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• Current MIT Graduate Students

Please Note: The CSE PhD application deadline was December 1; the portal is now closed and we are no longer accepting applications for 2024 enrollment.

Thank you for considering the academic programs of the MIT Center for Computational Science and Engineering (CCSE) as part of your graduate education options.

CCSE offers a master’s degree and two doctoral programs in computational science and engineering (CSE) – one leading to a standalone doctoral degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an i nterdisciplinary doctoral degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD). Information about the application and admission process for each program is available via the links of the left and summarized below. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

The standalone CSE PhD program is intended for students who plan to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the Schwarzman College of Computing. Applicants to the standalone CSE PhD program are expected to have an undergraduate degree in CSE, applied mathematics, or another field that prepares them for an advanced degree in CSE.

In contrast, the interdisciplinary Dept-CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline. Applicants to the Dept-CSE PhD program should have an undergraduate degree in a related core disciplinary area as well as a strong foundation in applied mathematics, physics, or related fields.

When completing the MIT CSE graduate application , students are expected to declare which of the two programs they are interested in. Admissions decisions will take into account these declared interests, along with each applicant’s academic background, preparation, and fit to the program they have selected.

The Master of Science in Computational Science and Engineering (CSE SM) program , which is currently not accepting external applications,  is an interdisciplinary program for students interested in the development, analysis, and application of state-of-the-art computational approaches to science and engineering problems. Examples of such approaches include but are not limited to: numerical methods for integral and partial differential equations; molecular and stochastic simulation; model reduction; uncertainty quantification; computational statistics; optimization; high-performance computing; and machine learning applied to science and engineering problems. The curriculum comprises a core set of subjects focused on cross-cutting computational methodologies and an elective component encompassing specific disciplinary topics in science and engineering. Additional information about CSE SM admissions, including any future plans to restart external admissions, will be posted on the CSE SM Admissions webpage if/when they become available. In the interim, external requests for information about the CSE SM program will not be addressed. Thank you in advance for your understanding.

CSE’s graduate offerings require some background in computational science and engineering (CSE) and lead to degrees in CSE. They are not programs in computer science (CS). In particular, they have a different research and education focus from MIT’s computer science programs. Students who are instead interested in a graduate degree in computer science should apply to the graduate program of the Department of Electrical Engineering and Computer Science (EECS) .

MIT Graduate Admissions Statement March 26, 2020

In response to the challenges of teaching, learning, and assessing academic performance during the global COVID-19 pandemic, MIT has adopted the following principle: MIT’s admissions committees and offices for graduate and professional schools will take the significant disruptions of the COVID-19 outbreak in 2020 into account when reviewing students’ transcripts and other admissions materials as part of their regular practice of performing individualized, holistic reviews of each applicant.

In particular, as we review applications now and in the future, we will respect decisions regarding the adoption of Pass/No Record (or Credit/No Credit or Pass/Fail) and other grading options during the unprecedented period of COVID-19 disruptions, whether those decisions were made by institutions or by individual students. We also expect that the individual experiences of applicants will richly inform applications and, as such, they will be considered with the entirety of a student’s record.

Ultimately, even in these challenging times, our goal remains to form graduate student cohorts that are collectively excellent and composed of outstanding individuals who will challenge and support one another.

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3 Questions: A new PhD program from the Center for Computational Science and Engineering

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This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

Q: What is computational science and engineering?

Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

Q: What was the motivation behind creating a standalone PhD program?

Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

Q: How will this impact computing education and research at MIT?  

Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique.

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Computational and Systems Biology PhD Program

Computational and systems biology.

The field of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the human genome project and massively parallel approaches to probing biological samples, have created new opportunities to understand biological problems from a systems perspective. Systems modeling and design are well established in engineering disciplines but are newer in biology. Advances in computational and systems biology require multidisciplinary teams with skill in applying principles and tools from engineering and computer science to solve problems in biology and medicine. To provide education in this emerging field, the Computational and Systems Biology (CSB) program integrates MIT's world-renowned disciplines in biology, engineering, mathematics, and computer science. Graduates of the program are uniquely prepared to make novel discoveries, develop new methods, and establish new paradigms. They are also well-positioned to assume critical leadership roles in both academia and industry, where this field is becoming increasingly important.

