computer science economics phd

Economics and Computer Science Research

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• Social Sciences PhD Program
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Researchers who bridge economics and computer science use rigorous mathematical and computational tools to study financial transactions, economic issues, and the structures of social organizations that have been made exceedingly complex by e-commerce, the Internet age, and other aspects of a wired and faster-paced society. Their work has the potential to lead to new and improved designs for financial markets and systems, network protocols, and political processes.

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PhD Program

Year after year, our top-ranked PhD program sets the standard for graduate economics training across the country. Graduate students work closely with our world-class faculty to develop their own research and prepare to make impactful contributions to the field.

Our doctoral program enrolls 20-24 full-time students each year and students complete their degree in five to six years. Students undertake core coursework in microeconomic theory, macroeconomics, and econometrics, and are expected to complete two major and two minor fields in economics. Beyond the classroom, doctoral students work in close collaboration with faculty to develop their research capabilities, gaining hands-on experience in both theoretical and empirical projects.

How to apply

Students are admitted to the program once per year for entry in the fall. The online application opens on September 15 and closes on December 15.

Meet our students

Our PhD graduates go on to teach in leading economics departments, business schools, and schools of public policy, or pursue influential careers with organizations and businesses around the world. 

Economics and Computation

Long-Term Participants (including Organizers)

Constantinos Daskalakis (Massachusetts Institute of Technology)

Noam Nisan (Hebrew University of Jerusalem)

Christos Papadimitriou (Columbia University)

Tim Roughgarden (Stanford University)

Ilya Segal (Stanford University)

Chris Shannon (UC Berkeley)

Éva Tardos (Cornell University)

Gabriel Carroll (Stanford University)

Xi Chen (Columbia University)

Giorgos Christodoulou (University of Liverpool)

Richard Cole (New York University)

Vincent Conitzer (Duke University)

Xiaotie Deng (Shanghai Jiao Tong University)

Nikhil R. Devanur (Microsoft Research)

Shahar Dobzinski (Weizmann Institute)

Shaddin Dughmi (University of Southern California)

Federico Echenique (California Institute of Technology)

Edith Elkind (University of Oxford)

Michal Feldman (Tel Aviv University)

Amos Fiat (Tel Aviv University)

Paul Goldberg (University of Oxford)

Ramesh Johari (Stanford University)

Ehud Kalai (Northwestern University)

Ravi Kannan (Simons Institute, UC Berkeley)

Anna Karlin (University of Washington)

Elias Koutsoupias (University of Oxford)

Ron Lavi (Technion Israel Institute of Technology)

Stefano Leonardi (Sapienza University of Rome)

Kevin Leyton-Brown (University of British Columbia)

Katrina Ligett (Hebrew University)

Vangelis Markakis (Athens University of Economics and Business)

Hervé Moulin (University of Glasgow)

Evdokia Nikolova (University of Texas at Austin)

Sigal Oren (Ben Gurion University)

Mallesh Pai (University of Pennsylvania)

David Parkes (Harvard University)

Dmitrii Pasechnik (University of Oxford)

Bill Sandholm (University of Wisconsin-Madison)

Ella Segev (Ben-Gurion University of the Negev)

Pingzhong Tang (Tsinghua University)

Adrian Vetta (McGill University)

Bernhard von Stengel (London School of Economics)

Research Fellows

Simina Brânzei (Purdue University)

Yang Cai (Yale University)

Vasilis Gkatzelis (Drexel University)

Yash Kanoria (Columbia University)

Ruta Mehta (University of Illinois, Urbana-Champaign)

Georgios Piliouras (Singapore University of Technology and Design)

Daniela Saban (Stanford Graduate School of Business; Google Research Fellow)

Matt Weinberg (Princeton University; Microsoft Research Fellow)

Lirong Xia (Rensselaer Polytechnic Institute)

Visiting Graduate Students and Postdocs

Hedyeh Beyhaghi (Cornell University)

Zhe Feng (Harvard University)

Kira Goldner (Columbia University)

Nima Haghpanah (Massachusetts Institute of Technology)

Li Han (University of Southern California)

Pooya Jalaly (Cornell University)

Thodoris Lykouris (MIT)

Christos-Alexandros Psomas (UC Berkeley)

Aviad Rubinstein (UC Berkeley)

Manuel Sabin (UC Berkeley)

Nihar Shah (UC Berkeley)

Warut Suksompong (Stanford University)

Sam Wong (UC Berkeley)

James Wright (University of British Columbia)

Haifeng Xu (University of Southern California)

Manolis Zampetakis (UC Berkeley)

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• MIT Homepage

Below is a list of the MIT Schwarzman College of Computing’s graduate degree programs. The Doctor of Philosophy (PhD) degree is awarded interchangeably with the Doctor of Science (ScD).

Prospective students apply to the department or program under which they want to register. Application instructions can be found on each program’s website as well as on the MIT Graduate Admissions website.

Center for Computational Science and Engineering

The Center for Computational Science and Engineering (CCSE) brings together faculty, students, and other researchers across MIT involved in computational science research and education. The center focuses on advancing computational approaches to science and engineering problems, and offers SM and PhD programs in computational science and engineering (CSE).

• Computational Science and Engineering, SM and PhD . Interdisciplinary master’s program emphasizing advanced computational methods and applications. The CSE SM program prepares students with a common core of computational methods that serve all science and engineering disciplines, and an elective component that focuses on particular applications. Doctoral program enables students to specialize in methodological aspects of computational science via focused coursework and a thesis which involves the development and analysis of broadly applicable computational approaches that advance the state of the art.
• Computational Science and Engineering, Interdisciplinary PhD. Doctoral program offered jointly with eight participating departments, focusing on the development of new computational methods relevant to science and engineering disciplines. Students specialize in a computation-related field of their choice through coursework and a doctoral thesis. The specialization in computational science and engineering is highlighted by specially crafted thesis fields. 

Department of Electrical Engineering and Computer Science

The largest academic department at MIT, the Department of Electrical Engineering and Computer Science (EECS) prepares hundreds of students for leadership roles in academia, industry, government and research. Its world-class faculty have built their careers on pioneering contributions to the field of electrical engineering and computer science — a field which has transformed the world and invented the future within a single lifetime. MIT EECS consistently tops the U.S. News & World Report and other college rankings and is widely recognized for its rigorous and innovative curriculum. A joint venture between the Schwarzman College of Computing and the School of Engineering, EECS (also known as Course 6) is now composed of three overlapping sub-units in electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

• Computation and Cognition, MEng*. Course 6-9P builds on the Bachelor of Science in Computation and Cognition to provide additional depth in the subject areas through advanced coursework and a substantial thesis.
• Computer Science, PhD
• Computer Science and Engineering, PhD
• Computer Science, Economics, and Data Science, MEng*. New in Fall 2022, Course 6-14P builds on the Bachelor of Science in Computer Science, Economics, and Data Science to provide additional depth in economics and EECS through advanced coursework and a substantial thesis.
• Computer Science and Molecular Biology, MEng*. Course 6-7P builds on the Bachelor of Science in Computer Science and Molecular Biology to provide additional depth in computational biology through coursework and a substantial thesis.
• Electrical Engineering, PhD
• Electrical Engineering and Computer Science, MEng* , SM* , and PhD . Master of Engineering program (Course 6-P) provides the depth of knowledge and the skills needed for advanced graduate study and for professional work, as well as the breadth and perspective essential for engineering leadership. Master of Science program emphasizes one or more of the theoretical or experimental aspects of electrical engineering or computer science as students progress toward their PhD.
• Electrical Engineer / Engineer in Computer Science.** For PhD students who seek more extensive training and research experiences than are possible within the master’s program.
• Thesis Program with Industry, MEng.* Combines the Master of Engineering academic program with periods of industrial practice at affiliated companies. 

* Available only to qualified EECS undergraduates. ** Available only to students in the EECS PhD program who have not already earned a Master’s and to Leaders for Global Operations students.

Institute for Data, Systems, and Society

The Institute for Data, Systems, and Society advances education and research in analytical methods in statistics and data science, and applies these tools along with domain expertise and social science methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.

• Social and Engineering Systems, PhD. Interdisciplinary PhD program focused on addressing societal challenges by combining the analytical tools of statistics and data science with engineering and social science methods.
• Technology and Policy, SM . Master’s program addresses societal challenges through research and education at the intersection of technology and policy.
• Interdisciplinary Doctoral Program in Statistics . For students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st-century statistics and apply these concepts within their chosen field of study. Participating departments and programs: Aeronautics and Astronautics, Brain and Cognitive Sciences, Economics, Mathematics, Mechanical Engineering, Physics, Political Science, and Social and Engineering Systems.

Operations Research Center

The Operations Research Center (ORC) offers multidisciplinary graduate programs in operations research and analytics. ORC’s community of scholars and researchers work collaboratively to connect data to decisions in order to solve problems effectively — and impact the world positively.

In conjunction with the MIT Sloan School of Management, ORC offers the following degrees:

• Operations Research, SM and PhD . Master’s program teaches important OR techniques — with an emphasis on practical, real-world applications — through a combination of challenging coursework and hands-on research. Doctoral program provides a thorough understanding of the theory of operations research while teaching students to how to develop and apply operations research methods in practice.
• Business Analytics, MBAn. Specialized advanced master’s degree designed to prepare students for careers in data science and business analytics.

Doctoral Program

The Ph.D. program is a full time program leading to a Doctoral Degree in Economics.  Students specialize in various fields within Economics by enrolling in field courses and attending field specific lunches and seminars.  Students gain economic breadth by taking additional distribution courses outside of their selected fields of interest.

General requirements

Students  are required to complete 1 quarter of teaching experience. Teaching experience includes teaching assistantships within the Economics department or another department .

University's residency requirement

135 units of full-tuition residency are required for PhD students. After that, a student should have completed all course work and must request Terminal Graduate Registration (TGR) status.

Department degree requirements and student checklist

1. core course requirement.

Required: Core Microeconomics (202-203-204) Core Macroeconomics (210-211-212) Econometrics (270-271-272).  The Business School graduate microeconomics class series may be substituted for the Econ Micro Core.  Students wishing to waive out of any of the first year core, based on previous coverage of at least 90% of the material,  must submit a waiver request to the DGS at least two weeks prior to the start of the quarter.  A separate waiver request must be submitted for each course you are requesting to waive.  The waiver request must include a transcript and a syllabus from the prior course(s) taken.  

2.  Field Requirements

Required:  Two of the Following Fields Chosen as Major Fields (click on link for specific field requirements).  Field sequences must be passed with an overall grade average of B or better.  Individual courses require a letter grade of B- or better to pass unless otherwise noted.

Research fields and field requirements :

• Behavioral & Experimental
• Development Economics
• Econometric Methods with Causal Inference
• Econometrics
• Economic History
• Environmental, Resource and Energy Economics
• Industrial Organization
• International Trade & Finance
• Labor Economics
• Market Design
• Microeconomic Theory
• Macroeconomics
• Political Economy
• Public Economics

3.  Distribution

Required:  Four other graduate-level courses must be completed. One of these must be from the area of economic history (unless that field has already been selected above). These courses must be distributed in such a way that at least two fields not selected above are represented.  Distribution courses must be passed with a grade of B or better.

4.  Field Seminars/Workshops

Required:  Three quarters of two different field seminars or six quarters of the same field seminar from the list below.   

We have 6 Economics (computer science) PhD Projects, Programmes & Scholarships

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Economics (computer science) PhD Projects, Programmes & Scholarships

Epsrc cdt in machine learning systems, funded phd programme (european/uk students only).

Some or all of the PhD opportunities in this programme have funding attached. It is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full programme details for further information.

EPSRC Centre for Doctoral Training

EPSRC Centres for Doctoral Training conduct research and training in priority areas funded by the UK Engineering and Physical Sciences Research Council. Potential PhD topics are usually defined in advance. Students may receive additional training and development opportunities as part of their programme.

Fully Funded PhD Positions at the IMT School for Advanced Studies Lucca

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Italy PhD Programme

An Italian PhD usually takes 3-4 years and consists of some taught units as well as research towards your thesis. This will be examined at a public defence, rather than a private viva voce. Some programmes are taught in English.

An investigation of Quantum Cognition in Financial Decision Making

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Machine Learning for Energy System Analytics

Digitalisation of energy systems, big data modelling the knowledge economy.

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Harvard John A. Paulson School of Engineering and Applied Sciences

EconCS Group

Research at the intersection between computer science and economics

Yiling Chen

Gordon McKay Professor of Computer Science

Milind Tambe

David C. Parkes

George F. Colony Professor of Computer Science

Ariel Procaccia

Yannai A. Gonczarowski

Assistant Professor of Economics and of Computer Science

PhD Students

Paula rodriguez diaz.

(Advisor: Milind Tambe)

(Advisor: David Parkes)

(Advisor: Yannai A. Gonczarowski)

Bailey Flanigan

(Advisor: Ariel Procaccia)

Lucia Gordon

Daniel halpern, safwan hossain.

(Advisor:  Yiling Chen)

(Advisor:  David Parkes)

Jackson Killian

Hongjin lin, gary qiurui ma, aditya mate, eric mibuari, daniel moroz, aida rahmattalabi, sai srivatsa ravindranath.

(Advisor: David Parkes and Ariel Procaccia)

Sanket Shah

Itai shapira, yonadav shavit.

(Advisors: Ariel Procaccia and Yannai Gonczarowski)

(Advisors:  David Parkes and Hui Chen at MIT)

Tonghan Wang

Jamelle watson-daniels.

(Advisors: David Parkes and Berk Ustun at UCSD)

Bryan Wilder

(Advisors: David Parkes and Munther Dahleh at MIT)

Sonja Johnson Yu

Shirley zhang.

(Advisor: name)

Panayiotis Danassis

Matheus ferreira, jessie finocchiaro, matthias gerstgrasser, francisco marmolejo-cossío, dominik peters, yonatan sompolinsky, xintong wang, manuel wüthrich, anson kahng.

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Economics & Computation

Economics and Computation uses both computational paradigms and economic models to study interactions between self-interested entities. Sometimes also called Algorithmic Game Theory, aspects of the discipline are theoretical, proving theorems after mathematically modeling strategic behavior. Aspects of the discipline are also empirical, analyzing real-world behavior and providing policy guidance.

Algorithmic mechanism design, the design of algorithms to be deployed among self-interested agents, is a particularly active research area at Princeton. This includes matching markets, auction design, and consensus protocol design.

Associated Faculty

• Mark Braverman
• Peter Henderson
• Aleksandra Korolova
• Matthew Weinberg

Associated Graduate Students

• Aadityan Ganesh

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Brown University

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The joint Computer Science-Economics concentration exposes students to the theoretical and practical connections between computer science and economics. It prepares students for professional careers that incorporate aspects of economics and computer technology and for academic careers conducting research in areas that emphasize the overlap between the two fields. Concentrators may choose to pursue either the A.B. or the Sc.B. degree. While the A.B. degree allows students to explore the two disciplines by taking advanced courses in both departments, its smaller number of required courses is compatible with a liberal education. The Sc.B. degree achieves greater depth in both computer science and economics by requiring more courses, and it offers students the opportunity to creatively integrate both disciplines through a design requirement. If you are interested in declaring a concentration in Computer Science-Economics, please refer to this page for more information regarding the process. For more information about the CS Pathways, see this  page.

Standard Program for the Sc.B. degree.

APMA 1650 or APMA 1655 may be used in place of CSCI 1450 in CS pathway requirements. However, concentration credit will be given for only one of APMA 1650 , APMA 1655 , and CSCI 1450 .

Or ECON 1110 with permission. For students matriculating at Brown in Fall 2021 or later, note that if ECON 1110 is used, then one additional course from the mathematical-economics group will be required

Students may apply, at most, one Economics course whose number is in the range of 1000 to 1099 toward the concentration.  Note that ECON 1620 , ECON 1960 , and ECON 1970 (independent study) cannot be used for concentration credit.  However, 1620 and 1960 can be used for university credit and up to two 1970s may be used for university credit.

Standard Program for the A.B. degree:

CSCI 1951K can be counted as one of them, if it has not been used to satisfy the computer science requirements of the concentration and if the student has taken either ECON 1470 or ECON 1870 .

Note that ECON 1620 , ECON 1960 , and ECON 1970 (independent study) cannot be used for concentration credit.  However, 1620 and 1960 can be used for university credit and up to two 1970s may be used for university credit.

Students who meet stated requirements are eligible to write an honors thesis in their senior year.  Students should consult the listed honors requirements of whichever of the two departments their primary thesis advisor belongs to, at the respective departments' websites. If the primary thesis advisor belongs to Economics (Computer Science), then students must have a reader in the Computer Science (respectively, Economics) department.

Professional Track

The requirements for the professional track include all those of the standard track, as well as the following:

Students must complete full-time professional experiences doing work that is related to their concentration programs, totaling 2-6 months, whereby each internship must be at least one month in duration in cases where students choose to do more than one internship experience. Such work is normally done at a company, but may also be at a university under the supervision of a faculty member. Internships that take place between the end of the fall and the start of the spring semesters cannot be used to fulfill this requirement.

On completion of each professional experience, the student must write and upload to ASK a reflective essay about the experience addressing the following prompts, to be approved by the student's concentration advisor:

• Which courses were put to use in your summer's work? Which topics, in particular, were important?
• In retrospect, which courses should you have taken before embarking on your summer experience? What are the topics from these courses that would have helped you over the summer if you had been more familiar with them?
• Are there topics you should have been familiar with in preparation for your summer experience, but are not taught at Brown? What are these topics?
• What did you learn from the experience that probably could not have been picked up from course work?
• Is the sort of work you did over the summer something you would like to continue doing once you graduate? Explain.
• Would you recommend your summer experience to other Brown students? Explain.

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• Administration

Economics and Computer Science

Yannai A. Gonczarowski

Yannai A. Gonczarowski is an Assistant Professor of Economics and of Computer Science at Harvard University—the first faculty member at Harvard to have...

• Economics and Computer Science (1)
• Applied Microeconomics (1)
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• M.S. Economics & Computation

Economics and Computer Science interact in multiple areas. The traditional linkage has been in Numerical Analysis (or “numerical methods”), a standard Computer Science field that is also important to econometricians who write their own code. The same can be said for database analytics – an increasingly important tool as datasets explode in size. More recently, Machine Learning and Artificial Intelligence have become key tools in empirical work in economics, and the drive to link causal inference (an economists’ obsession) with Machine Learning brings the fields together tightly. At the same time, both Mechanism Design, and resulting matching models, as well as Network Theory have emerged as truly interdisciplinary fields – and faculty from both Duke Computer Science and Duke Economics are working in these areas.

