phd nyu data science

NYU Center for Data Science

Harnessing Data’s Potential for the World

CDS-Courant Undergraduate Research Program (CURP)

PROGRAM DATES: JANUARY 24 – MAY 18, 2022 LOCATION: RESEARCH PROJECTS WILL TAKE PLACE REMOTELY FELLOWSHIP AWARD: $3,500 APPLICATION DEADLINE: OCTOBER 31, 2021

The NYU Center for Data Science

The Center for Data Science (CDS) is the focal point for New York University’s university-wide efforts in Data Science. The Center was established in 2013 to advance NYU’s goal of creating a world-leading Data Science training and research facility, and arming researchers and professionals with the tools to harness the power of Big Data. Today, CDS counts 20 jointly appointed interdisciplinary faculty housed on three floors of our magnificent 60 5th Avenue building, one of New York City’s historic properties. It is home to a top-ranked MS in Data Science program, one of the first PhD programs in Data Science, and a new undergraduate program in Data Science, as well as a lively Fellow and Postdoctoral program. It has over 70 associate and affiliate faculty from 25 departments in 9 schools and units. With cross-disciplinary research and innovative educational programs, CDS is shaping the new field of Data Science.

March 2024 Monthly Research Feature

A new month brings fresh research explorations. Brenden Lake trains AI systems through the lens of a single child, while Yanli Zhou examines how humans and AI models achieve visual compositionality. This month’s CDS Research Feature also delves into neural networking training and more intriguing studies.

Data Science for Everyone: Book & Video Series

The universality of data science as a discipline is highlighted with the introduction of a comprehensive textbook and accompanying video series, building upon the foundation laid by the esteemed “Data Science for Everyone” course. These educational materials provide extensive coverage on critical concepts ranging from “how to think like a data scientist” to core subjects such as statistics, programming, and machine learning approaches.

Free Online Resource: Yann LeCun’s Deep Learning Course

Yann LeCun’s Deep Learning Course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Free Online Resource: Mathematical Tools for Data Science

Mathematical Tools for Data Science, developed by CDS Associate Professor Carlos Fernandez-Granda, provides an introduction to tools from several areas of mathematics such as linear algebra, Fourier analysis, probability theory, and convex optimization, which are useful in data science.

CDS Shines at NeurIPS 2023

Celebrating Success: CDS MS Graduates Forge Impressive Paths in Data Science

Micah Goldblum’s Survey Offers a Deeper Look Into Deep Learning

Hiring Clinical Faculty for 2024: Interview with current CDS Clinical Assistant Professor Louis Mittel

Upcoming events.

• Data Science Lunch Seminar Series: Brian Trippe (Columbia) March 27, 2024 , 12:30 pm
• Math and Data (MaD) Seminar: Zach Izzo (NEC Labs) March 28, 2024 , 2:00 pm
• Data Science Lunch Seminar Series: Julian Michael (NYU) April 10, 2024 , 12:30 pm
• NLP and Text-as-Data Speaker Series: Roger Levy (MIT) April 11, 2024 – April 12, 2024 , 4:00 pm
• Math and Data (MaD) Seminar: Tristan Buckmaster (NYU) April 18, 2024 , 2:00 pm
• Data Science Lunch Seminar Series: Umang Bhatt (NYU) April 24, 2024 , 12:30 pm
• NLP and Text-as-Data Speaker Series: Lena Voita (Meta) April 25, 2024 , 4:00 pm
• Math and Data (MaD) Seminar: Guy Bresler (MIT) May 9, 2024 , 2:00 pm

Events in March–June 2024

PhD | Information Systems

Information Systems

The ph.d. in information systems.

Information Systems (IS) is a multidisciplinary field that is growing in importance as the information technology revolution transforms the scale of data, the nature of products, the organization of work, and the way people interact. The Stern IS department has played an active leadership role in defining the fields of data science and business analytics, engaging in cross-disciplinary scholarship in computer science, economics, machine learning, marketing, management science and urban science. Ph.D. students at Stern have the advantage of working with IS faculty who also maintain close research ties with the digital, banking, advertising and media industries, which gives our students access to unique “big data” for research as well as opportunities for experiential learning.

