Twitter to study topics such as trolling behavior and its consequences or networks of influence. At the graduate level, the CSS Initiative is developing hands-on course materials on a range of topics including discrete choice modeling, network analysis, Bayesian statistics, and reproducible research.

CSS Workshops

To complement course development, a series of one-day technical workshops were offered during the 2017/2018 academic year that introduced students and faculty to quantitative methods for modeling human behavior and social dynamics.  The CSS Initiative will also cultivate cross-disciplinary interaction between faculty and students within the College of LSA and throughout the University of Michigan. Planned activities include a computational social science speaker series, a CSS faculty Working Group, a book group to discuss recent publications of interest, and coordinated activities with related groups such as the Michigan Data Science (MIDAS) initiative. For more information, please contact [email protected].

For other CSS sponsored Events and Seminars click "CSS Events" in the left list.

• Information For
• Prospective Students
• Current Students
• Faculty and Staff
• More about LSA
• How Do I Apply?
• LSA Magazine
• Student Resources
• Academic Advising
• Global Studies
• LSA Opportunity Hub
• Social Media
• Update Contact Info
• Privacy Statement
• Report Feedback

• Search This Site All UCSD Sites Faculty/Staff Search Term
• Instructional Assistantships
• Postdoctoral Scholars
• M.S. Students
• Administration
• Prospective Students
• Graduate Courses
• Masters FAQ
• Program Cost
• Capstone Project

The CSS M.S. program curriculum integrates the technical skills associated with large scale data analysis (programming, algorithms, data structures) with formal social science theories and computational approaches to modeling and simulation. Students admitted to the program will be required to take the courses listed below. 

Core Courses

The program begins with an intensive ten-week summer bootcamp. All students must satisfactorily complete the bootcamp. The bootcamp is formally composed of two courses: CSS 201S. Introduction to Computational Social Science and CSS 202S. Computational Social Science Technical Bootcamp. 

CSS 201S. Introduction to Computational Social Science (8). Overview of causal and statistical inference, data types/structure, and modeling/analytical approaches to social science data.  Topics include models of social phenomena at different scales (cognition, behavior, learning, communication, language, game theory, markets, etc.) and analysis of different types of social data (social networks, text, GIS, timeseries, etc.) Emphasis is placed on the understanding of analytical and modeling methods, their applications, and limitations.

CSS 202S. Computational Social Science Technical Bootcamp (8).  This course provides practical experience with the technical skills underpinning computational social science. Topics may include calculus, linear algebra, probability, Unix/Linux, working in the terminal, file encoding, filesystem organization, Python, numpy, pandas, scikit, notebooks, matplotlib, code style, scraping, data storage, SQL, JSON, CSV/TSV, version control, git, spreadsheets, algorithms, basic data visualization, and machine learning basics.

The courses below are additionally required:

CSS 204. Statistical Computing and Inference from Data I (6). The first of a series of intensive courses in statistical computing to draw inferences from data.  This course covers research design, causal inference, data wrangling, visualization, probability, statistical inference, and the general linear model.

CSS 205. Statistical Computing and Inference from Data II (4). The second of a series of intensive courses in statistical computing to draw inferences from data.  This course covers the generalized linear model, resampling methods, maximum likelihood estimation, and regularization. Prerequisites: PSYC 201A, POLI 204B, CSS 204 or instructor approval

CSS 206.   Machine Learning for Social Sciences. (4). An introduction to machine learning methods with applications to social science data. This course covers the entire machine learning pipeline: feature engineering, model design, tuning, training, tuning, evaluation, and validation. Emphasis is on foundational methods in supervised and unsupervised machine learning problems.  Prerequisites:  CSS 202S, MATH 18, or instructor approval.

CSS 296. Research in Computational Social Science. (2, 4, 4).  Independent research under the supervision of individual faculty members.  

Students take this course for 2 units in fall quarter,  4 units in winter quarter, and 4 units in spring quarter.  This course will always be formally supervised by a faculty member at UC San Diego, though primary project management may occur with an internship partner organization. The three-quarter CSS 296 functions as a capstone, and provides a portfolio project for each student graduating with an M.S. in Computational Social Science.  The capstone may take two forms, depending on student interest and placement availability:

• Internship Placement. Students will be embedded with a local company or organization in a mutually beneficial arrangement that allows the student to learn from the environment as well provide a meaningful contribution to the organization.
• Faculty Project. Students will work with a faculty member on a project of mutual interest, entailing Computational Social Science techniques.

Seminar Series

Each year, the Computational Social Science educational committee will circulate a list of elective courses for the coming year; the list will include at least two course options for each quarter with students choosing one elective in Fall, Winter, and Spring.

Course topics may include visual computing, computational modeling, econometrics, business, data analysis, casual interference etc.  

• Graduate Student Handbook

Get the Reddit app

This subreddit is for discussing academic life, and for asking questions directed towards people involved in academia, (both science and humanities).

Choosing a PhD Supervisor in Computational Social Science

By continuing, you agree to our User Agreement and acknowledge that you understand the Privacy Policy .

Enter the 6-digit code from your authenticator app

You’ve set up two-factor authentication for this account.

Enter a 6-digit backup code

Create your username and password.

Reddit is anonymous, so your username is what you’ll go by here. Choose wisely—because once you get a name, you can’t change it.

Reset your password

Enter your email address or username and we’ll send you a link to reset your password

Check your inbox

An email with a link to reset your password was sent to the email address associated with your account

Choose a Reddit account to continue