The MSEC program combines the strengths of the Departments of Economics and  Computer Science  to educate students in these important computational skills linked to economics, and to prepare them for Ph.D. studies or careers in economics, finance, government, and business. 

This program is designed to meet the needs of students with varied levels of exposure to either field, but a strong quantitative background is recommended.

What Makes Our Program Different?

We offer courses from multiple disciplines and departments, opportunities for teaching and research, a student population with diverse interests, career paths beyond academia, and more!

The MSEC differences:

• Courses everywhere! In nearly all master’s programs, students take a nearly set curriculum with few if any electives – and such electives that do exist tend to be within a single department. In contrast, MSEC students have few restrictions, and can take courses across the university (subject to advisor approval), both at the master’s and PhD levels. And students do! In addition to CS and Econ courses, students typically take courses in Mathematics, Statistics, Public Policy, Environmental Science, Health Policy/Medical School, and Business courses ranging from quantitative marketing to finance to strategy, and more.  
• Research Assistant (RA) opportunities: I n most CS and Econ master’s programs, research receives little or no emphasis, and there are no opportunities to work as an RA. Duke is different: master’s students can work as RA’s, and MSEC students are highly sought after for their mix of skills. Research assistant work is important for learning about the research process, strengthening applied skills and tools, and, in many cases, getting joint publications.  
• Teaching Assistant (TA) opportunities:  In most CS and Econ master’s programs, few if any master’s students have the opportunity to work as a TA. Again, Duke is different: master’s students can work as TA’s, and MSEC students are highly sought after for TA work as well. Being a TA is especially important for those who go on to PhD programs, and above all for international students: being hired as a TA sends a signal that professors have sufficiently high regard for your expository ability that they feel comfortable putting you in front of a class of native speaker undergraduates or MBAs. Moreover, TA work in a graduate course enhances your learning far beyond simply being a student in the class.  
• Diversity of student interests: the MSEC student body has interests that range enormously (see the above list of additional departments in which students take coursework) while sharing common core interests as well. Indeed, the MSEC experience allows student to explore a variety of interests before settling down – and a high fraction of MSEC students change their interests during the course of their study.  
• Industry vs. PhD: roughly 40% of MSEC students go on to doctoral programs, though many in that group will work for a couple years before doing so. We seek a diverse group, and prepare people for both industry and academe. Within industry, MSEC alumni go into tech, finance, marketing, consulting, research, and government. They also go into large established firms as well as – with the enthusiastic support of their advisors – start-ups. As MSEC is a STEM program, international students are highly recruited into private sector jobs in the US.  
• Elite program: MSEC is a tough program – there aren’t that many people who jump at taking graduate coursework in Computer Science and Economics…and often other highly quantitative disciplines as well. But those who do, and who have the requisite background form, well, an elite. MSEC recognizes that, and in turn commits to offering individualized programs and lots of faculty attention. This means keeping the program small, with entry classes in the range of 16-20.

Degree Requirements Summary

• ECON 601 Microeconomics 
• ECON 605 Advanced Microeconomic Analysis
• ECON 701 Microeconomic Analysis I
• ECON 705 Microeconomic Analysis II
• ECON 602 Macroeconomic Theory
• ECON 606 Advanced Macroeconomics II
• ECON 652 Economic Growth
• ECON 656S International Monetary Economics
• ECON 702 Macroeconomic Analysis I
• ECON 706 Macroeconomic Analysis II
• ECON 608 Introduction to Econometrics
• ECON 612 Time Series Econometrics
• ECON 613 Applied Econometrics in Microeconomics
• ECON 703 Econometrics I
• ECON 707 Econometrics II
• Or approved substitutes.
• At least 12 credits in Computer Science (500-level or higher)
• Internship  (optional)
• Any graded graduate computer science course (including independent study) with a significant project component may serve as a capstone course.
• An approved economics capstone course
•   Responsible Conduct of Research  (RCR) training during orientation and 1 RCR forum 2-hour course (either GS 711 or GS712)
• (For International Students)  English Language Proficiency

Course Details

The program requires 30 credits in computer science and economics, or related fields, subject to approval by the program's directors of graduate studies. We expect that students will take four semesters to complete all the requirements. Students must receive a grade of B- or better in the 30 degree course credits. 

It is the policy of The Graduate School that undergraduate courses (499 or lower) do not count towards the M.A. degree or a student's GPA. Courses that are cross-listed as both undergraduate- and graduate-level courses count towards the M.A. degree and a student's GPA only if they have a separate, more rigorous syllabus for graduate students. It is the student's responsibility to verify that this is the case before enrolling in any cross-listed courses.

You have a vast array of courses from many departments to choose from, and that means working with many different professors. We can’t list them all, but some of the people and teams of interest include:

• The Directors of Graduate Studies, Xiaowei Yang in Computer Science and Nelson Sa in Economics. They are the MSEC students’ primary academic advisors.
• The Economics applied micro group is large. Junior faculty in labor, public economics, and social topics such as crime and education, and who tend to work with MSEC students include Bocar Ba , Jason Baron , and  Pengpeng Xiao .
• Michael Pollmann (causal inference and machine learning) merits a separate bullet point since his work is central to many MSEC topics.
• Senior faculty in labor and public economics and who work with MSEC students include Peter Arcidiacono , Patrick Bayer , and  Marjorie McElroy . Macroeconomist David Berger also is close to this group and works with MSEC students...
• …as does micro theorist Huseyin Yildirim . Among the other micro theorists, Rachel Kranton ,who focuses on networks, is of particular interest.
• There is also a large group working in economic development. Those most likely to work with MSEC students include Erica Field and Rob Garlick .
• A final group of Econ faculty we must note is the industrial organization group – of particular interest as well to those interested in business topics such as quantitative marketing and strategy. Those who have worked with MSEC students include Allan Collard-Wexler , Jimmy Roberts , and Daniel Yi Xu .
• On the Computer Science side, Cynthia Rudin gets top billing – she teaches and works with more MSEC students than anyone at Duke. She works on machine learning techniques and causality in a vast array of applied and theoretical topics.
• Both Sudeepa Roy and Alex Volfovsky work closely with Cynthia – and with MSEC students.
• Pankaj Agarwal is right up there with Cynthia as well. His fields include Computational and combinatorial geometry, massive data processing, geographic information systems, ecological modeling, computational molecular biology, and robotics. Professor and Department Chair Jun Yang in database systems and architecture (and computational journalism!) is also important for MSEC students.
• Ashwin Machanavajjhala also attracts and works with MSEC students. His fields include Privacy preserving data analysis, fairness in data science and machine learning (ML) workflows, cryptography and secure computation, and combatting misinformation.
• In algorithms, social choice, and database and numerical analysis, Kamesh Munagala has drawn and worked with many MSEC students, while Debmalya Panigrahi in algorithms and Ron Parr in Bayesian networks, reasoning under uncertainty, Markov decision processes, reinforcement learning, and robotics are also key faculty for MSEC students.
• Carlo Tomasi , who teaches computer vision, is an MSEC student favorite.
• Other faculty of interest include Alexander Hartemink (Computational biology, machine learning, Bayesian statistics, systems biology, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, moral AI, computational neurobiology, classification, feature selection: a recent MSEC student TA’d for computational genomics and transformed their interests…from finance!). And Xiaobai Sun , who teaches numerical analysis, is a key (and helpful!) faculty member for MSEC students, especially those with weaker CS backgrounds.
• Finally, though CS is her secondary field, Becky Steorts (who often teaches STA 602, Bayesian stat), cannot go unmentioned, as she has had an enthusiastic MSEC following. Her research interests include computationally scalable approaches to social science applications, where she focuses on recovering high-dimensional objects from degraded data and determining how to recover the underlying structure. Methods used for this are entity resolution, small area estimation, locality sensitive hashing, and privacy-preserving record linkage as applied to medical studies, fmri studies, human rights violations, and estimation of poverty rates in hard-to-reach domains.

Mentoring relationships with faculty are an important element of the graduate education experience.  Mentoring is most important for students conducting research or other independent work. The Computer Science and Economics Departments both have mentoring statements that are somewhat applicable, but these are largely aimed at PhD students. Nonetheless, you should review these statements here for CS , and below for Economics (open "Faculty Advisor & M.A. Student Relationship" tab, as much of the commentary is highly appropriate, and will not be repeated here).

Given the limited time (3-4 semesters) of the MSEC program, the deep mentoring relationships that are formed during doctoral study are modified at the master’s level. However, an outstanding feature of the MSEC program relative to most if not all peer programs is that a substantial amount of mentoring exists, as do structures for it.

A mentor works with you to form goals that are right for you and to plan how to achieve them.  A mentor also evaluates your work and gives constructive feedback to help you focus your work and be more effective. Your primary mentors are, in approximate order of importance:

• The MSEC Directors of Graduate Study (DGS) in Economics (currently, Nelson Sa) and Computer Science (currently, Xiaowei Yang), who serve as your academic advisors;
• Any faculty in Computer Science and Economics for whom you are a research assistant
• Any faculty in Computer Science and Economics for whom you complete a major research assignment in a class that counts as a Capstone Course and that will be included in your portfolio as meeting the capstone requirement.

In addition, the MSEC program has two additional sources of mentoring:

• The MSEC Alumni Mentoring Team, which consists of 10-12 recent alumni both in industry and academe, and who meet periodically to discuss their career trajectories or to be available to offer career advice
• The Economics Master’s Alumni Advisory (MAAB) Board , which plays a similar role, but consists of more senior alumni and is available to all Economics master’s program students.

This document sets out some rules, responsibilities, and expectations for mentoring in the MSEC program. Its purpose is to guide students and faculty toward effective mentoring relationships that are mutually beneficial and free of conflicts.   Many mentoring interactions occur in the context of your research efforts, which are formalized in a research milestone assessment for the graduate program, and which involves independent work under the guidance and supervision of the faculty.

Completing the Graduate Program

You may view your graduate program as a sequence of steps or milestones in addition to coursework. In a research milestone you conduct some independent academic work in collaboration with a faculty research advisor and possibly others.  You write a paper or program, or organize a research-oriented website, and at oral examination defense you give a presentation about the work and answer questions from your audience. An academic committee of faculty members evaluates the work and certifies successful completion of the milestone.  Your advisor guides you in the work, certifies when you are ready to defend the work, suggests other faculty for your committee, and chairs the committee at the defense.

The MSEC program has a single milestone. You are expected to submit a comprehensive portfolio that includes major papers, computer programs, and reports of internships that you completed during your period of study. The portfolio is then reviewed in advance by a faculty committee that meets with you for an oral examination based on your course projects and other research.

Graduate Program Offices

The graduate program office (DGS office in Computer Science; EcoTeach in Economics) is here to assist you as you progress through your program.  We handle various administrative details for you to manage your funding, receive credit for your work, and complete your degree. The office also manages an administrative process when you enter the program and when you apply to graduate, and also plays a role in courses, exams, internships, fellowships, and other matters.   A designated faculty member from each department serves as Director of Graduate Studies (DGS), and works with a staff assistant (DGSA) and Graduate Program Coordinators.

We ask you to help us help you.  In particular, we expect you to know your degree requirements, plan ahead, follow our administrative instructions carefully, meet all relevant deadlines, and be responsive to our communications with you on your department email address.  In particular, students who get into trouble with meeting a degree requirement often say that they were unaware of what was expected of them, or that their advisor failed to push them to complete it.   It is your responsibility to know the requirements for your graduate program and to work with your advisor to meet them.

You should ask the DGS/EcoTeach office for help when you need it.  We can answer your questions and address situations that might arise.  If you feel that something is not going well or that you are blocked from your goals, then you should talk to us.  We will help make a plan to address the issue and connect you with other resources in the University as needed.

Your communications with the DGS/EcoTeach office are confidential, except that we are mandated to request help from a University office for certain equity issues and risks, such as situations involving harassment or a risk of violence.

In particular, you should contact the DGS/EcoTeach office to help you if you feel that you are treated unfairly or unprofessionally, that others are not meeting their responsibilities to you, that expectations set for you are unclear or unreasonable, or that you are encountering a hostile work environment or other unhealthy or unsafe conditions.  If you prefer, you may instead contact other offices or resources at Duke for help.   For example, you may connect at any time certain Duke University resources for wellness or counseling, or the Office of Institutional Equity, or the Graduate School (TGS), or the Computer Science Department Chair.   These offices and others publish web pages and other outreach to help you find them and understand what services and confidentiality they provide.

The Faculty

The Graduate School (TGS) outlines responsibilities of faculty members and students in mentoring roles and in all of their various roles and interactions.  That document also summarizes responsibilities of the graduate program and TGS, and a process for appeal of grievances to the Chair and Dean if the DGS is unable to resolve the situation. 

To summarize using language from that document, faculty are expected to: respect your interests/goals; assist you in pursuing/achieving them; provide clear expectations on your responsibilities as a student and expectations for the work you undertake with them; evaluate your progress and performance in a timely, regular, and constructive fashion; avoid assigning any duty or activity that is outside your interest or responsibility;  be fair, impartial, and professional in all dealings with you; avoid conflicts of interest; and ensure a collegial learning environment of mutual respect and collaboration.

Naturally, you share the faculty's responsibility by taking the lead for your own success, communicating your needs clearly, being appropriately professional, honorable, and respectful in your dealings with others,  and doing your part to promote a collegial and respectful learning environment for everyone.

In an academic environment, students and faculty are free to choose how to meet their goals and responsibilities to one another.  When you interact with faculty in any of their roles, you must be mindful that they balance their time spent with you against their other responsibilities, goals, and interests.  They choose how much of their time to allocate for you.  Their choices are based in part on the significance of their responsibilities to you in a specific role.  For example, your advisor for a research project may delegate some of their mentoring responsibility to guide your work and monitor your progress to other members of the research group.   Committee members may take a more or less active role depending on the nature of the project and milestone.

You in turn are responsible to make efficient use of the faculty time that you request, and to talk to the DGS office (in Computer Science) or EcoTeach office (in Economics) if you feel that you are not getting sufficient attention.

Faculty advisors assigned to MA students are responsible for assisting them in discovering and participating in appropriate channels of scholarly, professional, and disciplinary exchange; and for helping students develop the professional research, teaching, and networking skills that are required for a variety of career options, both within and outside academia. By doing this, advisors play a crucial role in the development and success of our graduate students, engaging with the next generation of researchers and scholars.

The advisor-advisee relationship is a cooperative partnership that should be based on mutual respect and acceptance of responsibilities. In this document, we describe the main responsibilities of advisors and students, as well as the channels available to resolve problems that can appear in this relationship.

Responsibilities for MA Advisors

An effective academic advisor has the following responsibilities:

• Have basic knowledge of MA program requirements and the Graduate School policies regarding academic milestones.
• Listen to and support an advisee’s scholarly and professional goals.
• Help the advisee develop a timeline for completing academic requirements and meeting professional goals. Take reasonable measures to ensure that this timeline is met.
• Communicate clearly and frequently with an advisee about expectations and responsibilities.
• Meet with an advisee to review progress, challenges, and goals.  Advisors should meet with their students at least once a semester, prior to registration. They should have at least one additional meeting with incoming students at the start of their first semester.
• Encourage openness about any challenges or difficulties that impact the graduate student experience and work with the advisee to resolve any challenges.
• Act as a liaison between the student and the Director of Graduate Studies and the department.
• Be aware of institutional resources that can provide support to advisees in times of academic, professional, and personal challenges and whom you, as an advisor, may consult for further guidance.
• Notify the Director of Graduate Studies if you know or suspect that your advisee is facing significant academic or personal challenges.

Responsibilities for Students

To be an effective advisee, students have the following responsibilities:

• Become familiar with the graduate program requirements and the Graduate School policies regarding academic milestones.
• Work with your advisor to develop a timeline for completing academic requirements and meeting professional goals.
• Devote an appropriate amount of time and energy toward achieving academic excellence and earning the advanced degree in a timely fashion.
• Take the initiative. Be proactive in finding answers to questions and in planning your future steps.
• Meet with their advisors once a semester, before registration. First-year students should also meet with their advisors at the start of their first semester.
• Be honest with your advisors. Alert them about any difficulties you may have about program requirements, normal progress, and performance expectations.
• Be willing to be mentored and open to feedback. Listen and respond appropriately to recommendations from advisors.
• Be mindful of time constraints and other demands imposed on faculty members and program staff.

Problem resolution

As with any other relationship, the advisor-advisee partnership may fail to function as expected. There may be multiple reasons for this. For example, the advisor or the advisee may repeatedly fail to satisfy the responsibilities described earlier; or the advisor and advisee may have a personal conflict that cannot be easily resolved.

These situations should be discussed first with the Director of Graduate Studies, and subsequently, and only if necessary, the Chair of the department. These department representatives will assist in mediating existing problems.

If the departmental efforts to resolve these problems are unsuccessful, students and faculty can refer to the Associate Dean or the Dean of the Graduate School for a formal resolution.

STEM Designation

This degree program classifies as STEM (CIP Code 45.0603: Econometrics and Quantitative Economics), and students in this program can apply for a  24-month STEM extension of F-1 Optional Practical Training (OPT) .

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Algorithmic Economics

The interplay of algorithmic, economic, and social systems is now fundamental to a variety of new services and marketplaces, such as data markets, social networks, electricity markets, cloud computing, and even privacy. Research on Algorithmic Economics at Caltech addresses this by bringing together researchers from economics, computer science, engineering, and mathematics in a truly interdisciplinary environment as part of the Center for Social and Information Sciences . The goal of work in this area is to improve the basic sciences of complex markets and social/communication networks while helping develop our understanding of the emerging interaction between the two. Faculty from CMS and Economics are actively engaged on this topic including Marina Agranov (mechanism design and information uncertainty), Steven Low (electricity markets), Eric Mazumdar (learning in strategic settings), Luciano Pomatto (strategic forecasting and evaluation of risk), Omer Tamuz (strategic behavior in networks), and Adam Wierman (networked markets).

Department of Economics

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Applied Mathematics-Economics Concentration

Economics offers joint concentrations with Applied Math, Computer Science, and Mathematics. The philosophy of this program is to provide sufficient command of mathematical concepts to allow pursuit of an economics program emphasizing modern research problems. Economic theory has come to use more and more mathematics in recent decades, and empirical research in economics has turned to sophisticated statistical techniques. The applied mathematics-economics concentration is designed to reflect the mathematical and statistical nature of modern economic theory and empirical research.