Computational and systems biology, as practiced at MIT, is organized around "the 3 Ds" of description, distillation, and design. In many research programs, systematic data collection is used to create detailed molecular- or cellular-level descriptions of a system in one or more defined states. Given the complexity of biological systems and the number of interacting components and parameters, system modeling is often conducted with the aim of distilling the essential or most important subsystems, components, and parameters, and of obtaining simplified models that retain the ability to accurately predict system behavior under a wide range of conditions. Distillation of the system can increase the interpretability of the models in relation to evolutionary and engineering principles such as robustness, modularity, and evolvability. The resulting models may also serve to facilitate rational design of perturbations to test understanding of the system or to change system behavior (e.g., for therapeutic intervention), as well as efforts to design related systems or systems composed of similar biological components.

CSB Faculty and Research

More than 70 faculty members at the Institute participate in MIT's Computational and Systems Biology Initiative (CSBi). These investigators span nearly all departments in the School of Science and the School of Engineering, providing CSB students the opportunity to pursue thesis research in a wide variety of different laboratories. It is also possible for students to arrange collaborative thesis projects with joint supervision by faculty members with different areas of expertise. Areas of active research include computational biology and bioinformatics, gene and protein networks, regulatory genomics, molecular biophysics, instrumentation engineering, cell and tissue engineering, predictive toxicology and metabolic engineering, imaging and image informatics, nanobiology and microsystems, biological design and synthetic biology, neurosystems biology, and cancer biology.

The CSB PhD Program

The CSB PhD program is an Institute-wide program that has been jointly developed by the Departments of Biology, Biological Engineering, and Electrical Engineering and Computer Science. The program integrates biology, engineering, and computation to address complex problems in biological systems, and CSB PhD students have the opportunity to work with CSBi faculty from across the Institute. The curriculum has a strong emphasis on foundational material to encourage students to become creators of future tools and technologies, rather than merely practitioners of current approaches. Applicants must have an undergraduate degree in biology (or a related field), bioinformatics, chemistry, computer science, mathematics, statistics, physics, or an engineering discipline, with dual-emphasis degrees encouraged.

CSB Graduate Education

All students pursue a core curriculum that includes classes in biology and computational biology, along with a class in computational and systems biology based on the scientific literature. Advanced electives in science and engineering enhance both the breadth and depth of each student's education. During their first year, in addition to coursework, students carry out rotations in multiple research groups to gain a broader exposure to work at the frontier of this field, and to identify a suitable laboratory in which to conduct thesis research. CSB students also serve as teaching assistants during one semester in the second year to further develop their teaching and communication skills and facilitate their interactions across disciplines. Students also participate in training in the responsible conduct of research to prepare them for the complexities and demands of modern scientific research. The total length of the program, including classwork, qualifying examinations, thesis research, and preparation of the thesis is roughly five years.

The CSB curriculum has two components. The first is a core that provides foundational knowledge of both biology and computational biology. The second is a customized program of electives that is selected by each student in consultation with members of the CSB graduate committee. The goal is to allow students broad latitude in defining their individual area of interest, while at the same time providing oversight and guidance to ensure that training is rigorous and thorough.

Core Curriculum

The core curriculum consists of three classroom subjects plus a set of three research rotations in different research groups. The classroom subjects fall into three areas described below.

Modern Biology (One Subject): A term of modern biology at MIT strengthens the biology base of all students in the program. Subjects in biochemistry, genetics, cell biology, molecular biology, or neurobiology fulfill this requirement. The particular course taken by each student will depend on their background and will be determined in consultation with graduate committee members.

Computational Biology (One Subject): A term of computational biology provides students with a background in the application of computation to biology, including analysis and modeling of sequence, structural, and systems data. This requirement can be fulfilled by 7.91[J] / 20.490[J] Foundations of Computational and Systems Biology.

Topics in Computational and Systems Biology (One Subject): All first-year students in the program participate in / 7.89[J] Topics in Computational and Systems Biology, an exploration of problems and approaches in the field of computational and systems biology through in-depth discussion and critical analysis of selected primary research papers. This subject is restricted to first-year PhD students in CSB or related fields in order to build a strong community among the class. It is the only subject in the program with such a limitation.

