computer science phd nyu

Master’s Degree Requirements

To satisfy the requirements for the master’s degree, the student must complete 30 credits, as described below, with an overall average of B. In addition, a B average is required across the required algorithms course and the four core courses, and a grade of B or better is required for the capstone course, as indicated below. The master’s curriculum has four components: 3 credits of algorithms, 12 credits of core elective courses (one of which may also count as the capstone course), one 3 credit capstone course, and 12 credits of general elective courses.

For students who enroll in the program with full-time status, the M.S. program is designed to be a 2-year program. Since not all courses are offered every semester, your course options are likely to be more limited if you elect to finish the program in less than 2 years

Required Course in Algorithms

Students are required to take CS-GY 6033 Design and Analysis of Algorithms I    or CS-GY 6043 Design and Analysis of Algorithms II   . Most students will take the Algorithms I course to satisfy the algorithms course requirement. Students are expected to have knowledge of Discrete Math equivalent to CS-GY 6003 Foundations of Computer Science    prior to taking the Algorithms I course. Students lacking that knowledge may be required to take CS-GY 6003 Foundations of Computer Science. Advanced students who previously took an equivalent Algorithms I course, and received a grade of at least A-, may want to take the Algorithms II course to satisfy the requirement.

Core Course Requirements

Students must take at least four courses from the list of core courses below. The list will be periodically updated by the CSE Department and certain courses may be substituted with departmental consent.

• CS-GY 6063 Software Engineering I 3 Credits
• CS-GY 6083 Principles of Database Systems 3 Credits
• CS-GY 6133 Computer Architecture I 3 Credits
• CS-GY 6233 Introduction to Operating Systems 3 Credits
• CS-GY 6313 Information Visualization 3 Credits
• CS-GY 6373 Programming Languages 3 Credits
• CS-GY 6513 Big Data 3 Credits
• CS-GY 6533 Interactive Computer Graphics 3 Credits
• CS-GY 6613 Artificial Intelligence I 3 Credits
• CS-GY 6643 Computer Vision 3 Credits
• CS-GY 6763 Algorithmic Machine Learning and Data Science 3 Credits
• CS-GY 6813 Information, Security and Privacy 3 Credits
• CS-GY 6843 Computer Networking 3 Credits
• CS-GY 6923 Machine Learning 3 Credits

Capstone Course Requirement

Certain courses in our department will be designated as capstone courses. Capstone courses are drawn from key technical areas in the M.S. program and they involve a substantial amount of programming effort. Students are required to take at least one capstone course with a grade of B or better. The list of capstone courses will be posted by the department and will be updated from time to time. If a course is listed both as a capstone course and as a core course, the course can be used to satisfy both the capstone and core course requirements. An M.S. thesis can also be used to satisfy the capstone course requirement.

Capstone Courses

Here is the approved list of capstone courses:

• CS-GY 6053 Foundation of Data Science 3 Credits
• CS-GY 6243 Operating Systems II 3 Credits
• CS-GY 6253 Distributed Operating Systems 3 Credits
• CS-GY 6413 Compiler Design and Construction 3 Credits
• CS-GY 6573 Penetration Testing and Vulnerability Analysis 3 Credits
• CS-GY 6823 Network Security 3 Credits
• CS-GY 6943 Artificial Intelligence for Games 3 Credits
• CS-GY 9163 Application Security 3 Credits
• CS-GY 9223 Special Topics in Computer Science: Distributed Systems 3 Credits

General Elective Requirements

In addition to the core electives, students are required to take four general elective courses with considerable flexibility; the only restriction is that no more than two of the courses may be taken from outside the Department of Computer Science and Engineering. In particular:

• Master’s thesis (6 credits) and/or independent study courses may be part of a student’s elective courses. Note that the master’s thesis (CS-GY 997X) has an important requirement, as described here    .
• Any of the core courses may be chosen as electives.
• Graduate­-level courses from outside of the department (at most two) may be chosen as electives.
• Any CS graduate course not included in the core areas may be chosen as electives.

These courses include (among others):

This list may be updated from time to time based on the current offerings of the department. 