This concentration comes in two flavors, or tracks. The first is the advanced economics track, which is intended to prepare students for graduate study in economics. The second is the mathematical finance track, which is intended to prepare students for graduate study in finance, or for careers in finance or financial engineering. Both tracks of the applied mathematics-economics concentration have A.B. degree versions and Sc.B. degree versions. Also note that for each degree version and track there is a  parallel professional track , which differs from the regular track by requiring completion of two internship or similar experiences. 

It is strongly recommended to those considering applying to a Ph.D. program in economics to write an  honors thesis  or at least to conduct some research with a faculty member that can be credited as a  senior capstone project .  Doing so will help the student obtain a better sense of what scholarly research in economics is like, and should have the extra benefit of leading to a relationship with a faculty member who will know you well enough to write a letter of recommendation for you, an important part of your application package.  We encourage all students in this concentration to write a thesis or complete a capstone project.

Requirements

• A.B. degree in Advanced Economics track
• Sc.B. degree in Advanced Economics track
• A.B. degree in Mathematical Finance track
• Sc.B. degree in Mathematical Finance track

Computer Science-Economics Concentration

The joint computer science-economics concentration exposes students to both theoretical and practical connections between computer science and economics. The intent of this concentration is to prepare students for either academic careers conducting research in areas that emphasize the overlap between the two fields; or professional careers that incorporate aspects of economics and computer technology.

The concentration is offered in two versions, the  A.B.  and the  Sc.B. While the A.B. degree allows students to explore the two disciplines by taking advanced courses in both departments, its smaller number of required courses is compatible with a liberal education. The Sc.B. degree achieves greater depth in both computer science and economics by requiring more courses, and it offers students the opportunity to creatively integrate both disciplines through a design requirement. Also note that for each degree version there is a  parallel professional track , which differs from the regular track by requiring completion of two internship or similar experiences.

• A.B. degree
• Sc.B. degree 

Mathematics-Economics Concentration

Designed to give a background in economic theory plus the mathematical tools needed to analyze and develop additional theoretical constructions. Emphasis is on the abstract theory itself. Like the Applied Math – Economics concentration, this concentration can also prepare a student to go on to the study of economics at the graduate level. Concentrators are urged to write an  honors thesis  or engage in a  capstone research project . 

Secondary Menu

Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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M.S. in Economics and Computation

General info.

• Faculty working with students: 10-12
• Students: 3-7
• Students receiving Financial Aid: 50% (getting partial aid)
• Part time study available: No
• Application terms: Fall
• Application deadline: January 30

Nelson Sa Director of Graduate Studies Department of Economics Duke University Box 90097 Durham, NC  27708-0097

Email: [email protected]

Website:  https://econ.duke.edu/masters-programs/degree-programs/msec

Xiaowei Yang Director of Graduate Studies Department of Computer Science Duke University Box 90129 Durham, NC 27708-0129 Phone: (919) 660-6500

Email:  [email protected]

Website:  https://cs.duke.edu/graduate/ms

Program Description

The Master's Program in Economics and Computation is a joint program between the departments of Economics and Computer Science to train and develop programming skills linked to economics and related areas to prepare graduates for Ph.D. studies or related professions. Students will study both economics and computer science coursework in depth, and must pass a final exam administered by their committees covering a portfolio of learning and research activities carried out during their master’s studies. Numerous opportunities for interdisciplinary research are possible through the connections with scholars at the Fuqua School of Business, Nicholas School of the Environment and Earth Sciences, the Sanford School of Public Policy, the National Institute of Statistical Sciences, the Statistical and Applied Mathematical Sciences Institute, and other departments, institutes, and local universities. Graduates will be awarded an M.S. degree in Economics and Computation.

Because MSEC graduates study sophisticated computational and analytical tools beyond the level covered in undergraduate and professional schools, they have a distinct advantage when proceeding to Ph.D. programs and other careers featuring quantitative analysis and forecasting.

• Economics and Computation: Master's Admissions and Enrollment Statistics
• Economics and Computation: Master's Career Outcomes Statistics

Application Information

Application Terms Available:  Fall

Application Deadline:  January 30

Graduate School Application Requirements See the Application Instructions page for important details about each Graduate School requirement.

• Transcripts: Unofficial transcripts required with application submission; official transcripts required upon admission
• Letters of Recommendation: 3 Required
• Statement of Purpose: Required
• Résumé: Required
• GRE Scores: GRE General Required (Note: GMAT not accepted)
• English Language Exam: TOEFL, IELTS, or Duolingo English Test required* for applicants whose first language is not English *test waiver may apply for some applicants
• GPA: Undergraduate GPA calculated on 4.0 scale required

Department-Specific Application Requirements (submitted through online application) Applicants are required to complete a supplemental questionnaire .

Writing Sample Applicants are recommended, but not required, to submit an original writing sample demonstrating academic and research capabilities.

We strongly encourage you to review additional department-specific application guidance from the program to which you are applying: Departmental Application Guidance

List of Graduate School Programs and Degrees

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Opportunities for Computer Science Graduates in the Field of Economics

I am a cs graduate as the title suggests. I have been working as a data engineering/ science consultant for the past couple of months. And I will hopefully get a public administrative job (civil service) soon.

I am extremely interested in the field of economics and that's why I have got admitted into a masters program in economics. I am very much interested into doing research on the interface of economics and computer science and want to do a PhD in this field.

In short what I want to know is, To what extent might I be at a disadvantage due to my CS background when I am trying to get a PhD position and then hopefully an academic position at a decent university in the field of economics or computational economics? How far will I be behind of my peers with an economics background in the overall job market if everything else on my CV is average?

• computer-science
• career-path
• changing-fields

• I would imagine that there are many areas of economics in which a background in CS would be a benefit rather than a drawback. Identify those areas and target your application accordingly. –  astronat supports the strike Commented Oct 17, 2021 at 18:39
• Is academia.stackexchange.com/questions/44651/… helpful? –  Bryan Krause ♦ Commented Oct 20, 2021 at 0:08

PhD economics is basically an applied statistics degree in most US programs. You will not be behind your peers if you have strong understanding of math through real analysis, good understanding of matrixes and probability, and good proof-writing abilities.

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Not the answer you're looking for browse other questions tagged phd computer-science career-path changing-fields economics ..

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PhD Program

Accelerate progress.

Adapting to rapid change requires unwavering conviction. And that goes double for creating it. Make a global impact and leave the world a better place than you found it. A PhD can get you there.

The Duke Difference

World-class research.

Cultivate new possibilities in computer engineering, engineering physics and microelectronics.

Mentoring, from Day One

An early introduction to research with a team that’s dedicated to your success.

Interdisciplinary Environment

Cross-disciplinary approaches foster innovation. Experience our unique learning and research ecosystems.

Comprehensive Mentorship & Support

Comprehensive mentoring is a cornerstone of the Duke ECE PhD experience. Once admitted, we help you assemble your Advising Team. Your team will include your research adviser, your departmental adviser, the director of graduate studies, a five-member dissertation committee, and the department chair.

Additional High-Value Resources

• Conference and travel support
• Grant supported traineeship programs
• Graduate certificate programs in tissue engineering, nanoscience and photonics

Certificates & Training Programs

Certificate in photonics.

Offered through the Duke Fitzpatrick Institute of Photonics

Certificate in Nanoscience

Offered through the Duke Graduate School

AI for Understanding and Designing Materials

Traineeship for the Advancement of Surgical Technology

Doctor of Philosophy in Electrical and Computer Engineering

The information below is a summary of the formal degree requirements for the PhD in ECE.

Requirements Overview

• Complete approved courses for PhD degree
• Complete  Responsible Conduct of Research  (RCR) training
• Complete the Qualifying Examination (QE)
• Establish and meet with a Supervisory Committee
• Complete the Preliminary Examination
• Complete two Teaching Assistantship assignments
• Prepare and defend a dissertation [ dissertation guidelines ]
• Complete the  Final Examination

For students  matriculating with a bachelor’s degree , a minimum of 10 courses are required, as follows:

• Six graduate-level courses in ECE (500-level or higher)
• Two approved graduate-level technical electives (500-level or higher, technical in nature, and chosen to provide a coherent program of study)
• Two approved electives (chosen to provide a coherent program of study)

For students  matriculating with a master’s degree from another institution , a minimum of five (5) courses are required, as follows:

• Three graduate-level courses in ECE (500-level or higher)
• One approved graduate-level technical elective (500-level or higher, technical in nature, and chosen to provide a coherent program of study)
• One approved elective (chosen to provide a coherent program of study)

A program of study detailing the planned/completed coursework must be approved at the Qualifying Exam (bring to exam with advisor’s signature) and Preliminary Exam stages of the PhD.

Access the  ECE PhD Program of Study

Important Notes:

• Courses must be worth 3 (or more) graduate semester hours
• Courses must be graded (Credit/No Credit or audited courses may not count toward the Program of Study)
• ECE 899 Independent Study can be used to satisfy only the Approved Elective requirement
• Undergraduate Courses (numbered 499 or lower) require DGS and Graduate School permission for enrollment and may have special restrictions
• Overall Program of Study must indicate adequate breadth, including some courses distinctly outside student’s main curricular area and research topic
• Course selection must be formally approved by a student’s adviser and the DGS through the submission and approval of a Program of Study (Qualifying Exam committee approves the first draft version as part of the exam process)
• Student must maintain a 3.0 GPA in order to remain in good standing and to graduate

Qualifying Examination

The purpose of the Qualifying Exam is to assess the potential to succeed in the PhD program by having students demonstrate:

• Reading and deeply understanding three selected papers in the field
• Understanding the strengths and shortcomings of the three papers
• Understanding why the particular problem space defined by the three papers is important
• Generating sound research ideas based on the strengths and shortcomings of the three papers
• Writing and presenting information supporting the points above

Qualifying Exam Details

• Qualifying Exam Guidelines
• QE Student Procedural Guidelines (step-by-step how-to document)
• QE Details Approval/Submission Form

Supervisory Committee

The supervisory committee is formed in preparation for the preliminary examination and must consist of at least five members (including the student’s advisor), at least three of which must be graduate ECE faculty members.

In addition, as required by The Graduate School, at least one (1) member of the committee must be from either another department or a clearly separate field of study within the Duke ECE Department. Committees are proposed using the  Committee Approval Form .

Note:  While the Graduate School’s Committee Approval Form lists a minimum of four (4) committee members, the ECE Department requires five (5) committee members.

Teaching Assistantship

All PhD students must complete two semesters of a Teaching Assistantship (TA) prior to graduation. We provide training before you enter an undergraduate classroom for the first time.

The student is expected to complete this requirement sometime during his or her third through the eighth semester. Teaching Assistantships will be assigned by the DGS based on the background and interests of the student and the current department needs.

Teaching Assistantships are expected to require 10 hours per week on average and may involve such activities as organizing and leading discussion sections, grading homework and quizzes, assisting in the development of course materials, supervising laboratory sessions and so forth.

TA training information »

Preliminary Examination

The preliminary examination, which must be completed by the end of academic year three, consists of (1) a written dissertation research proposal and 2) an oral presentation and defense of this proposal to an approved five-member faculty committee.

The written dissertation research proposal should consist of a 10-page (maximum) report plus appendices providing additional supporting information as well as an anticipated timeline for completion of all PhD degree requirements.

The oral presentation, approximately 45 minutes with extra time allotted for questions posed by the committee throughout and after the presentation, should reflect the contents of the report.

• Preliminary Exam Description
• Preliminary Exam Student Procedural Guidelines
• Graduate School PhD Committee Approval Form
• Preliminary Exam Details Form
• Preliminary Exam Outcome Form
• Preliminary Exam Rubric

Final Examination

The student must follow the Graduate School’s guidelines for submitting the dissertation and scheduling the Final Examination, including submitting the departmental defense announcement to the ECE Graduate Office and uploading the dissertation at least two weeks prior to the defense.

• Final Exam Student Procedural Guidelines
• Graduate School Guide for Electronic Submission of Theses and Dissertations
• Graduate School PhD Committee Change Form
• Final Exam Details Form
• Departmental Defense Announcement

Note:  Details concerning important dates and deadlines, filing of intention to graduate, committee approval, and additional details may be found in the  Graduate Bulletin .

PhD Contacts

Angela Chanh, M.Ed.

Assistant Director of Graduate Studies

Michael E. Gehm

Director of Graduate Studies, Professor of ECE

Kevyn Light

Senior Program Coordinator

Graduate Program Coordinator

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1st-year Ph.D. Student Reimbursement for a Computer Purchase

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All new to Cornell first year Information Science Ph.D. students are allowed a reimbursement for up to $1,500 USD toward the purchase of a laptop computer. This is a  one-time  reimbursement and cannot be used towards any other expenses. Students are eligible to request a reimbursement only after they have matriculated, registered and enrolled in classes, which is typically at the end of August. Students have up to one year from the response deadline of April 15 to purchase a laptop computer and request a reimbursement. After this date the reimbursement offer is voided.

If the computer equipment total is less than $1,500 you will not be given the balance, and for equipment that is more than $1,500 you will be responsible for the amount over the $1,500 cap. All equipment must be purchased at one time, and the receipt(s) submitted all together. Receipts must be in English and if the item(s) are purchased using foreign currency, please convert the amount to US currency.  

For reference, our students in the past have received a 13-inch MacBook Pro with Touch Bar (1.4GHz quadcore Intel Core i5 processor; 256GB SSD storage). This is just a suggestion on the type of laptop you may want to consider purchasing. Students should consult with their advisors if they have doubts on what specifications will be needed to support their research. We expect students to use this money to purchase equipment such as the items listed below:

• Laptop computer
• Desktop computer
• Monitor for a computer
• External Hard Drive
• Noise Canceling Headphones

Items that we will  not  reimburse for are listed below, but this is not limited to this list.  Again, please contact us if you are unsure before purchasing anything. 

• Parts to build your own computer
• Replacement of a stolen or broken piece of technology
• Service contracts (e.g., AppleCare)

A receipt with the total cost of the approved equipment and the  laptop policy form  need to be submitted to Seamus Buxton, [email protected], and the receipt(s) must be in English.

Note:  Students who are currently enrolled in a Ph.D. program at Cornell and are admitted through the Change of Program petition process are not eligible for this reimbursement.  Students should work with their advisor for any equipment purchases that are needed. 

If you are interested in applying, and have questions not answered above, please contact

us at:  [email protected] .  

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Department of Economics

Economics is a broad field that aims to understand why the world works as it does and how government and other interventions might affect well-being. The field is diverse methodologically, encompassing mathematical modeling, data science, and randomized trials as appropriate. It interacts both with other social sciences, as with political science and psychology in the attempt to better understand government and individual behavior, and with the sciences, as with statistics and computer science in developing data analysis techniques.

Economics studies decision-making at the individual level and the aggregate outcomes that result when individuals, firms, institutions and governments interact. It remains concerned with classic topics, such as the causes of business cycles, the effects of industry regulations, and the consequences of tax policies, but also focuses on the diverse social challenges of the developed and developing world: poverty, education, health, the environment, and inequality.  

The Department of Economics offers subjects at multiple levels in the three core areas of the discipline—microeconomic theory, macroeconomics, and econometrics—and specialized subjects in many applied fields, including development economics, environmental economics, health economics, industrial organization, international trade, labor economics, political economy, and public finance.

The department offers several undergraduate programs that prepare students for careers in business, finance, consulting, law and public policy, and for further study. Its doctoral program is frequently ranked as the best in the world.

Bachelor of Science in Economics (Course 14-1)

Bachelor of science in mathematical economics (course 14-2), bachelor of science in computer science, economics, and data science (course 6-14), minor in economics, undergraduate study.

Course 14-1, leading to the Bachelor of Science in Economics , provides students with a breadth and depth of training in economics that is unusual at the undergraduate level. It combines training in technical economics with in-depth exploration of students’ areas of interest. Students choose from a diverse set of upper-level undergraduate subjects and are encouraged to engage in independent research.

The aims of the SB in Economics degree program are threefold: to give students a firm grounding in economic theory and data analysis, to develop in-depth knowledge of particular economic issues, and to develop students’ capabilities for independent research. These aims correspond roughly to the requirements in the Course 14-1 program of theory, statistics and econometrics, electives, and research.

The requirements allow substantial freedom for students in designing individual programs within economics and in balancing the program with subjects in other disciplines. The ample elective slots let students apply their technical skills to develop a deep understanding of whatever interests them, whether that is poverty in developing countries, international trade, game theory, for example. The department recommends that students interested in graduate work in economics build their technical skills with additional subjects in mathematics and computer science. Students can also complement their studies in the major with subjects in political science, history, and other social sciences.

The major is sufficiently flexible that students can transfer into the major or add it as a second major without having taken courses beyond 14.01 Principles of Microeconomics and 14.02 Principles of Macroeconomics in the first two years.  Students typically complete an intermediate micro subject,  14.05 Intermediate Macroeconomics , 14.30 Introduction to Statistical Methods in Economics , and 14.32 Econometric Data Science by the third year. This satisfies the prerequisites for all subjects (including 14.33 Research and Communication in Economics: Topics, Methods, and Implementation ) and prepares students for research on their thesis and in other elective subjects.

The SB in Mathematical Economics is designed for students who desire a deeper mathematical foundation and allows them to concentrate in a subset of economics topics. This program is well suited to students interested in mathematical microeconomic theory or econometrics.  Students will gain the strong mathematical and theoretical preparation needed for subsequent graduate study in economics.

Students majoring in Mathematical Economics start with the same introductory micro and macro courses as 14-1 majors. They go on to take a program that includes rigorous mathematical training in microeconomic theory and econometrics, and substantial coursework in mathematics, including 18.100x Real Analysis, a choice between 18.06 Linear Algebra or 18.03 Differential Equations , and at least one mathematics seminar.