Research Group Rotations (Three Rotations): To assist students with lab selection and provide a range of research activities in computational and systems biology, students participate in three research rotations of one to two months' duration during their first year. Students are encouraged to gain experience in experimental and computational approaches taken across different disciplines at MIT.

Advanced Electives

The requirement of four advanced electives is designed to develop both breadth and depth. The electives add to the base of the diversified core and contribute strength in areas related to student interest and research direction. To develop depth, two of the four advanced electives must be in the same research area or department. To develop breadth, at least one of the electives must be in engineering and at least one in science. Each student designs a program of advanced electives that satisfies the distribution and area requirements in close consultation with members of the graduate committee.

Additional Subjects: As is typical for students in other doctoral programs at MIT, CSB PhD students may take classes beyond the required diversified core and advanced electives described above. These additional subjects can be used to add breadth or depth to the proposed curriculum, and might be useful to explore advanced topics relevant to the student's thesis research in later years. The CSB Graduate Committee works with each graduate student to develop a path through the curriculum appropriate for his or her background and research interests.

Training in the Responsible Conduct of Research: Throughout the program, students will be expected to attend workshops and other activities that provide training in the ethical conduct of research. This is particularly important in interdisciplinary fields such as computational and systems biology, where different disciplines often have very different philosophies and conventions. By the end of the fourth year, students will have had about 16 hours of training in the responsible conduct of research.

Qualifying Exams: In addition to coursework and a research thesis, each student must pass a written and an oral qualifying examination at the end of the second year or the beginning of the third year. The written examination involves preparing a research proposal based on the student's thesis research, and presenting the proposal to the examination committee. This process provides a strong foundation for the thesis research, incorporating new research ideas and refinement of the scope of the research project. The oral examination is based on the coursework taken and on related published literature. The qualifying exams are designed to develop and demonstrate depth in a selected area (the area of the thesis research) as well as breadth of knowledge across the field of computational and systems biology.

Thesis Research: Research will be performed under the supervision of a CSBi faculty member, culminating in the submission of a written thesis and its oral defense before the community and thesis defense committee. By the second year, a student will have formed a thesis advisory committee that they will meet with on an annual basis.

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Approximately 40% of undergraduates and 17% of graduate students at MIT are enrolled in computing programs within the Schwarzman College of Computing. More still augment or compliment their studies with subjects in computing and data science and classes that blend computational thinking with their chosen discipline. The College includes a breadth of academic offerings — from a traditional degree in Computer Science to a blended major in Computation and Cognition to a minor in Statistics and Data Science.

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U.S. News Releases 2024 Best Graduate Programs Rankings

Find the top-ranked graduate schools in business, education, law, nursing and other fields.

U.S. News Ranks Best Graduate Schools

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To help prospective graduate students find a school that fits their needs, U.S. News released the 2024 rankings for multiple graduate fields.

Depending on the job or field, earning a graduate degree may lead to higher earnings, career advancement and specialized skill development.

But with several types of degrees and hundreds of graduate schools, it can be difficult to narrow down the options. To help prospective graduate students find a school that fits their needs, U.S. News released its 2024 Best Graduate Schools rankings today. They evaluate business, education, fine arts, health, law, library studies, nursing, public affairs, science, and social sciences and humanities graduate programs. Medical school and engineering rankings are not being released at this time.

A notable methodology change includes a new salary indicator based on profession in the business rankings.

Additionally, for the first time in four years, there are new rankings for a blend of doctoral and master's programs in audiology, occupational therapy, physical therapy, pharmacy, nurse midwifery and speech-language pathology. Graduate programs in nurse anesthesia and social work are also ranked for the first time since 2016 and 2022, respectively. Those and other specialty rankings are based on reputation ratings from scholars at other surveyed schools.

Read each program's specific methodology for the most detailed explanations of all the changes. The rankings are one source of information among many that prospective college students can use to inform their college decision. Below is a summary of the top-ranked schools in four major graduate program areas:

Best Law Schools

Best business schools, best nursing schools, best education schools.

Among the top 10 law schools . Yale Law School in Connecticut and California-based  Stanford Law School shared the top spot again. The  University of Chicago Law School in Illinois maintained its No. 3 rank, followed by a four-way tie at No. 4: Duke University School of Law in North Carolina, Harvard Law School in Massachusetts, the University of Pennsylvania Carey Law School and the University of Virginia School of Law .