• CS-GY 6003 Foundations of Computer Science 3 Credits
• CS-GY 6033 Design and Analysis of Algorithms I 3 Credits
• CS-GY 6043 Design and Analysis of Algorithms II 3 Credits
• CS-GY 6093 Advanced Database Systems 3 Credits
• CS-GY 6323 Large-Scale Visual Analytics 3 Credits
• CS-GY 6543 Human Computer Interaction 3 Credits
• CS-GY 6553 Game Design 3 Credits
• CS-GY 6703 Computational Geometry 3 Credits
• CS-GY 6753 Theory of Computation 3 Credits
• CS-GY 6803 Information Systems Security Engineering and Management 3 Credits
• CS-GY 6903 Applied Cryptography 3 Credits
• CS-GY 6913 Web Search Engines 3 Credits
• CS-GY 6953 Deep Learning 3 Credits
• CS-GY 6963 Digital Forensics 3 Credits
• CS-GY 9053 Special Topics in Computer Science: Intro to Java 3 Credits
• CS-GY 9223 Selected Topics in Computer Science 3 Credits
• CS-GY 9963 Advanced Project in Computer Science 3 Credits
• CS-GY 997X M.S. Thesis in Computer Science Variable Credits

Sample Course Plans

The particular courses that a student takes during the program will vary according to the student’s interests and background, course offerings, and whether the student does an internship. The following are two sample courses of study. These are just samples meant to help in planning the courses for the degree. Individual course plans may differ depending on when courses are offered.

Sample Plan 1

Sample course plan for a student not doing an internship and taking Foundations of Computer Science (CS-GY 6003).

Sample Plan 2

Sample course plan for a student doing internships and not taking Foundations of Computer Science (CS-GY 6003).

Preparatory Course

The 100% online NYU Tandon Bridge course prepares students without a Computer Science degree or other substantial programming experience to apply for select NYU Tandon master’s degree programs. In the course, students will learn computer science fundamentals and programming with C++. Students’ performance in the Bridge will count toward their master’s degree application decisions. The Bridge is a non-credit certificate course, and those who complete the Bridge with a final grade of C or above will earn a Certificate of Completion, and those who earn a B+ or above will receive a Certificate of Completion with Distinction. Note: regardless of performance, successful completion of the Bridge course does not guarantee admission to any academic program.

The NYU Tandon Bridge course is taught by faculty members of the Computer Science department at the NYU Tandon School of Engineering, aided by NYU Tandon Graduate student teaching assistants. Students will participate in interactive online modules, live webinars, assignments, and tests.

Learn more:  Computer Science Bridge Program

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PhD, Statistics and Computational Social Science

36-48 Total Credits Required

To satisfy the requirements of the doctoral degree in Statistics and Computational Social Science, you will complete 36-48 credits of coursework, pass comprehensive exams, engage in research activities, and write and defend a dissertation. If you enter the doctoral program with a graduate degree, you may be eligible for advanced standing and can waive up to 12 credits of coursework.

Quantitative research addressing societal issues increasingly relies on a combination of innovative statistical modeling, typically involving sophisticated computational methods, along with a deep understanding of social science, broadly conceived. To complete the requirements of the doctoral degree in Statistics and Computational Social Science, you will take two foundational courses, along with courses in statistics, computational methods, and the social sciences. 

Foundations:

• Data science for social impact
• Data ethics

Statistics; representative courses include:

• Probability
• Experimental/Survey Design
• Generalized Linear and Mixed Models
• Causal Inference
• Specialized topics (e.g., Spatial Statistics, Networks, Multilevel Modeling)

Computational methods; representative courses include:

• Programming
• Machine Learning
• Data Structures & Algorithms
• Large/Messy Data
• Database Systems

Social sciences and related disciplines; courses chosen from areas including:

• Political Science
• Public Policy 

Comprehensive Exams

You will take two written comprehensive examinations covering core areas of knowledge that underpin the application of statistics to research in the social sciences; it is expected that students pass both exams by the end of their second year in the program. The first exam will be a standard timed exam assessing knowledge of statistics (with specific topics including, e.g., causal inference, machine learning, probability, and inference) and computational methods (with specific topics including, e.g., optimization and analysis of algorithms). The second exam will take place over the course of a week, and will involve writing a hypothetical grant proposal outlining a research question, proposed data collection strategies, research design and analysis choices, and dissemination plans.

Qualifying Paper

To demonstrate depth of knowledge outside statistical and computational methodology, you will write a qualifying paper describing current knowledge and specific questions relevant to your “cognate discipline,” that is, a sub-area within a social science or related field, and pertaining to the planned dissertation topic. This paper will be evaluated by at least one professor outside the core SCSS faculty who is an expert in the cognate discipline, and must be completed before the dissertation proposal stage.