The Department of Electrical Engineering and Computer Science and the Department of Economics offer a joint curriculum leading to a  Bachelor of Science in Computer Science, Economics and Data Science (Course 6-14) . The interdisciplinary major provides  students a  portfolio  of  skills in economics,  computing,  and  data  science that are increasingly valued in both the business world and academia.   The economics and computer science disciplines have a substantial overlap both in their reliance on game theory and mathematical modeling techniques and their use of data analytics.   The economics side of the program includes subjects in microeconomic theory and econometrics and electives that expose students to how economists in various fields use mathematical models and statistical evidence to think about problems.   The computer science side includes a number of subjects that develop complementary knowledge, including the study of algorithms, optimization, and machine learning (which is increasingly integrated with econometrics).   The program also includes coursework in several mathematical subjects, including linear algebra, probability, discrete mathematics, and statistics, which can be taken in various departments.  

The Course 6-14 major is also well suited to students whose primary interest is in game theory and mathematical modeling.   It can prepare students for graduate study in either discipline.

The objective of the minor is to extend the understanding of economic issues beyond the level of the concentration. This is done through specialized analytical subjects and elective subjects that provide an extensive treatment of economic issues in particular areas.

The Minor in Economics consists of six subjects arranged into three levels of study:

For more information regarding admissions or financial aid , contact Julia Martyn-Shah, 617-253-8787. For undergraduate admissions and academic programs , contact Gary King, 617-253-0951. For any other information, contact Megan Miller,  617-253-3807.

Master of Science in Economics

Master of applied science in data, economics, and design of policy, master of engineering in computer science, economics, and data science.

Doctor of Philosophy in Economics

Graduate Study

Admission requirements for graduate study.

The Department of Economics specifies the following prerequisites for graduate study in economics: one full year of college mathematics and an appreciable number of professional subjects in economics for those qualified students who have majored in fields other than economics. Applicants for admission who have deficiencies in entrance requirements should consult with the department about programs to remedy such deficits.

In unusual circumstances, admission may be granted to current MIT students seeking the Master of Science degree. The general requirements for the SM are given in the section on Graduate Education.

The Master of Applied Science in Data, Economics, and Design of Policy is an intensive program consisting of a series of nine subjects plus a capstone experience (a summer internship and a corresponding project report). Students gain a strong foundation in microeconomics, development economics, probability, and statistics; engage with cutting-edge research; and develop practical skills in data analysis and the evaluation of social programs. Student choose between two tracks: International Development (focused on low- and middle-income contexts) and Public Policy (focused on high-income contexts). Only students who have successfully completed the MITx MicroMasters credential in Data, Economics, and Design of Policy in the corresponding track are eligible to apply to the on-campus master’s program.

Email for more information or visit the website .

The Department of Electrical Engineering and Computer Science and the Department of Economics offer a joint curriculum leading to a Master of Engineering in Computer Science, Economics, and Data Science . Computer science and data science provide tools for problem solving, and economics applies those tools to domains where there is rapidly growing intellectual, scholarly, and commercial interest, such as online markets, crowdsourcing platforms, spectrum auctions, financial platforms, crypto currencies, and large-scale matching/allocation systems such as kidney donation and public school choice systems. This joint program prepares students for jobs in economics, management consulting, and finance. Students in the program are full members of both departments, with a single advisor chosen from EECS or Economics based on interests of the student as well as the advisor's interest and expertise in the 6-14 area.

The Master's of Engineering in Computer Science, Economics, and Data Science (Course 6-14P) builds on the foundation provided by the Bachelor of Science in Computer Science, Economics, and Data Science (Course 6-14) to provide both advanced classwork and master's-level thesis work. The student selects (with departmental review and approval) 42 units of advanced graduate subjects, which include two subjects in economics and two subjects in electrical engineering and computer science. A further 24 units of electives are chosen from a restricted departmental list of math electives.

The Master of Engineering degree also requires 24 units of thesis credit. While a student may register for more than this number of thesis units, only 24 units count toward the degree requirement.

Programs leading to the five-year Master of Engineering degree or to the four-year Bachelor of Science degree can be arranged to be identical through the junior year. At the end of the junior year, students with a strong academic record will be offered the opportunity to continue through the five-year master's program. A student in the Master of Engineering program must be registered as a graduate student for at least one regular (non-summer) term. To remain in the program and to receive the Master of Engineering degree, students will be expected to maintain a strong academic record. Admission to the Master of Engineering program is open only to undergraduate students who have completed their junior year in the Course 6-14 Bachelor of Science program.

Financial Support

The fifth year of study toward the Master of Engineering degree can be supported by a combination of personal funds, a fellowship, or a graduate assistantship. Assistantships require participation in research or teaching in the department or in one of the associated laboratories. Full-time assistants may register for no more than two scheduled classroom or laboratory subjects during the term, but may receive academic credit for their participation in the teaching or research program. Support through an assistantship may extend the period required to complete the Master of Engineering program by an additional term or two. Support is granted competitively to graduate students and will not be available for all of those admitted to the Master of Engineering program. If provided, department support for Master of Engineering candidates is normally limited to the first three terms as a graduate student unless the Master of Engineering thesis has been completed, the student has served as a teaching assistant, or the student has been admitted to the doctoral program, in which cases a fourth term of support may be permitted.

For additional information regarding teaching and research programs, contact the EECS Undergraduate Office, Room 38-476, 617-253-4654, or visit the department's website .

Doctor of Philosophy

The Department of Economics offers a Doctor of Philosophy (PhD) in Economics . Students in the doctoral program complete a course of study involving a series of required core subjects in microeconomic theory, macroeconomics, and econometrics; coursework (with a grade of B or better) in two major and two minor fields of study from among those offered by the department; a research paper; and a thesis. The coursework and research paper, completed in the program's first two years, culminate in a general examination. The four fields of study are chosen from advanced economic theory; computation and statistics (minor field only); econometrics; economic development; finance; industrial organization; international economics; labor economics; monetary economics; organizational economics; political economy; and public economics.

Following successful completion of the general examination requirement, the student forms a thesis committee of two or three faculty members. The thesis must meet high professional standards and make a significant original contribution to the student’s chosen research area. The thesis must be approved by the thesis committee and then by an independent faculty member in the department selected by the chair of the Graduate Committee. Upon successful completion of the program, students are awarded the PhD in economics.

There is no required minimum number of graduate subjects in the department. Students must be in residence for a minimum of two years. However, candidates ordinarily need two full academic years of study to complete the core and field of study requirements, and the doctoral thesis typically requires three to four years of additional research effort.

Interdisciplinary Program

Economics and statistics.

The Interdisciplinary Doctoral Program in Statistics provides training in statistics, including classical statistics and probability as well as computation and data analysis, to students who wish to integrate these valuable skills into their primary academic program. The program is administered jointly by the departments of Aeronautics and Astronautics, Economics, Mathematics, Mechanical Engineering, Physics, and Political Science, and the Statistics and Data Science Center within the Institute for Data, Systems, and Society. It is open to current doctoral students in participating departments. For more information, including department-specific requirements, see the full program description under Interdisciplinary Graduate Programs.

Many doctoral students are supported by scholarship and fellowship grants, as well as by teaching and research assistantships.

For more information regarding admissions or financial aid , contact Julia Martyn-Shah, 617-253-8787. For undergraduate admissions and academic programs , contact Gary King, 617-253-0951. For any other information , contact Megan Miller,  617-253-3807.

Faculty and Teaching Staff

Jonathan Gruber, PhD

Ford Professor

Professor of Economics

Head, Department of Economics

David Atkin, PhD

Barton L. Weller (1940) Professor

Associate Head, Department of Economics

Alberto Abadie, PhD

Member, Institute for Data, Systems, and Society

Daron Acemoglu, PhD

Institute Professor

Nikhil Agarwal, PhD

(On leave, fall)

Isaiah Andrews, PhD

Charles E. and Susan T. Harris Professor

Joshua Angrist, PhD

David H. Autor, PhD

Daniel (1972) and Gail Rubenfeld Professor

Abhijit Banerjee, PhD

Ford International Professor

Ricardo J. Caballero, PhD

Victor V. Chernozhukov, PhD

Arnaud Costinot, PhD

David J. Donaldson, PhD

Class of 1949 Professor

Esther Duflo, PhD

Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics

Glenn Ellison, PhD

Gregory K. Palm (1970) Professor

Amy Finkelstein, PhD

John and Jennie S. MacDonald Professor

Drew Fudenberg, PhD

Paul A. Samuelson Professor

Robert S. Gibbons, PhD

Sloan Distinguished Professor of Management

Professor of Applied Economics

Nathaniel Hendren, PhD

Anna Mikusheva, PhD

Edward A. Abdun-Nur (1924) Professor

Stephen Morris, PhD

Peter A. Diamond Professor

Sendhil Mullainathan, PhD

Peter de Florez Professor

Professor of Electrical Engineering and Computer Science

Whitney K. Newey, PhD

Benjamin A. Olken, PhD

Jane Berkowitz Carlton and Dennis William Carlton Professor

Parag Pathak, PhD

Class of 1922 Professor

James M. Poterba, PhD

Mitsui Professor

Drazen Prelec, PhD

Digital Equipment Corp. Leaders for Global Operations Professor of Management

Professor of Management Science

Professor of Brain and Cognitive Sciences

Nancy L. Rose, PhD

Charles P. Kindleberger Professor of Applied Economics

Robert Townsend, PhD

Elizabeth and James Killian (1926) Professor

Ivan Werning, PhD

Robert M. Solow Professor

Michael Whinston, PhD

Society of Sloan Fellows Professor of Management

Alexander Greenberg Wolitzky, PhD

Muhamet Yildiz, PhD

Associate Professors

Martin Beraja, PhD

Pentti Kouri Career Development Professor

Associate Professor of Economics

(On leave, spring)

Simon Jaeger, PhD

Silverman (1968) Family Career Development Professor

Tobias Salz, PhD

Castle Krob Career Development Professor

Frank Schilbach, PhD

Assistant Professors

Ian Ball, PhD

Gary Loveman Career Development Professor

Assistant Professor of Economics

Jacob Moscona, PhD

3M Career Development Assistant Professor of Environmental Economics

Ashesh Rambachan, PhD

Nina Roussille, PhD

Lister Brothers Career Development Professor

Christian Wolf, PhD

Rudi Dornbusch Career Development Professor

Visiting Assistant Professors

Bradley Setzler, PhD

Visiting Assistant Professor of Economics

Senior Lecturers

Sara F. Ellison, PhD

Senior Lecturer in Economics

Professors Emeriti

Olivier Jean Blanchard, PhD

Robert M. Solow Professor Emeritus

Professor Emeritus of Economics

Peter A. Diamond, PhD

Institute Professor Emeritus

Stanley Fischer, PhD

Jeffrey E. Harris, MD, PhD

Jerry A. Hausman, PhD

John and Jennie S. MacDonald Professor Emeritus

Bengt Holmström, PhD

Paul A. Samuelson Professor Emeritus

Professor Emeritus of Applied Economics

Paul L. Joskow, PhD

Elizabeth and James Killian Professor Emeritus

Michael J. Piore, PhD

David W. Skinner Professor Emeritus

Professor Emeritus of Political Economy

Professor Emeritus of Political Science

Richard Schmalensee, PhD

Howard W. Johnson Professor Emeritus

Professor Emeritus of Management

Peter Temin, PhD

Elisha Gray II Professor Emeritus

William C. Wheaton, PhD

Professor Emeritus of Urban Studies and Planning

General Economics and Theory

14.00 undergraduate internship in economics.

Prereq: Permission of instructor U (IAP, Summer) Units arranged [P/D/F] Can be repeated for credit.

For Course 14 students participating in off-campus internship experiences in economics. Before registering for this subject, students must have an employment offer from a company or organization and must identify a Course 14 advisor. Upon completion of the internship, student must submit a letter from the employer describing the work accomplished, along with a substantive final report from the student approved by the MIT advisor. Subject to departmental approval. Consult departmental undergraduate office.

Consult D. Donaldson

14.000 Graduate Internship in Economics

Prereq: Permission of instructor G (IAP, Summer) Units arranged [P/D/F] Can be repeated for credit.

For Course 14 students participating in off-campus internship experiences in economics. Before registering for this subject, students must have an employment offer from a company or organization and must identify a Course 14 advisor. Upon completion of the internship, student must submit a letter from the employer describing the work accomplished, along with a substantive final report from the student approved by the MIT advisor. Subject to departmental approval. Consult departmental graduate office.

Consult I. Andrews

14.001 Data Economics and Development Policy Summer Internship

Prereq: Permission of department G (Fall, Spring, Summer) 0-1-0 units

Provides students in the DEDP Master's program the opportunity to synthesize their coursework and professional experience in development economics and data analysis. In the context of a summer internship, students apply the knowledge gained in the program towards a project with a host organization, typically in the development sector. Students will be supported in finding a suitable opportunity or research project. All internship placements are subject to approval by the program director. Each student must write a capstone project report. Restricted to DEDP MASc students.

14.003 Microeconomic Theory and Public Policy

Subject meets with 14.03 Prereq: 14.01 or permission of instructor G (Fall, Spring) 4-0-8 units

Students master and apply economic theory, causal inference, and contemporary evidence to analyze policy challenges. These include the effect of minimum wages on employment, the value of healthcare, the power and limitations of free markets, the benefits and costs of international trade, the causes and remedies of externalities, the consequences of adverse selection in insurance markets, the impacts of labor market discrimination, and the application of machine learning to supplement to decision-making. Class attendance and participation are mandatory. Students taking graduate version complete additional assignments.

Consult D. Autor, S. Jaeger

14.009 Economics and Society's Toughest Problems

Prereq: None Acad Year 2024-2025: Not offered Acad Year 2025-2026: U (Fall) 1-0-2 units

Should we trade more or less with China? Why are some countries poor, and some countries rich? Why are the 1% getting richer? Should the US have a universal basic income? Why is our society becoming so polarized? What can we do to mitigate climate change? Will robots take all the jobs? Why does racism persist and how can we fight it? What will the world economy look like after the COVID-19 recession? Economics shows you how to think about some of the toughest problems facing society — and how to use data to get answers. Features lectures by MIT's economics faculty, showing how their cutting-edge research can help answer these questions. In lieu of problem sets, quizzes, or other written assignments, students produce materials of their choice (podcasts, TikToks, longer videos) with the view to make a potential audience excited about economics. Subject can count toward the 6-unit discovery-focused credit limit for first-year students.

14.01 Principles of Microeconomics

Prereq: None U (Fall, Spring) 3-0-9 units. HASS-S

Introduces microeconomic concepts and analysis, supply and demand analysis, theories of the firm and individual behavior, competition and monopoly, and welfare economics. Applications to problems of current economic policy.

Consult N. Agarwal, D. Donaldson, S. Ellison, J. Gruber

14.02 Principles of Macroeconomics

Provides an overview of macroeconomic issues including the determination of national income, economic growth, unemployment, inflation, interest rates, and exchange rates. Introduces basic macroeconomic models and illustrates key principles through applications to the experience of the US and other economies. Explores a range of current policy debates, such as the economic effects of monetary and fiscal policy, the causes and consequences of the 2008 global financial crisis, and the factors that influence long-term growth in living standards. Lectures are recorded and available for students with scheduling conflicts.

M. Beraja, R. Caballero, J. Poterba

14.03 Microeconomic Theory and Public Policy

Subject meets with 14.003 Prereq: 14.01 or permission of instructor U (Fall, Spring) 4-0-8 units. HASS-S

14.04 Intermediate Microeconomic Theory

Prereq: Calculus II (GIR) and 14.01 U (Fall) 4-0-8 units. HASS-S

Analysis of consumer and producer decisions including analysis of competitive and monopolistic markets. Price-based partial and general equilibrium analysis. Introduction to game theory as a foundation for the strategic analysis of economic situations. Imperfect competition, dynamic games among firms. Failures of general equilibrium theory and their resolutions: externalities, public goods, incomplete information settings, signaling, screening, insurance, alternative market mechanisms, auctions, design of markets.

14.05 Intermediate Macroeconomics

Prereq: 14.01 and ( 14.02 or permission of instructor) U (Fall) 4-0-8 units. HASS-S

Uses the tools of macroeconomics to investigate various macroeconomic issues in depth. Topics range from economic growth and inequality in the long run to economic stability and financial crises in the short run. Surveys many economic models used today. Requires a substantial research paper on the economics of long-run economic growth.

14.06 Advanced Macroeconomics

Prereq: 14.01 and 14.02 U (Fall) Not offered regularly; consult department 4-0-8 units. HASS-S

Blends a thorough study of the theoretical foundations of modern macroeconomics with a review of useful mathematical tools, such as dynamic programming, optimal control, and dynamic systems. Develops comfort with formal macroeconomic reasoning and deepens understanding of key macroeconomic phenomena, such as business cycles. Goes on to study more specific topics, such as unemployment, financial crises, and the role of fiscal and monetary policy. Special attention to reviewing relevant facts and disentangling them from their popular interpretations. Uses insights and tools from game theory. Includes applications to recent and historical events.

Consult Department Headquarters

14.08 Technical Topics in Economics

Prereq: 14.01 U (Fall, Spring) 4-0-8 units Can be repeated for credit.

Considers technical issues of current research interest in economics.

14.09 Reading Seminar in Economics

Prereq: 14.04 and 14.06 U (Fall, IAP, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

Reading and discussion of particular topics in economics. Open to undergraduate students by arrangement with individual faculty members. Consult Department Headquarters.

D. Donaldson

14.10 Reading Seminar in Economics

Prereq: 14.04 and 14.06 U (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

14.11 Topics in Economics

Prereq: 14.01 U (Fall) Not offered regularly; consult department 4-0-8 units. HASS-S Can be repeated for credit.

Considers issues of current research interest in economics.

14.12 Economic Applications of Game Theory

Prereq: 14.01 and (6.041B, 14.04 , 14.30 , 18.05 , or permission of instructor) U (Fall) 4-0-8 units. HASS-S

Analysis of strategic behavior in multi-person economic settings. Introduction to solution concepts, such as rationalizability, backwards induction, Nash equilibrium, subgame-perfect equilibrium, and sequential equilibrium. Strong emphasis on dynamic games, such as repeated games. Introduction to Bayesian games, focusing on Bayesian Nash Equilibrium, Perfect Bayesian Equilibrium, and signaling games. Applications drawn from microeconomics: imperfect competition, implicit cartels, bargaining, and auctions.

14.121 Microeconomic Theory I

Prereq: 14.04 and permission of instructor G (Fall; first half of term) 3-0-3 units

Covers consumer and producer theory, markets and competition, general equilibrium and the welfare theorems; featuring applications, uncertainty, identification and restrictions models place on data. Enrollment limited; preference to PhD students.