Columbia Law School in New York ranked No. 8 again, while there was a three-way tie for No. 9: New York University School of Law , Northwestern University's Pritzker School of Law in Illinois and the University of Michigan—Ann Arbor Law School .

Looking beyond the top 10, multiple law schools moved up in the rankings. William & Mary Law School in Virginia, for instance, jumped nine spots from a tie at No. 45 to a five-way tie at No. 36.

U.S. News also ranked 13 law specialties: business/corporate, clinical training, constitutional, contracts/commercial, criminal, dispute resolution, environmental, health care, intellectual property, international, legal writing, tax and trial advocacy. (You can filter by specialty on the  main ranking page .)

Meanwhile, in the  part-time law school rankings – which consists of law schools with at least 20 part-time students enrolled in fall 2022 and fall 2023 – the top three stayed the same. The  Georgetown University Law Center in Washington, D.C., is once again at the top while D.C.-based  George Washington University Law School , now No. 3, switched places with the  Fordham University School of Law in New York City, which claimed second place.

Previously ranked at No. 3 and No. 6 respectively, the University of Pennsylvania's Wharton School and Stanford Graduate School of Business took the top spot in this year's full-time MBA program rankings . Northwestern's Kellogg School of Management and the University of Chicago's Booth School of Business moved down from their previous places in the top two to tie at No. 3.

While the top 10 mostly consists of the same schools as last year, both the Haas School of Business at the University of California, Berkeley and the University of Virginia's Darden School of Business joined those ranks this year. UC Berkeley rose from a three-way tie at No. 11 to a three-way tie at No. 7, while UVA moved up four spots from No. 14 to a tie at No. 10.

Farther down the full-time MBA rankings, there were some big changes. For example, Pitt's Joseph M. Katz Graduate School of Business soared 39 spots from a tie at No. 86 to a tie at No. 47.

Meanwhile, the very top of the part-time MBA rankings looks similar to last year, with the same schools in the top 5: UChicago, UC Berkeley, Northwestern, NYU's Leonard N. Stern School of Business and the Anderson School of Management at the University of California—Los Angeles. But UChicago took the No. 1 spot from UC Berkeley this year.

Moving up from No. 2, Johns Hopkins University School of Nursing in Maryland tied with Emory University's Nell Hodgson Woodruff School of Nursing in Georgia to claim the top spot in this year's nursing master's program rankings. Duke University School of Nursing in North Carolina climbed up by one to claim the third spot.

Johns Hopkins ranked No. 1, as it did last year, in the Doctor of Nursing Practice program rankings. George Mason University School of Nursing in Virginia – which reported more graduates and resources per faculty – soared from a four-way tie at No. 39 to take the No. 2 spot. Duke tied with the University of Washington School of Nursing to round out the top three.

Duke also ranked No. 1 in all of the ranked nursing master's nursing practice specialties, including administration, family, both acute and primary care adult gerontology, and mental health.

Once again, Teachers College, Columbia University in New York was No. 1 in the graduate education schools rankings. This year, however, it tied with the University of Wisconsin—Madison School of Education , which climbed two spots.

The University of Michigan—Ann Arbor's School of Education dropped from the top position to tie with the UCLA School of Education and Information Studies at No. 3. UCLA was previously tied at No. 7.

U.S. News also ranks nine education specialties, with the College of Education at Michigan State University claiming the top spot in the following categories: curriculum and instruction, educational administration, elementary teacher education, higher education administration and secondary teacher education.

Searching for a grad school of education? Access our  complete rankings  of Best Graduate Schools.

Grad Degree Jobs With $100K+ Salaries

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Degree programs

Mit offers a wide range of degrees and programs..

All graduate students, whether or not they are participating in an interdepartmental program, must have a primary affiliation with and be registered in a single department. Every applicant accepted by MIT is admitted through one of the graduate departments. MIT has a number of established interdepartmental programs, and there are many more opportunities for students to arrange interdepartmental programs with interested faculty members.

All MIT graduate degree programs have residency requirements, which reflect academic terms (excluding summer). Some degrees also require completion of an acceptable thesis prepared in residence at MIT, unless special permission is granted for part of the thesis work to be accomplished elsewhere. Other degrees require a pro-seminar or capstone experience.