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Getting a PhD in Data Science: What You Need to Know

A PhD in data science prepares you for some of the most cutting-edge research in the field and can advance your career. But, whether you should pursue one depends on your own personal goals and resources. Learn more inside.

A Doctor of Philosophy (PhD) is the highest degree that a professional can obtain in the field of data science. Focused primarily on equipping degree holders with the skills and knowledge required to conduct original research, a PhD prepares degree holders for advanced professional positions in both industry and academia. 

But, the path to obtaining a PhD is filled with many years of potentially costly study that can be discouraging to those looking for rapid career progression. Before jumping into a doctoral program, then, it’s important to define what your goals are and how a PhD may (or may not) fit into them. 

In this article, you’ll learn more about PhDs in data science, the different factors you should consider before joining one, and types of programs to consider. At the end, you’ll also find some suggested online courses to help you get started today. 

PhD in Data Science: Overview 

A Doctor of Philosophy (PhD) is the terminal degree in the field of data science, meaning it is the highest possible degree that can be obtained in the subject. Holding a PhD in data science, consequently, signals your mastery and knowledge of the field to both potential employers and fellow professionals. 

At a glance, here’s what you should know about a Data Science PhD: 

PhD vs. Master’s Degree in Data Science

There are two graduate degrees in the field of data science: a master’s in Data science and a PhD in Data Science. While both of these degrees can have a beneficial impact on your job prospects, they also have key differences that might impact which one is better for you. 

A Master’s in Data Science is a graduate degree between a bachelor’s and PhD, which usually takes between one and two years to complete. A master’s degree expands on what was learned in undergraduate school through more advanced courses in topics such as machine learning, data analytics, and statistics. Often, a master’s student in data science also pursues original research and completes a capstone project, which highlights what they learned in their program.

A PhD in Data Science is a research degree that typically takes four to five years to complete but can take longer depending on a range of personal factors. In addition to taking more advanced courses, PhD candidates devote a significant amount of time to teaching and conducting dissertation research with the intent of advancing the field. At the conclusion of their doctoral program, a PhD holder in Data Science will complete a dissertation representing a significant contribution to the field. 

Typically, bachelor’s degree holders entering a PhD program are able to earn their master’s degree as a part of their doctoral program. Those entering a master’s program, however, will usually have to apply for a PhD program even if it’s in the same department. 

Skills and curriculum 

Every PhD program is unique with its own requirements and focus. Nonetheless, they do have similar features, such as course, credit, and teaching requirements. To help you get a better understanding of how a doctoral graduate program in data science might be, here’s an example curriculum from NYU [ 1 ]: 

Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.

Core courses in topics like probability, statistics, machine learning, big data, inference, and research. 

39 credit hours for elective courses in such topics as deep learning, natural language processing, and computational cognitive modeling. 

Complete teaching requirements.

Pass a comprehensive exam. 

Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester. 

Complete all steps for approval of their PhD dissertation. 

Is a PhD in Data Science worth it? 

A PhD can open doors to new career opportunities and boost your employment prospects. But, it can also take a lot of time and money to complete. Everyone’s personal and professional goals are different, so consider these things when deciding if you should pursue a PhD in Data Science:  

Cost and time

The amount of time and money it takes to complete a PhD are perhaps the most concrete considerations one makes when deciding whether or not they should pursue a doctoral degree. According to research conducted by Education Data Initiative, the average cost of a doctorate degree is $114,300 and takes roughly four to eight years to complete [ 2 ]. 

The exact amount of time and money you might spend obtaining your doctoral degree will depend on your own circumstances and program. Before applying for a doctoral degree, make sure to review each program’s graduation requirements and costs, so you have a clear understanding of what you’re getting into. 

Data Science PhD salary 

While there are no official statistics on the salary gains data scientist earn by getting a PhD, the median salary for all data scientists is much higher than the national average in the United States. According to the U.S. Bureau of Labor Statistics (BLS), for example, the median salary for data scientists was $100,910 as of May 2021 [ 3 ]. 

Typically, the entry-level degree to get a data science position is a bachelor’s degree, meaning that even just an undergraduate degree could help you land a job that earns a higher than average salary. Nonetheless, a PhD will likely prepare you for more advanced positions that could offer higher pay than less specialized roles. 