14.122 Microeconomic Theory II

Prereq: 14.121 and permission of instructor G (Fall; second half of term) 3-0-3 units

Introduction to game theory. Topics include normal form and extensive form games, and games with incomplete information. Enrollment limited.

14.123 Microeconomic Theory III

Prereq: 14.121 , 14.122 , and permission of instructor G (Spring; first half of term) 3-0-3 units

Models of individual decision-making under certainty and uncertainty. Additional topics in game theory. Enrollment limited.

D. Fudenberg

14.124 Microeconomic Theory IV

Prereq: 14.123 or permission of instructor G (Spring; second half of term) 3-0-3 units

Introduction to statistical decision theory, incentive contracting (moral hazard and adverse selection), mechanism design and incomplete contracting. Enrollment limited.

A. Wolitzky

14.125 Market Design

Prereq: 14.124 G (Spring) 4-0-8 units

Theory and practice of market design, building on ideas from microeconomics, game theory and mechanism design. Prominent case studies include auctions, labor markets, school choice, prediction markets, financial markets, and organ exchange clearinghouses.

N. Agarwal, P. Pathak

14.126 Game Theory

Prereq: 14.122 G (Spring) 3-0-9 units

Investigates equilibrium and non-equilibrium solution concepts and their foundations as the result of learning or evolution. Studies the equilibria of supermodular games, global games, repeated games, signaling games, and models of bargaining, cheap talk, and reputation.

D. Fudenberg, A. Wolitzky, M. Yildiz

14.127 Advanced Game Theory

Prereq: None G (Fall) 4-0-8 units

For students who plan to do game theory research. Covers the following topics: epistemic foundations of game theory, higher order beliefs, the role and status of common prior assumptions, social networks and social learning, repeated and stochastic games, non-equilibrium learning, stochastic stability and evolutionary dynamics, game theory experiments, and behavioral game theory.

D. Fudenberg, M. Yildiz

14.129 Advanced Contract Theory

Prereq: 14.121 , 14.281 , or permission of instructor G (Spring; first half of term) 3-0-3 units

Presents the contract theory, mechanism design, and general equilibrium theory necessary for an understanding of  a variety of recent innovations: crypto currencies, digital assets; intermediation through digital big techs; central bank digital currency; and decentralized finance (DeFi) versus centralized exchange and contract platforms. Three broad themes: 1) Take stock of new technologies' characteristic features (distributed ledgers and blockchain, e-transfers, smart contacts, and encryption); 2) Translate these features into formal language;  3) Inform normative questions: Should we delegate programmable contacts to the private sector and the role of public authorities. 

Consult R. Townsend

14.13 Psychology and Economics

Subject meets with 14.131 Prereq: 14.01 U (Spring) 4-0-8 units. HASS-S

Introduces the theoretical and empirical literature of behavioral economics. Examines important and systematic departures from the standard models in economics by incorporating insights from psychology and other social sciences. Covers theory and evidence on time, risk, and social preferences; beliefs and learning; emotions; limited attention; and frames, defaults, and nudges. Studies applications to many different areas, such as credit card debt, procrastination, retirement savings, addiction, portfolio choice, poverty, labor supply, happiness, and government policy. Students participate in surveys and experiments in class, review evidence from lab experiments, examine how the results can be integrated into models, and test models using field and lab data. Students taking graduate version complete additional assignments.

F. Schilbach

14.130 Reading Economic Theory

Prereq: 14.121 and 14.451 G (Fall) 2-0-10 units Can be repeated for credit.

Class will read and discuss current research in economic theory with a focus on game theory, decision theory, and behavioral economics. Students will be expected to make one presentation and to read and post comments on every paper by the day before the paper is presented. Permission of the instructor required, and auditors are not allowed.

14.131 Psychology and Economics

Subject meets with 14.13 Prereq: 14.01 G (Spring) 4-0-8 units

Introduces the theoretical and empirical literature of behavioral economics. Examines important and systematic departures from the standard models in economics by incorporating insights from psychology and other social sciences. Covers theory and evidence on time, risk, and social preferences; beliefs and learning; emotions; limited attention; and frames, defaults, and nudges. Studies applications to many different areas, such as credit card debt, procrastination, retirement savings, addiction, portfolio choice, poverty, labor supply, happiness, and government policy. Students participate in surveys and experiments in class, review evidence from lab experiments, examine how the results can be integrated into models, and test models using field and lab data.  Students taking graduate version complete additional assignments.

14.137[J] Psychology and Economics

Same subject as 9.822[J] Prereq: None G (Spring) 4-0-8 units

Examines "psychology appreciation" for economics students. Aims to enhance knowledge and intuition about psychological processes in areas relevant to economics. Increases understanding of psychology as an experimental discipline, with its own distinct rules and style of argument. Topics include self-knowledge, cognitive dissonance, self-deception, emotions, social norms, self-control, learning, mental accounting, memory, individual and group behavior, and some personality and psycho-analytic models. Within each of these topics, we showcase effective and central experiments and discuss their role in the development of psychological theory. Term paper required.

14.147 Topics in Game Theory

Prereq: 14.126 Acad Year 2024-2025: Not offered Acad Year 2025-2026: G (Fall) 4-0-8 units

Advanced subject on topics of current research interest.

14.15[J] Networks

Same subject as 6.3260[J] Subject meets with 14.150 Prereq: 6.3700 or 14.30 U (Spring) 4-0-8 units. HASS-S

Highlights common principles that permeate the functioning of diverse technological, economic and social networks. Utilizes three sets of tools for analyzing networks -- random graph models, optimization, and game theory -- to study informational and learning cascades; economic and financial networks; social influence networks; formation of social groups; communication networks and the Internet; consensus and gossiping; spread and control of epidemics; control and use of energy networks; and biological networks. Students taking graduate version complete additional assignments.

14.150 Networks

Subject meets with 6.3260[J] , 14.15[J] Prereq: 6.3700 or 14.300 G (Spring) 4-0-8 units

14.16 Strategy and Information

Subject meets with 14.161 Prereq: 14.01 or permission of instructor U (Spring) 4-0-8 units. HASS-S

Covers modern applications of game theory where incomplete information plays an important role. Applications include bargaining, auctions, global games, market design, information design, and network economics. Students taking graduate version complete additional assignments.

14.160 Behavioral Economics

Prereq: 14.122 G (Spring) 4-0-8 units

Covers recent theory and empirical evidence in behavioral economics. Topics include deviations from the neoclassical model in terms of (i) preferences (present bias, reference dependence, social preferences), (ii) beliefs (overconfidence, projection bias), and (iii) decision-making (cognition, attention, framing, persuasion), as well as (iv) market reactions to such deviations. Applications will cover a large range of fields, including labor and public economics, industrial organization, health economics, finance, and development economics.

A. Banerjee,  F. Schilbach

14.161 Strategy and Information

Subject meets with 14.16 Prereq: 14.01 or permission of instructor G (Spring) 4-0-8 units

14.163 Algorithms and Behavioral Science

Prereq: ( 14.122 and 14.381 ) or permission of instructor G (Spring) 4-0-8 units

Examines algorithms and their interaction with human cognition.  Provides an overview of supervised learning as it relates to econometrics and economic applications. Discusses using algorithms to better understand people, using algorithms to improve human judgment, and using understanding of humans to better design algorithms.  Prepares economics PhD students to conduct research in the field.

S. Mullainathan, A. Rambachan

14.18 Mathematical Economic Modeling

Prereq: 14.04 , 14.12 , 14.15[J] , or 14.19 U (Spring) 4-0-8 units. HASS-S

Guides students through the process of developing and analyzing formal economic models and effectively communicating their results. Topics include decision theory, game theory, voting, and matching. Instruction and practice in oral and written communication provided. Prior coursework in microeconomic theory and/or proof-based mathematics required. Limited to 18 students.

14.19 Market Design

Prereq: 14.01 U (Fall) 4-0-8 units. HASS-S

Covers the design and operation of organized markets, building on ideas from microeconomic and game theory. Topics may include mechanism design, auctions, matching markets, and other resource allocation problems.

14.191 Independent Research Paper

Prereq: Permission of instructor G (Fall, IAP, Spring, Summer) 0-12-0 units Can be repeated for credit.

Under guidance from a faculty member approved by Graduate Registration Officer, student writes a substantial, probably publishable research paper. Must be completed by the end of a student's second year to satisfy the departmental minor requirement.

14.192 Advanced Research and Communication

Prereq: 14.124 , 14.382 , and 14.454 G (Fall, IAP, Spring) 2-4-6 units Can be repeated for credit.

Guides second-year Economics PhD students through the process of conducting and communicating economic research. Students choose topics for research projects, develop research strategies, carry out analyses, and write and present research papers. Limited to second year Economics PhD students.

14.193 Advanced Seminar in Economics

Prereq: 14.121 and 14.451 G (Fall, Spring, Summer; first half of term) Units arranged Can be repeated for credit.

Reading and discussion of current topics in economics. Open to advanced graduate students by arrangement with individual members of the staff.

Consult Department headquarters

14.195 Reading Seminar in Economics

Prereq: 14.121 G (Fall, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

14.197 Independent Research

Prereq: None G (Fall, IAP, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

Under guidance from a faculty member approved by Graduate Registration Officer, student conducts independent research.

14.198, 14.199 Teaching Introductory Economics

Prereq: None G (Fall, Spring) 2-0-2 units Can be repeated for credit.

Required of teaching assistants in introductory economics ( 14.01 and 14.02 ), under guidance from the faculty member in charge of the subject.

14.198: N. Agarwal, D. Donaldson 14.199: M. Beraja, R. Caballero

14.281 Contract Economics

Prereq: 14.124 or permission of instructor G (Fall) 4-0-8 units

Covers theoretical research on contracts in static as well as dynamic settings. Topics include agency theory, mechanism design, incomplete contracting, information design and costly information acquisition. 

I. Ball, S. Morris

Industrial Organization

14.20 industrial organization: competitive strategy and public policy.

Subject meets with 14.200 Prereq: 14.01 U (Spring) 4-0-8 units. HASS-S

Analyzes the current debate over the rise of monopolies, the strategic behavior and performance of firms in imperfectly competitive markets, and the role of competition policy. Topics include monopoly power; pricing, product choice, and innovation decisions by firms in oligopoly markets; static and dynamic measurement of market performance; and incentives in organizations. Requires regular participation in class discussion and teamwork in a competitive strategy game. Students taking graduate version complete additional assignments.

14.200 Industrial Organization: Competitive Strategy and Public Policy

Subject meets with 14.20 Prereq: 14.01 G (Spring) 4-0-8 units

14.27 Economics and E-Commerce

Subject meets with 14.270 Prereq: 14.01 and ( 6.3700 or 14.30 ) U (Spring) 4-0-8 units. HASS-S

Uses theoretical economic models and empirical evidence to help understand the growth and future of e-commerce. Economic models help frame class discussions of, among other topics, content provision, privacy, piracy, sales taxation, group purchasing, price search, and advertising on the internet. Empirical project and paper required. Students taking graduate version complete additional assignments.

14.270 Economics and E-Commerce

Subject meets with 14.27 Prereq: 14.01 and ( 6.3700 or 14.30 ) G (Spring) 4-0-8 units

14.271 Industrial Organization I

Prereq: None. Coreq: 14.122 and 14.381 G (Fall) 5-0-7 units

Covers theoretical and empirical work dealing with the structure, behavior, and performance of firms and markets and core issues in antitrust. Topics include: the organization of the firm, monopoly, price discrimination, oligopoly, and auctions. Theoretical and empirical work are integrated in each area.

14.272 Industrial Organization II

Prereq: 14.271 G (Spring) 5-0-7 units

Continuation of 14.271 . Focuses on government interventions in monopoly and oligopoly markets, and addresses both competition and regulatory policy. Topics include horizontal merger policy and demand estimation, vertical integration and vertical restraints, and the theory and practice of economic regulation. Applications include the political economy of regulation; the performance of economic regulation; deregulation in sectors including electric power, transportation, and financial services; and pharmaceutical and environmental regulation in imperfectly competitive product markets.

N. Rose, M. Whinston

14.273 Advanced Topics in Industrial Organization

Empirical analysis of theoretically derived models of market behavior. Varied topics include demand estimation, differentiated products, production functions, analysis of market power, entry and exit, vertical relationships, auctions, matching markets, network externalities, dynamic oligopoly, moral hazard and adverse selection. Discussion will focus on methodological issues, including identification, estimation, counter-factual analysis and simulation techniques.

N. Agarwal, T. Salz

Organizational Economics

14.26[j] organizational economics.

Same subject as 15.039[J] Subject meets with 14.260 Prereq: 14.01 Acad Year 2024-2025: Not offered Acad Year 2025-2026: U (Spring) 4-0-8 units. HASS-S

Provides a rigorous, but not overly technical introduction to the economic theory of organization together with a varying set of applications. Addresses incentives, control, relationships, decision processes, and organizational culture and performance. Introduces selected fundamentals of game theory. Students taking graduate version complete additional assignments. Limited to 60.

C. Angelucci

14.260 Organizational Economics

Subject meets with 14.26[J] , 15.039[J] Prereq: None Acad Year 2024-2025: Not offered Acad Year 2025-2026: G (Spring) 4-0-8 units

14.282 Introduction to Organizational Economics

Prereq: 14.124 G (Fall) 5-0-7 units

Begins with survey of contract theory for organizational economists, then introduces the main areas of the field, including the boundary of the firm; decision-making, employment, structures and processes in organizations; and organizations other than firms.

C. Angelucci, R. Gibbons, N. Kala

14.283 Advanced Topics in Organizational Economics I

Prereq: 14.282 G (Spring; first half of term) 2-0-4 units

Builds on the work done in 14.282 to develop more in-depth analysis of topics in the field.

14.284 Advanced Topics in Organizational Economics II

Prereq: 14.282 G (Spring; second half of term) 2-0-4 units

Statistics and Econometrics

14.30 introduction to statistical methods in economics.

Subject meets with 14.300 Prereq: Calculus II (GIR) U (Fall) 4-0-8 units. REST

Self-contained introduction to probability and statistics with applications in economics and the social sciences.  Covers elements of probability theory, statistical estimation and inference, regression analysis, causal inference, and program evaluation. Couples methods with applications and with assignments involving data analysis. Uses basic calculus and matrix algebra.  Students taking graduate version complete additional assignments. May not count toward HASS requirement.

14.300 Introduction to Statistical Methods in Economics

Subject meets with 14.30 Prereq: Calculus II (GIR) G (Fall) 4-0-8 units

Self-contained introduction to probability and statistics with applications in economics and the social sciences. Covers elements of probability theory, statistical estimation and inference, regression analysis, causal inference, and program evaluation. Couples methods with applications and with assignments involving data analysis. Uses basic calculus and matrix algebra. Students taking graduate version complete additional assignments.

14.310 Data Analysis for Social Scientists

Prereq: None G (Spring) Not offered regularly; consult department 4-0-8 units

Introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. Presents essential notions of probability and statistics. Covers techniques in modern data analysis: regression and econometrics, prediction, design of experiment, randomized control trials (and A/B testing), machine learning, data visualization, analysis of network data, and geographic information systems. Projects include analysis of data with a written description and interpretation of results; may involve gathering of original data or use of existing data sets. Applications drawn from real world examples and frontier research. Instruction in use of the statistical package R. Students taking graduate version complete additional assignments.

Consult E. Duflo

14.32 Econometric Data Science

Subject meets with 14.320 Prereq: 14.30 or 18.650[J] U (Fall, Spring) 4-4-4 units. Institute LAB

Introduces regression and other tools for causal inference and descriptive analysis in empirical economics. Topics include analysis of randomized experiments, instrumental variables methods and regression discontinuity designs, differences-in-differences estimation, and regression with time series data. Develops the skills needed to conduct — and critique — empirical studies in economics and related fields. Empirical applications are drawn from published examples and frontier research. Familiarity with statistical programming languages is helpful. Students taking graduate version complete an empirical project leading to a short paper. No listeners. Limited to 70 total for versions meeting together.

A. Mikusheva, J. Angrist

14.320 Econometric Data Science

Subject meets with 14.32 Prereq: 14.300 or 18.650[J] G (Fall, Spring) 4-4-4 units

Introduces regression and other tools for causal inference and descriptive analysis in empirical economics. Topics include analysis of randomized experiments, instrumental variables methods and regression discontinuity designs, differences-in-differences estimation, and regress with time series data. Develops the skills needed to conduct — and critique — empirical studies in economics and related fields. Empirical applications are drawn from published examples and frontier research. Familiarity with statistical programming languages is helpful. Students taking graduate version complete an empirical project leading to a short paper. No listeners. Limited to 70 total for versions meeting together.

14.33 Research and Communication in Economics: Topics, Methods, and Implementation

Prereq: 14.32 and ( 14.01 or 14.02 ) U (Fall, Spring) 3-4-5 units. HASS-S

Exposes students to the process of conducting independent research in empirical economics and effectively communicating the results of the research. Emphasizes econometric analysis of an assigned economic question and culminates in each student choosing an original topic, performing appropriate analysis, and delivering oral and written project reports. Limited to 20 per section.

14.35 Why Markets Fail

Prereq: 14.04 , 14.12 , 14.15[J] , or 14.19 U (Fall) 4-0-8 units. HASS-S

Guides students through the process of developing and communicating economic and data analysis. Discusses topics in which markets fail to provide efficient outcomes or economic opportunity. Topics include health insurance, intergenerational mobility, discrimination, climate change, and more. Instruction and practice in oral and written communication provided. Key course activities include the writing of a term paper conducting original economic analysis and an in-class slide presentation of the work. Limited to 18.

14.36 Advanced Econometrics

Subject meets with 14.387 Prereq: 14.32 or permission of instructor U (Fall) 4-0-8 units

Advanced treatment of the core empirical strategies used to answer causal questions in applied microeconometric research. Covers extensions and innovations relating to econometric applications of regression, machine learning, instrumental variables, differences-in-differences and event-study models, regression discontinuity designs, synthetic controls, and statistical inference.  Students taking graduate version complete an additional assignment.  

14.38 Inference on Causal and Structural Parameters Using ML and AI

Subject meets with 14.388 Prereq: 14.32 U (Spring) 4-0-8 units

Provides an applied treatment of modern causal inference with high-dimensional data, focusing on empirical economic problems encountered in academic research and the tech industry. Formulates problems in the languages of structural equation modeling and potential outcomes. Presents state-of-the-art approaches for inference on causal and structural parameters, including de-biased machine learning, synthetic control methods, and reinforcement learning. Introduces tools from machine learning and deep learning developed for prediction purposes, and discusses how to adapt them to learn causal parameters. Emphasizes the applied and practical perspectives. Requires knowledge of mathematical statistics and regression analysis and programming experience in R or Python.