Applicants interested in graduate education should apply to the department or graduate program conducting research in the area of interest. Below is an alphabetical list of all the available departments and programs that offer a graduate-level degree.

Interested in reading first-hand accounts of MIT graduate students from a variety of programs? Visit the Grad Blog . Prospective students who want to talk with a current student can reach out to their department(s) of interest for connections or, if they are interested in the MIT experience for diverse communities, can reach out to a GradDiversity Ambassador .

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Bachelor of Urban Science and Planning with Computer Science

In the years to come, solutions to the complex global problems, which are increasingly urban, will require an understanding of large amounts of data and a facility with analysis, visualization, sensors, and even the integration of artificial intelligence into planning and policy-making contexts in a democratic and ethical manner. At the same time, the fields of computer science and machine learning can benefit from the urgency and “hands-on” nature of the sorts of challenges presented in policy-making and urban planning contexts and can lead to democratic and ethical innovations of technology. In short: urban planners have excellent problems, and computer scientists have excellent tools.

The 11-6 degree aims to help undergraduates use their computer science skills to make positive social impacts. Students will learn the theory and practice of (1) urban planning and policy-making including ethics and justice; (2) statistics, data science, geospatial analysis, and visualization, and (3) computer science, robotics, and machine learning.

To accomplish these ends, the required subjects include core courses in both computer science and urban planning fundamentals, as well as lab and project-based courses that will help students synthesize and integrate across the two departments. On the Urban Studies and Planning side, students will also receive a grounding in the political, sociological, legal, and ethical aspects of collecting and using new information flows. On the Computer Science side, they will enhance their skills in programming, statistics, data visualization, applied spatial analysis and machine learning.

For the predominantly technically-minded undergraduates at MIT, working within real urban contexts and environments will expose them to:

• Fundamental and socially-relevant questions of equity, fairness, diversity, and implementation in a global context;
• Specific applications of technology and systems in environmental management, transportation, infrastructure financing, cybersecurity, provision of housing, and job creation.
• Diverse contexts in which technology is tested and used, especially at the critical intersections between government and industry; policy-making and implementation; and in both the developed and developing world.

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Do you have questions about the major, classes, or on-going research? Want to get involved with the new major or discuss how 11-6 would prepare you for future endeavors? Listed below are volunteer faculty and staff who are eager to answer your questions, please reach out to them directly via the provided email address.

Eran Ben-Joseph Professor of Landscape Architecture and Urban Planning [email protected]

Eran Ben-Joseph is the Class of 1922 Professor of Landscape Architecture and Urban Planning in the Department of Urban Studies and Planning at the Massachusetts Institute of Technology. Eran served as Head of the Department of Urban Studies and Planning at MIT from 2013 to 2020.

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Sarah Williams Associate Professor of Technology and Urban Planning [email protected]

Sarah Williams is an Associate Professor of Technology and Urban Planning. She also is Director of the Civic Data Design Lab at MIT’s School of Architecture and Planning. The Civic Data Design Lab works with data, maps, and mobile technologies to develop interactive design and communication strategies that expose urban policy issues to broader audiences. Trained as a Geographer (Clark University), Landscape Architect (University of Pennsylvania), and Urban Planner (MIT), Williams’s work combines geographic analysis and design. Williams is most well known for her work as part of the Million Dollar Blocks team which highlighted the cost of incarceration, Digital Matatus which developed the first data set on a informal transit system searchable in Google Maps, and a more a recent project that uses social media data to understand housing vacancy and Ghost Cities in China.

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David Hsu Associate Professor of Urban and Environmental Planning [email protected]

David is an Associate Professor in the Department of Urban Studies and Planning at the Massachusetts Institute of Technology. His research and teaching areas focus on how to use environmental policy and planning to shape cities to become more efficient in their use of resources, more livable, and healthier. Much of his work seeks to assist local policymakers and environmental advocates directly in the stages of policy design and implementation.