Research Activities

In addition to formal coursework, you will also engage in a variety of research-related activities each semester, beginning with the first semester on campus:

• Regular meetings with a faculty advisor to discuss ongoing research projects. Doctoral students are also welcome to work with faculty outside of the department with the approval of their advisor.
• Implementing a research plan to contribute to ongoing research projects
• Regular attendance in research seminars hosted by NYU’s PRIISM Center.
• Presentations of relevant literature, questions of interest, and ongoing research findings in research seminars to program faculty.
• Participation in a consulting project, such as a research-practice partnership, under the guidance of a faculty mentor.
• Preparation for comprehensive exams and qualifying paper.

Dissertation

The activities of research, coursework, seminars, comprehensive exams and the qualifying review paper will have exposed you to a wide range of faculty and their interests. By the third year in the program, you will have developed an independent research agenda that you can pursue with support from your advisor, and which will result in the completion of a dissertation.

The dissertation format for the Ph.D. in Statistics and Computational Social Science will follow a three-paper model, common in many social science disciplines, that codifies the interdisciplinary philosophy of this doctoral degree program. Each paper will address a different aspect of the same research topic — for example, one could be a review paper intended for the “cognate” discipline’s audience, the second could be a methods paper, incorporating both statistical and computational elements, and intended for a more technical audience, and the third could be an applied paper that demonstrates the utility of the method to practitioners. Together, the three papers should change the way in which we understand the world in a manner that was unattainable without the mixture of disciplines and related techniques. 

After completing comprehensive exams and the qualifying paper, you will choose three faculty members to serve on your dissertation committee, with one designated as committee chair. Given the interdisciplinary nature of the degree, at least one faculty member should represent the “cognate” discipline. Committee members will provide regular feedback on dissertations and dissertation proposals.

Dissertation Proposal

You will prepare, submit, and orally defend a manuscript research proposal, similar to a dissertation proposal.

Dissertation Defense

The manuscripts, taken together, must reflect a coherent and cohesive research line of inquiry and will be defended in a final oral defense for completion of the PhD. The presentation portion of the dissertation defense will be open to the public. By the date of the defense, at least one  first-authored manuscript must be under review at a peer-reviewed journal. 

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Bachelor's Degree in Computer Science

Why pursue a bachelor's degree in computer science.

The concentration in Computer Science is designed to teach students skills and ideas they will use immediately and in the future. Because information technology affects every aspect of society, graduates with computer science degrees have open to them an enormous variety of careers—engineering, teaching, medicine, law, basic science, entertainment, management, and countless others. 

At Harvard College, students choose a "concentration," which is what we call a major. All prospective undergraduate students, including those intending to study engineering and applied sciences, apply directly to Harvard College . During your sophomore spring you’ll declare a concentration, or field of study. You may choose from 50 concentrations and 49 secondary field (from Harvard DSO website ).

All undergraduates in Computer Science at Harvard are candidates for the Bachelor of Arts degree (A.B.) . With the knowledge that it requires extra course work, you can consider the more intensive  A.B./S.M. option  through a concurrent masters degree.

Learn about our Computer Science concentrators  >

Apply to Harvard College  >

A.B. in Computer Science

The basic degree requirements are eleven to fourteen 4-credit courses in mathematics, theoretical computer science, computer software, and other areas of computer science. Math courses cover linear algebra, single variable calculus and probability/statistics. Students who place out of part or all of the introductory calculus sequence, Mathematics 1ab, reduce their concentration requirements to 11 courses.

Computer Science Secondary Field

A lightweight way of getting official recognition within Harvard for work in two fields is to do one or the other as a secondary field. For Computer Science, this involves taking 4 courses in the secondary field. Learn more about the  computer science secondary field .

A.B./S.M. in Computer Science

Our  AB/SM degree program  is for currently enrolled Harvard College students only. Students who are eligible for  Advanced Standing  on the basis of A.P. tests before entering Harvard may be able to apply for admission to the S.M. program of the Graduate School of Arts and Sciences and graduate in four years with both a bachelor’s and master’s degree (not necessarily in the same field).

Beginning with the class of 2022, students have the opportunity to apply to the Graduate School of Arts and Sciences for a master’s degree pursued concurrently with the bachelor’s degree. As part of the  concurrent degree program , students will be allowed to double-count up to sixteen credits (normally, four courses) for the Bachelor of Arts and the Master of Science. An undergraduate pursuing the concurrent degree must complete both of these degrees by the end of eight terms of residency, or the equivalent.