V. Chernozhukov

14.380 Statistical Method in Economics

Prereq: 14.32 or permission of instructor G (Fall; first half of term) 3-0-3 units

Introduction to probability and statistics as background for advanced econometrics. Covers elements of probability theory, sampling theory, asymptotic approximations, hypothesis testing, and maximum-likelihood methods. Illustrations from economics and application of these concepts to economic problems. Limited to 40 PhD students.

A. Mikusheva, A. Rambachan

14.381 Estimation and Inference for Linear Causal and Structural Models

Prereq: 14.380 and 18.06 G (Fall; second half of term) 3-0-3 units

Explains basic econometric ideas and methods, illustrating with empirical applications. Causal inference is emphasized and examples of economic structural models are given. Topics include randomized trials, regression, including discontinuity designs and diffs-in-diffs, and instrumental variables, including local average treatment effects. Basic asymptotic theory for regression is covered and robust standard errors and statistical inference methods are given. Restricted to PhD students from Courses 14 and 15. Instructor approval required for all others.

14.382 Econometrics

Prereq: 14.381 or permission of instructor G (Spring) 3-0-3 units

Covers key models as well as identification and estimation methods used in modern econometrics. Presents modern ways to set up problems and do better estimation and inference than the current empirical practice. Introduces generalized method of moments and the method of M-estimators in addition to more modern versions of these methods dealing with important issues, such as weak identification. Also discusses the bootstrap. Students gain practical experience by applying the methods to real data sets. Enrollment limited.

14.383 High-Dimensional Econometrics (New)

Prereq: 14.382 or permission of instructor G (Spring; second half of term) 3-0-3 units

Continuation of topics in 14.382 , with specific focus on large dimensional models. Students gain practical experience by applying the methods to real data sets. Enrollment limited.

14.384 Time Series Analysis

Prereq: 14.382 or permission of instructor G (Fall) 5-0-7 units

Studies theory and application of time series methods in econometrics, including spectral analysis, estimation with stationary and non-stationary processes, VARs, factor models, unit roots, cointegration, and Bayesian methods. Enrollment limited.

A. Mikusheva

14.385 Nonlinear Econometric Analysis

Develops a full understanding of and ability to apply micro-econometric models and methods. Topics include extremum estimators, including minimum distance and simulated moments, identification, partial identification, sensitivity analysis, many weak instruments, nonlinear panel data, de-biased machine learning, discrete choice models, nonparametric estimation, quantile regression, and treatment effects. Methods are illustrated with economic applications. Enrollment limited.

A. Abadie, W. Newey

14.386 New Econometric Methods

Prereq: 14.382 G (Spring) 4-0-8 units

Exposes students to the frontier of econometric research. Includes fundamental topics such as empirical processes, semiparametric estimation, nonparametric instrumental variables, inference under partial identification, large-scale inference, empirical Bayes, and machine learning methods. Other topics vary from year to year, but can include empirical likelihood, weak identification, and networks.

14.387 Applied Econometrics

Subject meets with 14.36 Prereq: 14.381 or permission of instructor G (Fall) 4-0-8 units

Advanced treatment of the core empirical strategies used to answer causal questions in applied microeconometric research. Covers extensions and innovations relating to econometric applications of regression, machine learning, instrumental variables, differences-in-differences and event-study models, regression discontinuity designs, synthetic controls, and statistical inference.  Students taking the graduate version complete an additional assignment.  

14.388 Inference on Causal and Structural Parameters Using ML and AI

Subject meets with 14.38 Prereq: 14.381 G (Spring) 4-0-8 units

Provides an applied treatment of modern causal inference with high-dimensional data, focusing on empirical economic problems encountered in academic research and the tech industry. Formulates problems in the languages of structural equation modeling and potential outcomes. Presents state-of-the-art approaches for inference on causal and structural parameters, including de-biased machine learning, synthetic control methods, and reinforcement learning. Introduces tools from machine learning and deep learning developed for prediction purposes, and discusses how to adapt them to learn causal parameters. Emphasizes the applied and practical perspectives. Requires knowledge of mathematical statistics and regression analysis and programming experience in R or Python.

14.39 Large-Scale Decision-Making and Inference (New)

Subject meets with 14.390 Prereq: 14.32 U (Fall) 4-0-8 units. HASS-S

Covers the use of data to guide decision-making, with a focus on data-rich and high-dimensional environments as are now commonly encountered in both academic and industry applications. Begins with an introduction to statistical decision theory, including Bayesian perspectives. Covers empirical Bayes methods, including related concepts such as false discovery rates, illustrated with economic applications. Requires knowledge of mathematical statistics and regression analysis, as well as programming experience in R or Python. Students taking the graduate version submit additional assignments.

14.390 Large-Scale Decision-Making and Inference (New)

Subject meets with 14.39 Prereq: 14.320 G (Fall) 4-0-8 units

14.391 Workshop in Economic Research

Prereq: 14.124 and 14.454 G (Fall) 2-0-10 units Can be repeated for credit.

Develops research ability of students through intensive discussion of dissertation research as it proceeds, individual or group research projects, and critical appraisal of current reported research. Workshops divided into various fields, depending on interest and size.

14.392 Workshop in Economic Research

Prereq: 14.124 and 14.454 G (Spring) 2-0-10 units Can be repeated for credit.

14.399 Seminar in Data Economics and Development Policy

Prereq: Permission of instructor G (Spring) 2-0-10 units

Group study of current topics in development policy and research. Includes student presentations and invited speakers. Restricted to DEDP MASc students.

National Income and Finance

14.41 public finance and public policy.

Subject meets with 14.410 Prereq: 14.01 U (Fall) 4-0-8 units. HASS-S

Explores the role of government in the economy, applying tools of basic microeconomics to answer important policy questions such as government response to global warming, school choice by K-12 students, Social Security versus private retirement savings accounts, government versus private health insurance, setting income tax rates for individuals and corporations. Students taking the graduate version complete additional assignments.

14.410 Public Finance and Public Policy

Subject meets with 14.41 Prereq: 14.01 G (Fall) 4-0-8 units

14.416[J] Asset Pricing

Same subject as 15.470[J] Prereq: None G (Fall) 4-0-8 units

See description under subject 15.470[J] .

L. Schmidt, L. Mota

14.42 Environmental Policy and Economics

Subject meets with 14.420 Prereq: 14.01 U (Spring) Not offered regularly; consult department 4-0-8 units. HASS-S

Introduces key concepts and recent advances in environmental economics, and explores their application to environmental policy questions. Topics include market efficiency and market failure, methods for valuing the benefits of environmental quality, the proper role of government in the regulation of the environment, environmental policy design, and implementation challenges. Considers international aspects of environmental policy as well, including the economics of climate change, trade and the environment, and environmental challenges in developing countries. Students taking graduate version complete additional assignments.

14.420 Environmental Policy and Economics

Subject meets with 14.42 Prereq: 14.01 G (Spring) Not offered regularly; consult department 4-0-8 units

Introduces students to key concepts and recent advances in environmental economics, and explores their application to environmental policy questions. Topics include market efficiency and market failure, methods for valuing the benefits of environmental quality, the proper role of government in the regulation of the environment, environmental policy design and implementation challenges. Also considers international aspects of environmental policy including the economics of climate change, trade and the environment and environmental challenges in developing countries. Students taking graduate version complete additional assignments.

14.43[J] Economics of Energy, Innovation, and Sustainability

Same subject as 15.0201[J] Prereq: 14.01 or 15.0111 U (Fall) Not offered regularly; consult department 3-0-9 units. HASS-S Credit cannot also be received for 15.020

See description under subject 15.0201[J] .

14.44[J] Energy Economics and Policy

Same subject as 15.037[J] Prereq: 14.01 or 15.0111 U (Spring) 4-0-8 units. HASS-S Credit cannot also be received for 14.444[J] , 15.038[J]

Analyzes business and public policy issues in energy markets and in the environmental markets to which they are closely tied. Examines the economic determinants of industry structure and evolution of competition among firms in these industries. Investigates successful and unsuccessful strategies for entering new markets and competing in existing markets. Industries studied include oil, natural gas, coal, electricity, and transportation. Topics include climate change and environmental policy, the role of speculation in energy markets, the political economy of energy policies, and market power and antitrust. Two team-based simulation games, representing the world oil market and a deregulated electricity market, act to cement the concepts covered in lecture. Students taking graduate version complete additional assignments. Limited to 60.

14.440[J] Advanced Corporate Finance

Same subject as 15.473[J] Prereq: None G (Spring) 3-0-9 units

See description under subject 15.473[J] . Primarily for doctoral students in finance, economics, and accounting.

14.441[J] Corporate Finance

Same subject as 15.471[J] Prereq: None G (Spring) 3-0-9 units

See description under subject 15.471[J] .

A. Schoar, D. Thesmar

14.442[J] Advanced Asset Pricing

Same subject as 15.472[J] Prereq: None G (Fall) 3-0-9 units

See description under subject 15.472[J] . Primarily for doctoral students in finance, economics, and accounting.

14.444[J] Energy Economics and Policy

Same subject as 15.038[J] Prereq: 14.01 or 15.0111 G (Spring) 4-0-8 units Credit cannot also be received for 14.44[J] , 15.037[J]

14.448[J] Current Topics in Finance

Same subject as 15.474[J] Prereq: None G (Spring) 3-0-9 units Can be repeated for credit.

See description under subject 15.474[J] . Primarily for doctoral students in accounting, economics, and finance.

Consult J. Alton

14.449[J] Current Research in Financial Economics

Same subject as 15.475[J] Prereq: Permission of instructor G (Fall, IAP, Spring, Summer) 3-0-3 units Can be repeated for credit.

See description under subject 15.475[J] . Restricted to doctoral students.

14.451 Dynamic Optimization Methods with Applications

Prereq: 14.06 and permission of instructor G (Fall; first half of term) 3-0-3 units

Provides an introduction to dynamic optimization methods, including discrete-time dynamic programming in non-stochastic and stochastic environments, and continuous time methods including the Pontryagin maximum principle. Applications may include the Ramsey model, irreversible investment models, and consumption choices under uncertainty. Enrollment limited.

14.452 Economic Growth

Prereq: 14.451 and permission of instructor G (Fall; second half of term) 3-0-3 units

Introduces the sources and modeling of economic growth and income differences across nations. Topics include an introduction to dynamic general equilibrium theory, the neoclassical growth model, overlapping generations, determinants of technological progress, endogenous growth models, measurement of technological progress, the role of human capital in economic growth, and growth in a global economy. Enrollment limited.

D. Acemoglu

14.453 Economic Fluctuations

Prereq: 14.452 and permission of instructor G (Spring; first half of term) 3-0-3 units

Investigation of why aggregate economic activity fluctuates, and the role of policy in affecting fluctuations. Topics include the link between monetary policy and output, the economic cost of aggregate fluctuations, the costs and benefits of price stability, and the role of central banks. Introduction to real business cycle and new Keynesian models. Enrollment limited.

14.454 Economic Crises

Prereq: 14.453 and permission of instructor G (Spring; second half of term) 3-0-3 units

Provides an overview of models of the business cycle caused by financial markets' frictions and shocks. Topics include credit crunch, collateral shocks, bank runs, contagion, speculative bubbles, credit booms, leverage, safe asset shortages, capital flows and sudden stops. Enrollment limited.

R. Caballero

14.461 Advanced Macroeconomics I

Prereq: 14.122 and 14.452 G (Fall) 5-0-7 units

Advanced subject in macroeconomics that seeks to bring students to the research frontier. Topics vary from year to year, covering a wide spectrum of classical and recent research. Topics may include business cycles, optimal monetary and tax policy, monetary economics, banking, and financial constraints on investment and incomplete markets.

M. Beraja, I. Werning

14.462 Advanced Macroeconomics II

Prereq: 14.461 G (Spring) 5-0-7 units

Topics vary from year to year. Often includes coordination failures; frictions in beliefs, such as rational inattention, higher-order uncertainty, certain forms of bounded rationality, heterogeneous beliefs, and ambiguity; implications for business cycles, asset markets, and policy; financial frictions and obstacles to trade; intermediation; liquidity; safe assets; global imbalances; financial crises; and speculation.

14.47[J] Global Energy: Politics, Markets, and Policy

Same subject as 11.167[J] , 15.2191[J] , 17.399[J] Prereq: None U (Spring) Not offered regularly; consult department 3-0-9 units. HASS-S Credit cannot also be received for 11.267[J] , 15.219[J]

See description under subject 15.2191[J] . Preference to juniors, seniors, and Energy Minors.

14.471 Public Economics I

Prereq: 14.04 G (Spring) 4-0-8 units

Theory and evidence on government taxation policy. Topics include tax incidence; optimal tax theory; the effect of taxation on labor supply and savings; taxation and corporate behavior; and tax expenditure policy.

N. Hendren, J. Poterba, I. Werning

14.472 Public Economics II

Prereq: 14.471 G (Fall) 3-0-9 units

Focuses on government expenditures and policies designed to correct market failures and/or redistribute resources. Key topics include theoretical and empirical analysis of insurance market failures, the optimal design of social insurance programs, and the design of redistributive programs.

A. Finkelstein, N. Hendren

14.475 Environmental Economics

Prereq: None G (Spring) 4-0-8 units

Theory and evidence on environmental externalities and regulatory, tax and other government responses to problems of market failure. Topics include cost-benefit analysis; measurement of the benefits of non-market goods; evaluation of the impacts of regulation; and international environmental issues including the economics of climate change and trade and the environment.

International, Interregional, and Urban Economics

14.54 international trade.

Subject meets with 14.540 Prereq: 14.01 U (Fall) 4-0-8 units. HASS-S

Provides an introduction to theoretical and empirical topics in international trade. Offers a brief history of globalization. Introduces the theory of comparative advantage and discusses its implications for international specialization and wage inequality. Studies the determinants and consequences of trade policy, and analyzes the consequences of immigration and foreign direct investment. Students taking graduate version complete additional assignments.

A. Costinot

14.540 International Trade

Subject meets with 14.54 Prereq: 14.01 G (Fall) 4-0-8 units

14.581 International Economics I

Prereq: 14.04 G (Fall) 5-0-7 units

Covers a variety of topics, both theoretical and empirical, in international trade, international macroeconomics, and economic geography. Focuses on general equilibrium analysis in neoclassical economies. Considers why countries and regions trade, and what goods they trade; impediments to trade, and why some countries deliberately erect policy to impede; and implications of openness for growth. Also tackles normative issues, such as whether trade openness is beneficial, whether there are winners and losers from trade and, if so, how they can possibly be identified.

D. Atkin, A. Costinot, D. Donaldson

14.582 International Economics II

Prereq: 14.06 G (Spring) 5-0-7 units

Building on topics covered in 14.581 , revisits a number of core questions in international trade, international macroeconomics, and economic geography in the presence of increasing returns, imperfect competition, and other distortions. Stresses their connection to both macro and micro (firm-level) data for questions related to trade policy, inequality, industrial policy, growth, and the location of economic activities. Focuses on both theoretical models, empirical findings, and the challenging task of putting those two together.

Labor Economics and Industrial Relations

14.64 labor economics and public policy.

Subject meets with 14.640 Prereq: 14.30 or permission of instructor Acad Year 2024-2025: Not offered Acad Year 2025-2026: U (Spring) 4-0-8 units. HASS-S

Provides an introduction to the labor market, how it functions, and the important role it plays in people's lives. Topics include supply and demand, minimum wages, labor market effects of social insurance and welfare programs, the collective bargaining relationship, discrimination, human capital, and unemployment. Completion of or concurrent enrollment in 14.03 or 14.04 ,  and 14.32 recommended. Students taking graduate version complete additional assignments.

14.640 Labor Economics and Public Policy

Subject meets with 14.64 Prereq: 14.300 or permission of instructor Acad Year 2024-2025: Not offered Acad Year 2025-2026: G (Spring) 4-0-8 units

Provides an introduction to the labor market, how it functions, and the important role it plays in people's lives. Topics include supply and demand, minimum wages, labor market effects of social insurance and welfare programs, the collective bargaining relationship, discrimination, human capital, and unemployment. Completion of or concurrent enrollment in 14.03 or 14.04 , and 14.32 recommended. Students taking graduate version complete additional assignments.

14.661 Labor Economics I

Subject meets with 14.661A Prereq: 14.32 and ( 14.03 or 14.04 ) G (Fall) 5-0-7 units

A systematic development of the theory of labor supply, labor demand, and human capital. Topics include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. Particular emphasis on the interaction between theoretical and empirical modeling. No listeners.

D. Acemoglu, J. Angrist, P. Pathak

14.661A Labor Economics I

Subject meets with 14.661 Prereq: 14.32 and ( 14.03 or 14.04 ) G (Fall) 5-0-7 units

Covers the same material as 14.661 but in greater depth. Additional assignments required. Limited to economics PhD students who wish to declare a major field in labor economics.

14.662 Labor Economics II

Subject meets with 14.662A Prereq: 14.32 and ( 14.03 or 14.04 ) G (Spring) 5-0-7 units

Theory and evidence on the determinants of earnings levels, inequality, intergenerational mobility, skill demands, and employment structure. Particular focus on the determinants of worker- and firm-level productivity; and the roles played by supply, demand, institutions, technology and trade in the evolving distribution of income.

D. Autor, S. Jaeger

14.662A Labor Economics II

Subject meets with 14.662 Prereq: 14.32 and ( 14.03 or 14.04 ) G (Spring) 5-0-7 units

Covers the same material as 14.662 but in greater depth. Additional assignments required. Limited to economics PhD students who wish to declare a major field in labor economics.

Economic History

14.70[j] medieval economic history in comparative perspective.

Same subject as 21H.134[J] Prereq: None U (Spring) 3-0-9 units. HASS-S; CI-H

See description under subject 21H.134[J] .

Economic Development

14.73 the challenge of world poverty.

Prereq: None U (Fall) 4-0-8 units. HASS-S; CI-H

Designed for students who are interested in the challenge posed by massive and persistent world poverty. Examines extreme poverty over time to see if it is no longer a threat, why some countries grow fast and others fall further behind, if growth or foreign aid help the poor, what we can do about corruption, if markets or NGOs should be left to deal with poverty, where to intervene, and how to deal with the disease burden and improve schools.