Data Science PhD programs 

There are several types of doctoral programs that you might consider if you would like to obtain a PhD in data science. These include: 

PhD in data science online

An online PhD program may appeal to individuals who are interested in a more flexible program that allows them to complete their coursework at their own pace. Often, online programs can also be cheaper than their in-person counterparts, though they often offer less opportunities for networking and mentorship. If you’re an independent, self-starter looking for a program that can fit into their already busy life, then you might consider an online PhD program. 

PhD in data science in-person

An in-person PhD program is a more traditional, educational method in which you attend classes on campus with your peers and instructors. In addition to providing doctoral-level instruction, you will also have more opportunities to network and gain more personalized instruction than you will likely encounter through online programs. In-person programs tend to be more expensive and inflexible than in-person ones.

If you prefer real-world instruction, networking opportunities, and a more rigid structure, then you might consider an in-person doctoral program. 

Alternatives 

As an alternative to a PhD program, you might also consider obtaining a master’s degree. While covering some of the same material as a doctoral program, a master’s usually takes much less time and money to complete.

If you’re motivated primarily by the desire to boost your chances of landing a job and gaining financial stability, then a master’s degree program might better help you achieve your goals.

Learn more about data science 

Whatever your educational goals, data science requires extensive knowledge and training to enter the profession. To prepare for your next career move, then, you might consider taking a flexible online course through Coursera. 

The University of Colorado Boulder’s Data Science Foundations: Data Structures and Algorithms Specialization teaches course takers how to design algorithms, create applications, and organize, store, and process data efficiently. Their online Master of Science in Data Science , meanwhile, teaches broadly applicable foundational skills alongside specialized competencies tailored to specific career paths in just two years of instruction. 

Article sources

NYU Center for Data Science. “ PhD in Data Science, Curriculum , https://cds.nyu.edu/phd-curriculum-info/.” Accessed September 27, 2022. 

Education Data Initiative. “ Average Cost of a Doctorate Degree ,  https://educationdata.org/average-cost-of-a-doctorate-degree.” Accessed September 27, 2022. 

US BLS. “ Occupational Outlook Handbook: Data Scientists , https://www.bls.gov/ooh/math/data-scientists.htm#tab-1.” Accessed September 27, 2022. 

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

NYU Center for Data Science

Harnessing Data’s Potential for the World

Master’s in Data Science

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The  Fall 202 4   application deadline was Monday, January 22, 2024, 5pm ET.

The Fall 2024 MS Admissions Information Session took place Thursday, October 19 at 10am.

Please note that we do not have Spring Admissions for the MSDS.

Admission to NYU’s Master of Science in Data Science is extremely competitive. This speaks both to the popularity of the field of Data Science and to the very high calibre of students who we seek as part of our program.

Without exception, you must submit the following to support your application for admission:

• TOEFL or IELTS; however, TOEFL is preferred (Required for all applicants whose native language is not English and who have not received a university degree in an English-speaking country)
• Official college transcripts
• Three letters of recommendation (we prefer all letters on letterhead)
• Statement of Academic Purpose
• (optional) Personal History Statement
• (optional) GRE scores

For more information, visit the graduate schools  application resource center .

Educational Prerequisites

Successful applicants to the MSDS come from many different undergraduate backgrounds, including degrees in Statistics, Computer Science, Mathematics, Engineering, Economics, Business, Biology, Physics and Psychology. In the 2023 intake cycle, the average GPA was 3.80. Our students’ transcripts usually include As and Bs (only), and we expect stronger grades in more relevant subject matter (see below) from those coming from less selective institutions. Regardless of degree, we require specific and substantial knowledge of certain mathematical competencies, and some training in programming and basic computer science.

To be considered for the program, you will be required to have completed the following (or equivalents, e.g. MOOCs certification or course credit):

• Calculus I: limits, derivatives, series, integrals, etc.
• Linear Algebra
• Intro to Computer Science (or an equivalent “CS-101” programming course): We have no set requirements as regards specific languages, but we generally expect serious academic and/or professional experience with Python and/or R  at a minimum .
• One of Calculus II, Probability, Statistics, or an advanced physics, engineering, or econometrics course with heavy mathematical content

Preference is given to applicants with prior exposure to machine learning, computational statistics, data mining, large-scale scientific computing, operations research (either in an academic or professional context), as well as to applicants with significantly more mathematical and/or computer science training than the minimum requirements listed above.