The Mind, Brain, and Behavior Program (MBB)

Students interested in addressing questions of neuroscience and cognition from the perspective of computer science may pursue a special program of study affiliated with the University-wide Mind, Brain, and Behavior Initiative, that allows them to participate in a variety of related activities. (Similar programs are available through the Anthropology, History and Science, Human Evolutionary Biology, Linguistics, Neurobiology, Philosophy, and Psychology concentrations.) Requirements for this honors-only program are based on those of the computer science Requirements for Honors Eligibility. See the  handbook entry  for more information and also  Frequently Asked Questions about the MBB Track . This is an honors track program: students are eligible for English Honors.

Why study CS at Harvard? What’s different about pursuing CS in a liberal arts setting?

Get the answer to these questions and learn more about CS .

Prerequisites

Learn about the prerequisites for the concentration on our  First-Year Exploration page . Students interested in concentrating in computer science can refer to our Sophomore Advising page  and request to be matched with a Peer Concentration Advisor  (PCA). PCAs serve as peer advisors for pre-concentrators (and current concentrators), providing a valuable perspective and helping students to discover additional resources and opportunities.

Requirements

Learn more about the Computer Science requirements >

View current Computer Science courses . >

View sample plans of study. >

Tags for Computer Science courses. > 

Research Opportunities in Computer Science

As part of your Bio/Biomedical Engineering coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects.  Learn more about research opportunities at Harvard SEA S.

Learn about the research interests of our Computer Science faculty .

Computer Science Career Paths

Learn about potential career paths for students for students concentrating in Computer Science . 

Computer Science & Society

Harvard Computer Science has several programs that allow undergraduate students to think about the broader issues in tech and CS.

Computer Science Clubs and Organizations

SEAS-affiliated student organizations are critical to the overall growth of our concentrators as engineering and applied science professionals. These organizations enable our students to pursue passion projects and events in areas of interest that are complementary to the current formal academic curriculum. Learn more about computer science student clubs and organizations .

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Stanford Online

Computer science ms degree.

Stanford School of Engineering

Get Started

In the Stanford Computer Science Master's degree , you will complete coursework covering the fundamental aspects of computer science and deepen your expertise in at least one specialized area of study.

If you want to pursue the degree on a part-time basis, so as not to interrupt your career, you can enroll in as few as one course per quarter.

For added flexibility, you can take courses online or in-person on Stanford’s campus. Each quarter, numerous computer science and other engineering courses are available online. While most specializations within the computer science degree require attending some in-person classes, you can complete the Artificial Intelligence, Information Management and Analytics, and Systems Specializations entirely through online coursework. (Note that students interested in earning the master's degree part-time or online must reside in the United States.) 

If you want more flexibility than the part-time master's degree, you can apply to take individual courses or pursue a graduate certificate without being formally admitted to Stanford master’s degree program. Choose from many options, including Foundations in Computer Science , Artificial Intelligence , Cybersecurity , Visual Computing , Software Systems , and Advanced Software Systems . Upon successful completion of each course, you will receive academic credit and a Stanford University transcript.

If you later choose to apply and are admitted into a master's degree program at Stanford, you may apply up to 18 units towards the master's degree (pending department approval).

Not sure which of these credentials is right for you? Compare our graduate certificate vs. master’s degree .

How Much It Will Cost

How long it will take.

To earn the Master of Science in Computer Science Degree, you must complete 45 units.

• As a part-time student, you can expect to finish the degree in 3 to 5 years.
• As a full-time student, you can expect to finish the degree in 1 to 2 years.

What You Need to Get Started

For admissions information , please visit the department's site or contact [email protected] .

For degree requirements , please review either the department's Guide to the MSCS Program Sheet or Stanford Bulletin . See the department's FAQs page .

For more about the policies, procedures, and logistics, please review our website .

While this degree can be completed online, it depends on your program plan and area of focus. Most courses in the Computer Science department are offered only on campus. Specific online course offerings depend heavily on your program plan, area of focus, and the online course offerings for any given academic quarter. Students who are outside the US cannot pursue the master's degree online.

What Our Learners Are Saying

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Roz discusses the connection between design and computer science as well as the goals accomplished by the courses she took through Stanford Online.