E. Duflo, F. Schilbach

14.74 Foundations of Development Policy

Subject meets with 14.740 Prereq: 14.01 U (Fall) Not offered regularly; consult department 4-0-8 units. HASS-S

Explores the foundations of policy making in developing countries, with the goal of spelling out various policy options and quantifying the trade-offs between them. Topics include education, health, fertility, adoption of technological innovations, financial markets (credit, savings, and insurance), markets for land and labor, political factors, and international considerations (aid, trade, and multinational firms). Some basic familiarity with probability and/or statistics is useful for this class. Students taking graduate version complete additional assignments.

14.740 Foundations of Development Policy

Subject meets with 14.74 Prereq: 14.01 G (Fall) Not offered regularly; consult department 4-0-8 units

14.75 Political Economy and Economic Development

Subject meets with 14.750 Prereq: 14.01 U (Spring) 4-0-8 units. HASS-S

Explores the relationship between political institutions and economic development, covering key theoretical issues as well as recent empirical evidence. Topics include corruption, voting, vote buying, the media, and war. Discusses not just what we know on these topics, but how we know it, covering how to craft a good empirical study or field experiment and how to discriminate between reliable and unreliable evidence.  Some basic familiarity with probability and/or statistics is useful for this class.  Students taking graduate version complete additional assignments.

A. Banerjee, B. Olken

14.750 Political Economy and Economic Development

Subject meets with 14.75 Prereq: 14.01 G (Spring) 4-0-8 units

Explores the relationship between political institutions and economic development, covering key theoretical issues as well as recent empirical evidence. Topics include corruption, voting, vote buying, the media, and war. Discusses not just what we know on these topics, but how we know it, covering how to craft a good empirical study or field experiment and how to discriminate between reliable and unreliable evidence. Some basic familiarity with probability and/or statistics is useful for this class.  Students taking graduate version complete additional assignments.

14.76 Firms, Markets, Trade and Growth

Subject meets with 14.760 Prereq: 14.01 and ( 14.30 or permission of instructor) U (Spring) 4-0-8 units. HASS-S

Examines how industrial development and international trade have brought about rapid growth and large-scale reductions in poverty for some developing countries, while globalization has simply increased inequality and brought little growth for others. Also considers why, in yet other developing countries, firms remain small-scale and have not integrated with global supply chains. Draws on both theoretical models and empirical evidence to better understand the reasons for these very different experiences and implications for policy. Students taking graduate version complete additional assignments.

D. Atkin, D. Donaldson

14.760 Firms, Markets, Trade and Growth

Subject meets with 14.76 Prereq: 14.01 and ( 14.30 or permission of instructor) G (Spring) 4-0-8 units

14.770 Introduction to Collective Choice and Political Economy

Broad introduction to political economy. Covers topics from social choice theory to political agency models, including theories of voter turnout and comparison of political institutions.

A. Banerjee, B. Olken, A. Wolitzky

14.771 Development Economics: Microeconomic Issues

Prereq: 14.121 and 14.122 G (Fall) 5-0-7 units

A rigorous introduction to core micro-economic issues in economic development, focusing on both key theoretical contributions and empirical applications to understand both why some countries are poor and on how markets function differently in poor economies. Topics include human capital (education and health); labor markets; credit markets; land markets; firms; and the role of the public sector.

E. Duflo, B. Olken

14.772 Development Economics: Macroeconomics

Prereq: 14.121 and 14.451 G (Spring) 5-0-7 units

Emphasizes dynamic models of growth and development. Topics include migration, modernization, and technological change; static and dynamic models of political economy; the dynamics of income distribution and institutional change; firm structure in developing countries; development, transparency, and functioning of financial markets; privatization; and banks and credit market institutions in emerging markets. Examines innovative yet disruptive digital technologies, including blockchain, digital assets, crypto currency, distributed ledgers, and smart contracts.

D. Atkins, A. Banerjee, R. Townsend

14.773 Political Economy: Institutions and Development

Economists and policymakers increasingly realize the importance of political institutions in shaping economic performance, especially in the context of understanding economic development. Work on the determinants of economic policies and institutions is in its infancy, but is growing rapidly. Subject provides an introduction to this area. Topics covered: the economic role of institutions; the effects of social conflict and class conflict on economic development; political economic determinants of macro policies; political development; theories of income distribution and distributional conflict; the efficiency effects of distributional conflict; the causes and consequences of corruption; the role of colonial history; and others. Both theoretical and empirical approaches discussed. Subject can be taken either as part of the Development Economics or the Positive Political Economy fields.

D. Acemoglu, A. Banerjee, J. Moscona

14.775 Comparing Societies (New)

Studies the cultural, social, and institutional foundations of societies around the world, emphasizing fundamentals and mechanisms that are outside the scope of traditional models in economics. Topics include social organization, perceptions of reality (e.g., the spiritual and meta-human world), drivers of innovation and technology diffusion, conflict, determinants of fertility and population growth, moral frameworks (e.g., views about right/wrong, fairness, equality, and community membership), religion, objectives and definitions of success, and societal equilibria. Emphasizes how research ranging from economic theory to development and policy design can benefit from an understanding of these vast differences that exist around the world. Also considers how these differences affect and are affected by culture, formal institutions, and development. Open to PhD students.

J. Moscona, N. Nunn, J. Robinson

14.78[J] Shaping the Future of Technology: From Early Agriculture to Artificial Intelligence

Same subject as 15.238[J] Prereq: None Acad Year 2024-2025: Not offered Acad Year 2025-2026: U (Spring) 4-0-8 units. HASS-S; CI-H

Provides a framework for thinking about major technological transitions over the past 12,000 years as a means to explore paths to a better future. Discusses who gains or loses from innovation and who can shape the future of artificial intelligence, biotech, and other breakthroughs. Introduces major questions tackled by researchers and relevant to economic policy through faculty lectures, interactive events with prominent guests, and group work. Instruction and practice in oral and written communication provided.

D. Acemoglu, S. Johnson

14.THG Graduate Thesis

Prereq: Permission of instructor G (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

Program of research and writing of thesis; to be arranged by the student with advising committee.

14.THU Thesis

Prereq: 14.33 U (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

Program of research and writing of thesis.

14.UR Undergraduate Research

Prereq: 14.02 U (Fall, IAP, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

Participation in research with an individual faculty member or research group, independent research or study under the guidance of a faculty member. Admission by arrangement with individual faculty member.

14.URG Undergraduate Research

Prereq: 14.02 U (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

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• Faculty and Staff

University of Hawai‘i ® at Mānoa 2024-2025 General Catalog

College of natural sciences: information and computer sciences.

• College of Natural Sciences
• Information and Computer Sciences
• Mathematics
• School of Life Sciences

College of Natural Sciences POST 317 1680 East-West Road Honolulu, HI 96822 Tel: (808) 956-7420 Fax: (808) 956-3548 Web: ics.hawaii.edu

* Graduate Faculty

*S. P. Robertson, PhD (Chair)—human-computer interaction, sociotechnical systems, civic tech, digital government and digital democracy *K. Baek, PhD—computer vision, machine learning, bioinformatics *M. Belcaid, PhD—data science education, big data approximation, probabilistic programming in genomics *E. Biagioni, PhD—networks, systems, languages *K. Binsted, PhD—artificial intelligence, software design for mobile devices, human-computer interaction, human space exploration *H. Casanova, PhD—high performance computing, distributed systems *M. E. Crosby, PhD—human-computer interaction, cognitive science, augmented cognition *B. Endicott, PhD—cyber-security *P. Johnson, PhD—software engineering, serious games, renewable energy *J. Leigh, PhD—big data visualization, virtual reality, high performance networking, human augmentics, video game design *D. Li, PhD—security, privacy and performance in systems, software, networks and databases *C. A. Moore, PhD—software engineering, application development: software quality *M. B. Ogawa, PhD—educational specialist *D. Pavlovic, PhD—security, software, search and networks, quantum computation *A. Peruma, PhD—software quality, software maintenance and evolution, program comprehension, identifier naming, mobile application quality *G. Poisson, PhD—bioinformatics *P. Sadowski, PhD—machine learning and artificial intelligence, deep learning in the natural sciences *P-M. Seidel, DrEng habil—formal methods, computer arithmetic, computer architecture, algorithms *N. Sitchinava, PhD—algorithms and data structures, parallel and distributed computation, I/O- and cache-efficient computation *D. Suthers, PhD—human-computer interaction, computer-supported collaborative learning, technology for education, socio-technical networks and online communities *P. Washington, PhD—digital health, precision health, data science, machine learning, human-centered computing, biomedical informatics

Cooperative Graduate Faculty

R. Gazan, PhD—social aspects of information technology F. N. Kazman, PhD—software architecture design and analysis, software engineering economics S. Still, PhD—machine learning, information theory F. Zhu, PhD—dynamics and control, robotics, intelligent systems

Affiliate Graduate Faculty

L. Altenberg, PhD—computational intelligence, theoretical evolutionary biology B. Auerhheimer, PhD—software engineering A. Koniges, PhD—high performance computing, machine learning D. R. Stoutemyer, PhD—computer algebra, mathematical software D. Streveler, PhD—medical informatics

Emeritus Faculty

D. Chin, PhD—user modeling, natural language processing, AI for games V. Harada, PhD—school library administration, information literacy S. Itoga, PhD—database system, expert system and logic programming D. Pager, PhD—compilers

Degrees Offered: BBS (including minor) in computer science, Undergraduate Certificate in Creative Computational Media, Undergraduate Certificate in Data Science, MS in computer science, PhD in computer science, and PhD in communication and information sciences (interdisciplinary)

The Academic Program

Information and computer sciences (ICS) is the study of the description and representation of information and the theory, design, analysis, implementation, and application of algorithmic processes that transform information. Students majoring in ICS will learn to use computer systems, a valuable skill which can be applied in all fields of study. Students will also learn the scientific principles and technology required to develop new computer systems and applications. The curriculum covers all major areas of computer science with special emphasis on software engineering, computer networks, artificial intelligence, human-computer interaction, bioinformatics, security science (UH Mānoa is an NSA/DHS designated Center of Academic Excellence in Cyber Defense Research), data science, machine learning, and areas uniquely suited to Hawai‘i’s role as a multicultural and geographical center of the Pacific.

Undergraduate Study

Bachelor’s degree.

To be admitted into the program, first-year students entering UH Mānoa directly from high school must first be admitted into the College of Natural Sciences. For continuing students, a cumulative GPA of at least 2.0 is required for admission.

The minimum required grade for prerequisites is a grade of C (not C-) or better, unless otherwise specified.

For information on a Bachelor Degree Program Sheet, go to programsheets/ .

BA in Information and Computer Sciences

Requirements.

Students pursuing these degrees are required to submit a short proposal listing the courses they intend to take to complete their ICS major. An ICS faculty advisor must approve this proposal in writing. Samples of course proposals are available at the ICS department office.

Students must complete the following related courses for all BA and BS degrees: (MATH 215 or 241 or 251A) and (MATH 216 or 242 or 252A).

There are two BA degree options you can choose from:

Bachelor of Arts in Information and Computer Sciences, Security Science (SecSci) Track

Students must complete the following courses (51 credits):

• Core: ICS 111, 141, 211, (212 or 215), 241, 311, 314, 321, 332
• Track: ICS 222, 355, (ICS 351 or 451)
• Four electives from: ICS 423, 425, 426, 428, 455, 495, ECE 406

Substitution allowed: ECE 367 for ICS 311.

Bachelor of Arts in Information and Computer Sciences, Creative Computational Media (M) Track

Students must complete the following courses (61-62 credits):

• Core ICS 110D, 111, 211, 212, 235, 311, 314, 321, 355, 369, 481, 487
• MATH 301, (307 or 311), 372
• AOC: Four electives from: ICS 464, 482, 484, 485, 486, 488, 489, 496 in CCM

Substitution allowed: (ICS 141 and 241) can be a substitution for (MATH 301 and 372). Substitution allowed: ECE 367 for ICS 311.

BS in Computer Science

Substitutions are permitted with the written approval of an ICS faculty advisor. Waiver of certain requirements, such as by Advanced Placement CS Exam, must be approved by the ICS faculty advisor.

There are three BS degree options you can choose from:

Bachelor of Science in Computer Science

Students must complete the following courses (57 credits)

• ICS 111, 141, 211, 212, 241, 311, 314, 321, 332, 355, 496, (MATH 307 or MATH 372) (if students take MATH 307, then they should take MATH 242 as Calculus II prerequisite)
• Two of (ICS 312 or 331), (ICS 313 or 361), (ICS 351 or 451)
• At least four ICS or other approved courses at the 400 level or above

Substitution allowed: (MATH 301 and 372) can be a substitution for (ICS 141 and 241). In that case, students must take MATH 307. Substitution allowed: ECE 367 for ICS 311.

Bachelor of Science in Information and Computer Science, Creative Computational Media Track

• ICS 110D, 111, 211, 212, 235, 311, 314, 321, 355, 369, 481, 487, 488, 496 in CCM
• Two electives (400-level or above) in an area relevant to CCM. The courses may include ICS courses or courses from other departments as long as they are approved by an ICS advisor and meet the minimum total of 6 credit hours

Bachelor of Science in Computer Science, Security Science (SecSci) Track

Students must complete the following courses (54 credits):

• ICS 111, 141, 211, 212, 241, 311, 314, 321, (312 or 331 or 332), (MATH 307 or 372) (If students take MATH 307, then they should take MATH 242 as calculus II prerequisite)

Bachelor of Science in Computer Science, Data Science Track

Students must complete the following courses (57 credits):

• ICS 111, 211, 212, 235, 311, 314, 321, 355, 434, 435, 438, 484
• MATH 301, 307, 372
• Three electives (400-level or above) in an area relevant to Data Science. The courses may include ICS courses or courses from other departments as long as they are approved by an ICS advisor and meet the minimum total of 9 credit hours.

Substitution allowed: (ICS 141 and 241) can be a substitution for MATH 301 in the Data Science Track only. Substitution allowed: ECE 367 for ICS 311.

A cumulative GPA of at least 2.0 and a grade of C (not C-) or higher in ICS 111 are required for admission.

Students must complete ICS 211, 212, and 241 and their prerequisites, 111 and 141, and three ICS courses at the 300 level and above with a grade of C (not C-) or better.

Undergraduate Certificate in Creative Computational Media

The Undergraduate Creative Computational Media (CCM) Certificate Program provides students and industry professionals with training necessary to enter exciting and lucrative immersive media job markets, such as video game and eSports design and development, digital film production and special effects, new media theatre and dance performance, interactive digital media installation development, and exhibit design for museums, theme parks, or marketing/advertising.

CCM Certificate is offered in collaboration with ACM: The School of Cinematic Arts (CINE) and the Department of Theatre & Dance (Arts and Humanities), the Department of Electrical Engineering (College of Engineering), and the Department of Information and Computer Sciences (ICS) (College of Natural Sciences).

Students must complete 18 credits of required and elective courses with a minimum of 9 credits from upper division courses and a cumulative GPA of 2.5 for the certificate courses taken.

Prerequisites (3 credits)

• ICS 110 (Alpha) or ICS 111 or ECE 160

Required Courses (9 credits)

• CINE 215, ICS/ECE 369, ICS 486/CINE 419

Elective Courses (9 credits)

• CINE 216, 255, 315, 316B, 317, 321, 325, ICS 464, ICS/ CINE/DATA 484, ICS 485/CINE 487, DNCE 362, 673

Additional electives identified by students may be considered through a petitioning process, whose approval can be conducted in collaboration with the affected departments.

Undergraduate Certificate in Data Science

The Undergraduate Data Science (DS) Certificate program provides students and industry professionals with training in modern computational tools for manipulating, visualizing, and extracting insights from data. This programming-intensive program prepares students to work in the high-demand, lucrative field of data science.

The DS Certificate is offered by the Department of Information and Computer Sciences (ICS), in collaboration with the Hawai‘i Data Science Institute and other data-intensive departments at UH Mānoa.

Prerequisites and Eligibility

• Applicants must have completed a calculus course that covers limits, derivatives, partial derivatives, and integrals.
• Applicants must have completed a programming course that covers basic data types, program control structure, and functions.
• Applicants must have a minimum GPA of 3.0.
• Applicants who already have a BS in Computer Science with the DS specialization or a BS in Mathematics with the DS specialization are ineligible for the certificate. Students with a certificate in Data Science from UH Hilo are similarly ineligible.

Students must complete 18 credits of required and elective courses.

Required Courses (12 credits)

• ICS 235, 434, 435, 484

At the discretion of the DS Program Committee, students who demonstrate proficiency in the topics covered in the required courses may substitute those courses with elective courses.

Elective Courses (6 credits)

• ATMO/CEE/SUST 449, BIOL/MBBE 483, PHYS 305, MATH 372 or MATH 472 (Due to overlap, cannot use both), ECON 425, 427, ICS/DATA 422, ICS/DATA 438

Combined Bachelor of Arts in Information & Computer Sciences and Master of Library and Information Science (MLISc)

The combined BA/MLISc is intended to allow students who wish to apply their technical skills to professional information service environments to complete the BA in ICS and the MLISc in Library & Information Science in 5 years, plus one summer course. To be admitted into the program, students must submit the Graduate Admissions Application as well as all required program admission materials specified in the “Graduate Study” section by the start of their junior year (5th semester).

Students pursuing this combined degree should meet the degree requirements for the BA in ICS and MLISc.

• Gateway course: ICS 311, with a grade of B or better.

The following courses can be double-counted in BA in ICS and MLISc. The minimum grade requirement for LIS 601 is B (not B-) or better.

• LIS 601, 605, 630

Combined Bachelor of Science and Master of Science in Computer Science

The combined BS/MS degree pathway is intended to allow students the opportunity to complete both a Bachelor of Science and Master of Science in Computer Science in 5 years. To be admitted into the program, students must submit the Graduate Admissions Application and fee as well as all required program admission materials by the deadline. Applications should be submitted in the spring of their junior year (6th semester), with admission to the BAM program commencing in the fall of their senior year (7th semester).

Students pursuing this degree should meet the degree requirements for regular Master of Science in Computer Science. Gateway course: ICS 311 with a grade B or higher. The minimum grade requirement is B (not B-) or higher.