Work Experience

Many of our students join us directly from undergraduate, but we also very much welcome evidence of relevant work experience—and clear employment goals once the MSDS is completed—in data science.  Past experience and career aspiration goals can be related to commercial industry, government, academia or some other sector.

Standardized Tests

GRE General Test is optional. If submitting, please note: we do not accept “out of date” scores (see FAQs here ); nor do we accept scores of other, similar tests.  

If submitting scores for the GRE General test with your application, please upload a PDF of the unofficial scores, which are made available upon completion of your test, to the “Additional Information Section” of your application. This is in addition to sending your official scores to the Graduate School of Arts and Science.

We also require evidence of proficiency with English as a second language for certain students who must provide it. For those students, we recommend a TOEFL score of at least 100 overall (and have strong preferences for better scores).

For additional information regarding standardized testing please visit the Graduate School of Arts and Science’s FAQ Page .

Three Letters of Recommendation

Recommendations for admitted students are invariably excellent, with references holding applicants in the highest esteem relative to other students or employees with whom they have interacted in the past several years. References from professors or employers who can comment directly and in a detailed way on the applicant’s case, aptitude for, and attitude to data science projects are treated with the most weight. Though not required, we prefer all letters on letterhead.

For additional information regarding letters of recommendation please visit the Graduate School of Arts and Science’s FAQ Page .

International Students

Answers to common questions that international student applicants may have can be found in the Graduate School of Arts & Science’s Application Resource Center . 

In particular, questions regarding what degree must an applicant have in order to enroll are addressed here . If you have further questions about your degree eligibility, please contact the GSAS Graduate Enrollment Services team at [email protected] . 

Information about international transcript evaluations and other information about being an international student at NYU can be found here .

Internal Transfers/Current NYU Students

We do not allow internal transfers from other NYU graduate programs. If you are a current NYU graduate student and you would like to be a part of the NYU Data Science MS program, please note that you are required to submit a new application.

Ready to Apply?

If your background meets the majority of these requirements, and you have a desire to develop the methods to harness the potential of data, then we encourage you to begin the application process. Please proceed to the Graduate School of Arts and Science webpage to apply for admission . 

Prior to starting your application please review the Graduate School of Arts and Science’s general application policies page .

For more questions, email us at  [email protected] .

Deferral Requests

The Center for Data Science will not approve a request for deferral of admission. If an admitted student wishes to delay enrollment, it will be necessary to turn down the offer of admission and reapply for admission the following year. A completely new application will be required. The new application will be considered along with all other applications at that later time.

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Urban Systems, Ph.D.

The NYU Doctoral program in Urban Systems offers an interdisciplinary learning and research environment designed to meet the needs of students pursuing careers in academia, research organizations, local and national government and public service agencies. This Ph.D. program expands upon the unique legacy of decades of collaboration in education and research, development and training between NYU faculty, city agencies, and industry. The program is administered by NYU Tandon in partnership with other NYU schools including: the Stern School of Business ,  Langone Health ,  Wagner Graduate School of Public Service , and NYU research centers including the  Center for Urban Science and Progress and the Center for Connected Mobility  C2SMART . 

This program is aligned with the vision and commitment of the university to work within the ‘city as a lab’ to accelerate the field deployment of innovative solutions to emerging urban needs. Areas of study include sustainability and climate action, infrastructure and resilience, public health and equity. This interdisciplinary laboratory of urban research and innovation brings together expertise and the research excellence of NYU faculty in New York as well as our global campuses in Abu Dhabi and Shanghai, and study abroad sites in London, Paris, Berlin, Madrid, Florence, and Prague. Drawing from the expertise of the Urban Faculty across the university, we have built a unique and competitive interdisciplinary educational environment based on the following disciplinary pillars:

• Data Science & Informatics
• Communication and Information Science
• Social Studies, Health, and Policy
• Systems Engineering
• Economics, Finance, and Planning                                 

The program is available to students with diverse educational backgrounds pursuing their studies across disciplines, including Engineering, Environmental Science, Architecture, Urban Planning, Computing, Data Science, Systems Science, Economics, Finance, Public Health, Public Policy, and Law. Development of skills in mathematics, statistics, and programming is included in the course of study, depending on the need associated with the student’s dissertation topic.