Joe Ferreira Professor, Post-Tenure [email protected]

Professor Ferreira was the founding director of the Planning Department’s Computer Resource Lab and is now head of Urban Information Systems. He teaches analytical methods and computer-based modeling for planning and urban management including courses involving extensive use of geographic information systems (GIS) and database management. Both Prof. Ferreira’s undergraduate degree (in electrical engineering) and his PhD degree (in operations research) are from MIT. His research uses GIS and interactive spatial analysis tools to model land use, transportation, and environmental interactions and to build sustainable information infrastructures for supporting urban and regional planning. He is a past-president of the Urban and Regional Information Systems Association (URISA) and has been principal investigator of numerous research projects studying job‐housing balance, urban performance measures, and urban information infrastructure. His current research includes the Future Urban Mobility project within the Singapore/MIT Alliance for Research and Technology where he is the SMART Research Professor of Urban Information Systems.

• Urban Information Systems

Andres Sevtsuk Associate Professor of Urban Science and Planning [email protected]

Andres Sevtsuk is a Charles and Ann Spaulding Career Development Associate Professor of Urban Science and Planning at the Department of Urban Studies and Planning, where he also leads the City Form Lab. His work bridges urban design with spatial analysis and urban technology. He has led various international research projects; exhibited his research at TEDx, the World Cities Summit and the Venice Biennale; and received the President’s Design Award in Singapore, International Buckminster Fuller Prize and Ron Brown/Fulbright Fellowship. Before joining MIT, Andres was an Associate Professor of Urban Planning and Design at the Harvard Graduate School of Design. He holds a PhD from the Department of Urban Studies and Planning and an SMArchs in Architecture and Urbanism from MIT.

Catherine D’Ignazio Associate Professor of Urban Science and Planning [email protected]

Catherine D’Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT. She is also Director of the Data + Feminism Lab which uses data and computational methods to work towards gender and racial equity, particularly as they relate to space and place. D’Ignazio is a scholar, artist/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. Prior to joining DUSP, D’Ignazio was an Assistant Professor of Data Visualization and Civic Media at Emerson College in the Journalism Department, taught for seven years in the Digital + Media graduate program at Rhode Island School of Design and did freelance software development for more than ten years. She holds an MS from the MIT Media Lab, an MFA from Maine College of Art, and a BA in International Relations (Summa Cum Laude, Phi Beta Kappa) from Tufts University.

Eric Huntley Lecturer of Urban Science and Planning [email protected]

Eric Huntley joined DUSP in August 2017 as a Technical Instructor of GIS, Data Visualization and Graphics. Huntley’s work combines methods and forms of representation in novel ways to produce compelling and visually-rich narratives that draw from geography, data science, and design. He has years of GIS teaching experience and an expansive skillset that includes data visualization, GIS and spatial analysis, web mapping, urban design representation, and media production. His design work has been exhibited at the Harvard Graduate School of Design and the University of Michigan’s Taubman College of Architecture & Urban Planning.

Do you have general questions about the 11-6 major such as: is this the right major for me? How can I be involved as a faculty advisor? Feel free to reach out to any of the individual faculty or staff members above or contact Sandra M. Elliott ( [email protected] ).

Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

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 .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

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Interdisciplinary Graduate Degrees

Computation and cognition (course 6-9p), computer science and molecular biology (course 6-7p), design and management (system design and management & integrated design and management), history, anthropology, and science, technology and society , oceanography and applied ocean science and engineering.

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Open to EECS undergraduates in 6-1, 6-2, 6-3, 6-4, 6-7, and 6-14 programs, the MEng program allows students to earn a bachelor’s and master’s degree in five years. They may earn the degree simultaneously or sequentially. Students study their field in greater depth by taking graduate subjects, completing a professional perspective requirement, and completing a thesis. Theses can be completed in a research lab on campus or with an industry partner through the 6-A program .

MEng students often begin their research as undergraduates, either through the UROP or SuperUROP programs. We encourage students to apply in their junior or senior years so that they have plenty of time to prepare for the MEng, if accepted.

Rawls College of Business

Master of science in data science.

Ranked the No. 3 best online, non-MBA program in the nation in 2022 , the Rawls College Master's in Data Science (MSDS) program provides graduates with the technical expertise needed to lead in the digital frontier. Through our 36-hour, STEM-designated program, learn how to manage, analyze and understand complex data to make strategic decisions. Upon graduation, you will have the skills and knowledge needed to be an agile data scientist capable of making impactful decisions across a variety of business settings and industries.

program highlights

Flexible format, on-campus or online classes.