There are three pathways students can take depending on their BS degree option. Each pathway differs in the set of courses that can be double-counted for both the bachelor’s and master’s degree.

BS and MS in Computer Science

The following courses can be double-counted in BS in Computer Science and MS in Computer Science.

• ICS (414 or 435 or 451 or 466), 621, 635

BS in Computer Science in Data Science and MS in Computer Science

The following courses can be double-counted in BS in Computer Science in Data Science track and MS in Computer Science.

• ICS (422 or 475 or 483 or 496), 621, 635

BS in Computer Science in Security Science and MS in Computer Science

The following courses can be double-counted in BS in Computer Science in Security Science track and MS in Computer Science.

• ICS (426 or 455 or 495), 621, 623

Graduate Study

The department offers the MS degree in computer science, and the PhD degree in computer science. The department is one of four academic programs that cooperate in an interdisciplinary doctoral program in communication and information sciences (see the “Communication and Information Sciences” section for more information).

Applicants from foreign countries must be academically qualified, proficient in English (TOEFL or IETLS with scores above the minimum required by Graduate Division, with the additional requirement that TOEFL scores be 580/237/92 or above for admission to the MS program, and 600/250/100 or above for admission to the PhD program, where scores are listed as paper/computer/internet), and sufficiently financially supported.

The department offers three forms of financial aid: teaching assistantships, research assistantships, and tuition waivers. The department offers a limited number of assistantships each semester, most of which are teaching assistantships. Teaching and research assistants work approximately 20 hours per week under the supervision of a faculty member and receive a stipend as well as a tuition waiver. Teaching assistants support instruction and research assistants support extramurally funded research projects. Teaching assistantships are awarded to those applicants who can best support the instructional program. Similarly, research assistantships are awarded to those applicants who can best assist faculty with their research projects. Applicants accepted for admission may be eligible for partial financial aid in the form of a tuition waiver from Graduate Division and foreign applicants from Pacific or Asian countries may be eligible for Pacific-Asian Scholarships. Prior to submitting a tuition waiver application form, foreign applicants must submit TOEFL/IETLS scores and documentation of financial support for expenses other than tuition to Graduate Division Student Services. To apply for any of these forms of support, students should submit the ICS Financial Aid Application (form on the ICS website) in addition to other required application materials. Because we can offer assistance to only a small fraction of applicants, we highly encourage students to also seek other forms of support, such as the EastWest Center or other scholarships or forms of employment.

Master’s Degree

The master’s program is intended for students planning to specialize in computer science or to apply computer science to another field. Applicants who do not possess an undergraduate degree in computer science from an accredited institution will need to complete equivalent course work.

Plan A (thesis) and Plan B (non-thesis) are available. A minimum of 31 credit hours is required under both plans. A minimum B average must be maintained in all courses.

Plan A (Thesis) Requirements

• At least six ICS graduate courses, i.e. courses with numbers between ICS 600 and 691, with the exception of ICS 690;
• Two additional elective 600-level courses must be taken either from the ICS department or some related discipline on a topic related to computer science. Elective courses must have prior approval from the ICS graduate chair as to the suitability prior to enrollment in the courses;
• Up to two of the graduate courses may be replaced by regular ICS 400-level courses (not ICS 499), taken after enrolling in the ICS graduate program;
• Thesis research taken as 6 credits of ICS 700 is required for the degree. These credits are typically taken close to or during the final semester in the program;
• ICS 690 (taken for CR/NC) in the first year of the program.

Plan B (Non-thesis) Requirements

• At least six ICS graduate courses, i.e. courses with numbers between ICS 600 and ICS 691, with the exception of ICS 690.
• A final project ending with a required written report, taken as ICS 699 (a maximum of six credits is counted toward the degree) under the supervision of a faculty member;

The administrative procedures for the program include the following:

• The student must meet with the graduate program chair during the first semester;
• Upon completion of at least 12 credit hours of courses applicable to the degree, students are encouraged to propose a degree plan by selecting Plan A (Thesis) or Plan B (NonThesis) options;
• Plan A students are encouraged to choose a thesis topic and committee upon completion of 18 credit hours of applicable courses; and
• All requests for changes in degree plan must be submitted in writing by the student and approved by the graduate program chair before the diploma application is filed.

PhD in Computer Science

The department offers a PhD in computer science that prepares students for creative research, teaching, and service. There are two programs leading to the PhD degree, one designed for the applicant entering with bachelor’s degrees, and the other for those who already have master’s degrees. Students may begin their program either in the fall or spring semesters.

Applicants with bachelor’s degrees must first satisfy the admission and degree requirements of the master’s degree in computer science. Advantages to this route are (1) students are admitted at an early stage to the PhD program; (2) the MS portion of the program will prepare students for their qualifying examination; and (3) students who have completed the MS requirements will have the option of obtaining a master’s degree even if they do not continue with the PhD program.

Applicants with master’s degrees in areas other than computer science may be admitted to the program, but will be required to fulfill their program deficiencies with additional course work.

Requirements for students to complete the PhD program are:

• Passing a qualifying examination demonstrating core competency in computer science no later than the end of the first year of their PhD studies;
• Preparing a portfolio showing research readiness by the end of the second year of their PhD studies;
• Passing the proposal defense;
• Passing the dissertation defense.

Interdisciplinary Doctoral Degree Program

The ICS department participates in an interdisciplinary program in Communication and Information Sciences (CIS) that integrates computer science, library science, communication and management information systems. Due to the broad knowledge base required to support the program, it draws on a variety of majors such as behavioral science, economics, engineering, and political science. The computer science program is one of four academic programs (COM, ICS, ITM, and LIS) that support this degree. See the “Interdisciplinary Program” section for more information on this program.

Connect With the Graduate Programs Office

The Graduate Programs Office (GPO) is a group of people passionate about helping the CSE Community navigate the graduate education experience, and we look forward to helping you! 

We’re located in 3909 Beyster Building, with the office open 8:00am – 4:30pm (summer hours are 7:30am-4:00pm). The staff is on a hybrid schedule.

Current Students: [email protected]

Prospective CSE Graduate Students: [email protected]

Need to talk to an advisor but don’t have an appointment? Consider virtual drop-in advising .

Meet the GPO team

Magda Calvillo Graduate Student Coaching and Community Engagement Manager 734-936-8875 (Professional coaching, community development)

Christa Carr Administrative Assistant 734-764-2606 (Event coordination)

Amanda Feaganes Graduate Programs Coordinator 734-647-0611 (Point of contact for current MS/SUGS student advising)

Tiffany Smith Financial Services and Admissions Specialist 734-647-0710 (Point of contact for funding, internships, & MS/SUGS admissions)

Jasmin Stubblefield Graduate Programs Manager 734-764-2624 (Point of contact for PhD program)

Emily Mower Provost Associate Chair for Graduate Studies

Quentin Stout Master’s Program Chair

Language Technologies Institute

School of computer science.

Ph.D. in Language and Information Technology

Ph.D. students are expected to publish papers about original research in the most competitive scientific journals and international conference proceedings, and to present their research at conferences and workshops. Most of our Ph.D. graduates become professors and research scientists, while a few have started their own companies.

Requirements

• Pass at least 96 units of graduate-level courses.
• Satisfy proficiencies in writing, presentation, programming and teaching; and
• Propose, write and defend a Ph.D. dissertation (thesis).
• Students must also attend the LTI Colloquium each semester and satisfy our Research Speaking Requirement.
• At least 72 units of LTI courses: Must include one class in each LTI focus area.
• At least 24 units of SCS courses.
• At least two lab courses in two different research areas.

A sample five-year schedule is shown below. It is just one of many paths that you can take through the PhD program. Each of the focus areas can be satisfied by several courses, which gives you some flexibility in how you satisfy degree requirements.

Course Categories

Ph.d. program intranet.

To Apply: Please see the Apply link near the top of this page.

Application Fee Waivers: Appliation fee waivers may be available in cases of financial hardship. For more information, please refer to the School of Computer Science Fee Waiver page .

Cost: Please see Carnegie Mellon's Cost of Attendance web page for the School of Computer Science.

Requirements The School of Computer Science requires the following for all Ph.D. applications. (Please note, these requirements may change for future application cycles.)

• GRE scores: GREs are now optional. If you want to submit GRE scores, they must be less than five years old. The GRE Subject Test is not required, but is recommended. Our Institution Code is 2074; Department Code is 0402.
• TOEFL scores: Required if English is not your native language. No exceptions. These scores may be more than two years old if you have pursued or are pursuing a bachelor's or graduate degree in the United States. (While the TOEFL is preferred, the IELTS test may also be submitted.) Successful applicants will have a minimum TOEFL score of 100. Our Institution Code is 4256; the Department Code is 78.
• Official transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation.
• For more details on these requirements, please see the SCS Doctoral Admissions page.
• A short (1-3 minute) video of yourself. Tell us about you and why you want to come to CMU. This is not a required part of the application process, but it's strongly suggested.
• Any outside funding you are receiving must be accompanied by an official award letter.
• No incomplete applications will be eligible for consideration.

Program Contact

For more information about the Ph.D. program, contact Stacey Young.

Program Handbook

Generative AI & Large Language Models

Online Graduate Certificate

GenAI is Transforming the World

What will you create with it.

Generative AI has already revolutionized the world and it’s not slowing down. As a trained computer scientist, if you want to contribute to the revolution of Generative AI, and make an immediate impact in your organization, now is the time to enhance your expertise.  

A training ground for Generative AI  

In Carnegie Mellon’s new Generative AI and Large Language Models graduate certificate, offered by CMU’s nationally-ranked School of Computer Science, you will learn the latest and most advanced techniques in Generative AI, large language models and multimodal machine learning from expert faculty at the forefront of computer science research.

This is not your average online certificate program. The coursework covers complex topics that build on expertise in applied mathematics, programming, machine learning and deep learning.

By the end of this certificate, you will be prepared to build customized applications of Generative AI. You will learn how to  design and implement scalable systems for large language models, evaluate and choose between existing models, do customization via finetuning, and leverage multimodal machine learning through integrating and modeling multiple communicative modalities (e.g. audio, images, and video).

More than theory, this program takes a hard-core systems approach by giving you not only the technical skills but the ability to implement and scale solutions based on your unique organizational needs and resources. Here you will gain the depth, breadth and practical skills to apply this technology immediately.

Our advanced program will teach you how to:

• Implement state-of-the-art language models such as GPT and LLaMA from scratch.
• Compare and contrast different models and approaches in order to determine the best setup for tasks you care about.
• Perform model training and inference using popular frameworks such as HuggingFace.
• Design and run generative AI systems on h igh performance computer infrastructure using tools like SLURM.  
• Understand and be able to apply algorithms and system techniques to efficiently train LLMs with huge datasets, including efficient fine-tuning and reinforcement learning with human feedback, acceleration on GPU and other hardware, model compression for deployment, and online system maintenance.
• Implement multimodal systems such as audio-visual speech recognition, image generation, and video captioning—addressing challenges in (1) multimodal representation learning, (2) translation and mapping between modalities, (3) modality alignment, (4) multimodal fusion and (5) co-learning.

A powerful certificate. Conveniently offered.

The Graduate Certificate in Generative AI and Large Language Models is offered 100% online to accommodate your busy schedule as a working professional. Along with weekly, live-online interactive classes taught by expert CMU faculty, you will complete hands-on learning activities on your own time that complement the discussions you have in class. To earn the certificate, you will complete three rigorous CMU classes over an 18-month period.

For computer science pioneers

This certificate program is best suited for:

• Industry professionals working in computer science, data science, software engineering or a similar field who want to enhance their domain knowledge with expertise in Generative AI and large language models so they can build new and innovative solutions for the future.  
• Recent college graduates with a degree in computer science, data science, software engineering or a similar field who want to gain in-depth, state-of-the-art knowledge about Generative AI and large language models to enhance their skills, make an immediate impact in their organization, and stay competitive in the job market. 

At a Glance

Start Date January 2025

Application Deadline Priority*: September 17, 2024 Final: December 3, 2024 *All applicants who submit by the priority deadline will receive a partial scholarship award.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for one hour in the evening with a second optional one-hour weekly recitation session.

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686  or send an email to  [email protected]  with your inquiries.

CMU Online Graduate Certificates

Below, explore more online opportunities offered by Carnegie Mellon University.

Machine Learning & Data Science With a STEM undergraduate degree and Python proficiency, you can learn how to harness the power of big data in this certificate offered by the School of Computer Science.

Foundations of Data Science Designed for individuals with non-technical backgrounds, this certificate from the Dietrich College of Humanities & Social Sciences can help you make data-driven decisions in the workplace.

AI Engineering Fundamentals Have a mechanical engineering degree, a familiarity with Python and an eagerness to design next-generation solutions? This program from the College of Engineering could be for you.

AI Engineering for Digital Twins & Analytics Learn how to lead the implementation of AI + Digital Twins for your organization from world-renowned experts in CMU's College of Engineering.

Managing AI Systems If you are interested in driving the adoption of AI in your organization, then this program from the Heinz School of Public Policy is for you. No technical expertise is required for admission.

On-Campus Degree

Interested in the on-campus Master of Science degree in Computational Data Science offered by CMU's School of Computer Science? Visit the program website  for more details.

Pioneering the use of AI across industries 

Carnegie Mellon University and CMU’s School of Computer Science are consistently ranked among the top schools in the nation for artificial intelligence, computer science and programming languages. When you enroll in this program, you can trust that you’re learning the most advanced techniques from some of the most distinguished and accomplished experts in the field.

Number ONE in the nation for artificial intelligence graduate programs.  

Number ONE in the nation for our programming languages courses.

Number FOUR in the nation for our computer science programs.

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Master of Science in Data Science

Join our innovative program, designed with input from industry leaders and available fully online. 

Enter the Growing, In-Demand Field of Data Science

The University of Wisconsin-Eau Claire's master of science in data science is a fully online degree program intended for students with a bachelor’s degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst, information technology analyst, database administrator, computer programmer, statistician, or other related position.

The rigorous program is the first online master's degree in data science offered in the UW System and is helping fill a critical need for data scientists. Using analytics, statistics, programming, business and storytelling, data scientists have the unique and important job of transforming big data into actionable insights. The field is already growing at an incredible pace, and as today's world continues to generate more and more data, employers across the country are in consistent need of professionals who know how to understand and interpret data.

Designed with input from industry leaders, the data science program offers a comprehensive, multidisciplinary curriculum grounded in computer science, math and statistics, management and communication. Coursework throughout the degree will show you how to clean, organize, analyze and interpret data using current industry tools and analytical methods. Since data scientists must also understand privacy and security policies, part of the curriculum throughout the program focuses on how to appropriately handle data found in financial records, medical records, consumer patterns, internet searches and other real-world situations — knowledge that is needed in countless industries and organizations.

Graduates of the data science graduate program leave with the knowledge, skills and tools necessary to mine data sets, find patterns and communicate ways to make use of the findings. The intensive program prepares you for expertise in a number of specialized areas — including data mining and warehousing, predictive analytics, statistical modeling, database infrastructures and data management, machine learning, and analytics-based decision making — making you a versatile and highly sought-after employee. 

Program Details

Accreditation information.

Wisconsin is a SARA state (State Authorization Reciprocity Agreement) and the University of Wisconsin-Eau Claire is a SARA-approved institution.

Throughout the data science program, you'll learn from diverse, distinguished faculty members from across six University of Wisconsin campuses and the University of Wisconsin-Extension. Their expertise, combined with UW Extended Campus' award-winning instructional and media design, ensures a rich and engaging educational experience that will prepare you well for your future career. 

While working toward your degree, you'll have direct access to the field's latest technology, including powerful tools like SQL Server, R, Python, and Tableau. This knowledge and experience will give you a competitive advantage when applying for jobs or transitioning into more executive roles within your current organization. 

Students in the data science program can take advantage of affordable tuition that compares favorably to competing graduate programs from other institutions. Like other collaborative online University of Wisconsin programs, students pay the same tuition whether they live in Wisconsin or elsewhere.

The data science degree was intentionally designed with significant input from businesses and industry leaders, ensuring curriculum aligns with employer needs. An industry advisory board consisting of leading organizations — including American Family, CUNA Mutual Group, Nicolet Bank, and TDS Telecom — provides further insights into what organizations are looking for and what the field needs right now. 

Blugold Stories

Analyze data. Develop computer programs. Perfect your coding skills. With a master’s degree in data science, you’ll do all this and more. Our expert faculty will guide you as you learn more than you ever thought possible, alongside peers that offer their own real-world insight.

Just the facts

100% Online This program can be completed entirely online.

100% Employed or Continuing Education Every 2022-2023 graduate from this major is currently employed or continuing their education.

Where can the master of science in data science program lead me after graduation?

Data science graduates enter a quickly growing field where demand is high for professionals who know how to transform complex data sets into actionable information and competitive advantages. And because data scientists are needed in virtually every sector, our Blugolds have no problem finding jobs upon graduation. Explore opportunities in manufacturing, construction, transportation, warehousing, communication, science, healthcare, computer science, information technology, retail, sales, marketing, finance, insurance, education, government, law enforcement, security, and so much more.

Example Careers

• Data scientist
• Data or research analyst/manager
• Data warehouse architect
• Enterprise strategy consultant
• Business intelligence manager/analyst
• Hadoop engineer
• Market intelligence analyst/manager

The master of science in data science is a 12-course, 36-credit online master's degree that prepares students for complex and fast-paced careers in data science and analytics. 

Featuring a multidisciplinary curriculum that draws primarily from computer science, mathematics and statistics, management and communication, the program teaches students how to derive insights from real-world data sets — both structured and unstructured. Using the latest data science tools, analytical methods and sophisticated visualization techniques, graduates learn to communicate their data discoveries and recommendations clearly. A focus on building leadership and communication skills rounds out the degree.

Here are a few courses in Master of Science in Data Science at UW-Eau Claire.

Foundations of Data Science

Introduction to data science and its importance in business decision making.

Visualization and Unstructured Data Analysis

Covers various aspects of data analytics including visualization and analysis of unstructured data such as social networks.

Ethics of Data Science

Ethical issues related to data science, including privacy, intellectual property, security, and the moral integrity of inferences based on data.

Meet the Faculty

Related Programs

Thinking about studying master of science in data science? You might also be interested in exploring these related programs.

What's Next?

University of Wisconsin-Eau Claire

105 Garfield Avenue  P.O. Box 4004  Eau Claire, WI 54702-4004 

715-836-4636