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Best Master’s in Data Science for 2024

It’s no secret that the need for data experts is growing due to the exponential amount of data being generated every day. One of the best ways to gain the in-demand skills to be able to harness, analyze, and create value from data is pursuing a master’s degree. This ranking was last updated February 2024.

UC Berkeley’s Master’s in Data Science — Online

Syracuse University MS in Applied Data Science Online

1. Harvard University

• ACCEPTANCE RATE, 2023-24
• AVERAGE UNDERGRADUATE GPA, 2023-24 ENROLLEES
• FALL TERM ENROLLMENT, 2022–23
• GRADUATION RATE, 2022-23
• NUMBER OF APPLICANTS IN 2023-24
• ONE-YEAR RETENTION RATE, 2022-23

2. University of North Texas

3. New York University

Earn Your Master’s in Data Science Online From SMU

4. University of Michigan–Ann Arbor

5. Carnegie Mellon University

6. University of California–Irvine

7. University of Rochester

8. Indiana University–Bloomington

Maryville University Master of Science in Data Science | Online

9. University of Arizona

10. University of Delaware

11. Appalachian State University

12. University of Minnesota

13. Oklahoma State University

14. University of Missouri

15. Georgia State University

16. Maryville University

17. University of Michigan–Dearborn

18. New York Institute of Technology

19. University of San Francisco

20. DePaul University

21. Marquette University

22. Willamette University

23. Rochester Institute of Technology

24. Texas Tech University

25. Worcester Polytechnic Institute

26. University of St. Thomas

27. American University

28. University of Maryland

29. CUNY Graduate Center

Frequently Asked Questions

Data science is one of the fastest growing fields—job openings are expected to grow by 35% by 2023, according to the U.S. Bureau of Labor Statistics . And students graduating with a master’s in data science often land six figure salaries. The reason it’s a fast growing field, with high paying jobs, is because companies across all industries want data-savvy professionals in this era of digitization. Data provides companies and organizations with the resources they need to make better decisions—and in turn, they need professionals with data science skills who know how to understand and analyze data. 

The GPA you’ll need to get accepted into a master’s program for data science varies by school. For all of the programs ranked by Fortune for 2024, the average undergraduate GPA for enrollees was 3.27. Students at Harvard and New York University had the highest GPA, with 3.87 and 3.75, respectively. Marquette University enrollees had the lowest reported GPA—at 3.01.

Master’s degree programs in data science can be offered in person, online or in a hybrid format—and that might be the difference in what the “best program” for you means. Fortune ranks the top five in-person programs for 2024 as: Harvard University, the University of North Texas, New York University, University of Michigan—Ann Arbor, and Carnegie Mellon University. Additionally, our ranking of the top five online programs in 2023 include: University of Southern California, UC—Berkeley, Bay Path University, New Jersey Institute of Technology, and Clemson University.

On average, it takes about one-and-a-half to two years to complete a master’s degree program in data science—with most programs requiring roughly anywhere from 25 to 60 credits to graduate. So it does depend on each individual program and whether you choose to be a full-time or part-time student. That said, thanks to a boost in salary and expanded career options, many students find it worthwhile to obtain a master’s degree in data science—and Gen Z considers the role of data scientist to be one of the most satisfying occupations .

A master’s degree in data science will teach you how to understand and analyze data. But because it’s a recently defined career path, how it’s applied can vary significantly. As Maurizio Porfiri, a New York University professor, told Fortune: “It’s a weird thing because it’s very vague. I discovered after a while that I had become a data scientist : people just started to refer to me as such.” But sometimes the first step to finding your place in the world of data science is picking a specialization—what type of problem you want to solve by using data. And a master’s degree can either help you find that specialization, or if you’ve already got the answer, will teach you the skills to pursue it.

Fortune compiled a list of seven universities that offer free online data science courses , which offers prospective students an opportunity to learn more about this field. Each university—Harvard University, the University of Michigan, UC Irvine, John Hopkins University, Columbia University, MIT, and Duke University—offers a different course, from linear regression to data science ethics to data science in real life. However, the common goal of these free courses is to give people an inside look into the field.

In 2022, data scientists earned median salaries of $103,500, according to the U.S. Bureau of Labor Statistics . But a degree from a top program might mean even more money; New York University’s (ranked third on Fortune’s best in-person data science programs) 2022-23 graduates with a master’s in data science earned an average salary of $143,000 four months after graduation, according to data provided by the university.