Options to complete coursework on campus or online allow you to choose the course modality that best fits your personal needs.

One or Two-Year Options

Complete your degree in as little as a year, or take fewer classes per semester by selecting the online, two-year option.

Optional Practical Training (OPT) Eligible

International students may qualify to work in the U.S. for up to three years after receiving their degrees.

Cutting-Edge Curriculum

We prioritize the real-world application of knowledge and skills to best support students who want to accelerate their careers in data science, business analytics, business intelligence and big data fields. Through our comprehensive curriculum, you will learn how to use advanced technologies and statistical methods to manipulate data and translate findings into actionable organizational strategies.

Classes include foundational building blocks for today's data scientists:

Statistics for Data Science

Scripting Languages

Database Concepts

Data Technology Environments

Big Data Strategy

Business Intelligence

Multivariate Analysis

Time Series Analysis

Simulation & Optimization

Machine Learning

Decision Theory and Business Analytics

Big Data Security

program format

The MSDS program requires 36-credit hours, consisting of specialized data science courses. This is a lock-step program, requiring students to take classes in a specific order, as concepts build on each other. The program begins in the summer, and summer courses are four to five weeks in length. Fall and spring courses are seven to eight weeks in length. Coursework for the one-year program can be completed on campus or online. The two-year program is available online only.

Working professionals experience their core courses together, creating a stimulating cohort-based learning environment. During your tenure in the program, you will build relationships with peers, emanating from diverse backgrounds and industries, resulting in a larger professional network upon graduation.

View sample degree programs »

Our MSDS faculty members include engaged technology practitioners who utilize real-world experience to inspire their instruction. Their areas of expertise include computer-aided decision making, information requirements determination, operations management, health care analytics, information economics and more.

Meet our faculty »

class profile

At Rawls College, we believe diversity drives opportunities for collaboration and learning. Together, we benefit from the many perspectives, skills and experiences our students from all over the world bring to our learning environment.

supporting your success

Rawls Career Management Center

Whether you are looking to switch careers or advance on your current path, the Rawls Career Management Center (CMC) is dedicated to supporting your success. The staff in the CMC can help you explore professions and industries, learn strategic career advancement techniques, and connect you with top employers.

Techsan Connection

The Techsan Connection is a free, online platform for Texas Tech alumni. Through the platform, alumni can apply to jobs, reconnect with fellow classmates, network with industry professionals and volunteer to mentor current students.

The admission process is the first step toward earning your degree. We will work closely with you to ensure your application process is personal, simple and successful.

Application Requirements

While no prior work experience is required, applicants must have a bachelor's degree. Most applicants have an education or work background in computer science, management information systems, science, engineering, or similar fields. Basic knowledge of computer programming software such as R, SQL, and Python will be beneficial throughout the program's coursework. Additionally, applicants will benefit from prior completion of coursework in calculus, statistics and probability.

Unofficial Transcripts

Applicants must submit unofficial transcripts from any degree-awarding college or university, as well as any post-secondary institution attended. 

Applicants must submit a detailed current resume, indicating professional work experience—including start and end dates (month and year) for each position held. Provide accomplishments and skills acquired, including managerial experience.

GMAT Scores

The summer 2023 intake will not require a GRE or GMAT for application, but submission of scores are encouraged. 

We don't have a set minimum or maximum requirement for test scores. We review students holistically taking the application in its entirety into consideration.

English Proficiency for International Students

All international applicants must provide proof of English proficiency before their applications can be considered for admission. Only your most recent measure of English proficiency is considered for admission purposes. This test is waived only for graduates of U.S. universities or universities in English proficiency-exempt countries. Applicants who have completed at least two consecutive years at a college or university in the U.S. or in an English proficiency-exempt country are also exempt from the English proficiency requirement.

Application Deadlines

Summer Entry: May 1

International students are encouraged to apply at least six months in advance when possible.

student resources

• Prospective Students
• Current Students

Program Questions

[email protected] 806.742.3184

Cy Cawthron 806.834.1069

Events@Rawls

Professional mba weekend classes.

Saturday, June 1, 2024 - Sun , June 2, 2024 (all day)

Where: Rawls College of Business

Saturday, June 29, 2024 - Sun , June 30, 2024 (all day)

Where: Center for Business Communications Room 139

Contact TTU

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