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• Master or bachelor degree from an accredited program in physical and mathematical sciences, social sciences, or engineering (other fields upon approval of program administrator)
• Minimum master and bachelor degree GPA of 3.5/4.0, and 3.0/4.0, respectively.
• Submission of GRE and English language proficiency test scores

An applicant who has not yet earned a master’s degree may be directly admitted into the Ph.D. program with the written approval of the program director. Applicants with a master’s degree in any discipline other than Master of Science may be required to have prerequisites in the subjects listed below:

• Calculus with analytic geometry,
• Statistics and Probability,
• Introduction to computer programming.

In addition to these degree requirements and the NYU Tandon general admission requirements , acceptance to the program will depend on (1) academic excellence, (2) research interests congruent with areas of urban scholarship and faculty research at NYU including the global campuses, and (3) positive recommendations (e.g., from former employers or research advisors).

Note: Application and/or admission to the program does not require prior identification of a research advisor. Acceptance to the program is not automatically accompanied with financial support.

The Ph.D. curriculum includes 54 credits of graduate coursework beyond the Bachelor’s degree and 21 credits of dissertation research, totaling 75 credits. The student may use 30 credits from an approved Master’s degree completed within 10 years of admission to the program, as well as 18 additional graduate credits subject to approval of the program director. The program also includes a qualifying exam, a research proposal exam, and the dissertation defense.

To earn a doctoral degree in Urban Systems, the candidate must meet the following requirements:

54 credits of graduate coursework beyond the Bachelor’s degree (not including the Ph.D. dissertation), with cumulative average of 3.5 or better on a 4.0 scale. Up to 6 credits of the 54 credits may be satisfied by individual guided studies, readings, and projects.

Successful completion of the qualifying examination. The qualifying examination has a written section and an oral section. The written exam is based on the program’s three core courses, while the oral exam is designed to judge the students' critical thinking.

The qualifying exam is administered only once, regularly scheduled at the end of spring semester of the first year of the program

Passing of the dissertation proposal exam. This exam should be administered on or before the spring midterm of year two of the program, and signed off by the dissertation/guidance committee and submitted for the record within a week of the exam. Meeting this deadline is a requirement of the program.

Completion and successful defense of 21-credits of dissertation. Dissertations must consist of original research that advances the state of the art in the research subject area and should result in the publication of at least three papers in a peer-reviewed journal (2 published by time of defense, another under review). It is expected that the student is the first author; it is also encouraged to engage the dissertation guidance committee members to the extent that they may be justified as co-authors.

Submission of the Ph.D. dissertation following the University’s  Guidelines for Dissertations . It is encouraged that the student’s publication be planned in advance such that they may be used as the backbone of the dissertation content.

The program includes fifty-four (54) credits of graduate coursework beyond the Bachelor’s degree and twenty-one (21) credits of dissertation research. A total of 15 of the 54 credits are based on required courses, while the remainder are electives. The required courses include three Core courses (9 credits), an Urban Systems Studio (1.5 credit), and a Community Impact Project (1.5 credits) which takes place as an immersion, outside of the classroom/campus (see section on required courses). The program’s elective coursework is designed to be flexible in order to support the student’s research interests, educational background and career goals, offering an integrated education program that blends urban domains with supporting informatics content. Below are details on dissertation credits and minimum credit requirements.

a. CE-GY 998X: Dissertation level research, up to 6 credits can be taken before passing the qualifying exam.

b. CE-GY 999X: Dissertation level research. A minimum of 15 credits of CE-GY 999X must be taken after passing the qualifying examination.

c. Registration for CE-GY 998X is optional before registering for CE-GY 999x.

d. After the qualifying exam, registration for a minimum of 3 credits per term of dissertation work is required, and must be continuous (excluding summer semesters), unless a formal leave of absence is requested and approved.

e. Ph.D. candidates who have completed the 75-credit requirement, including all dissertation credits, will be permitted to maintain their student status by registering for Maintenance of Studies (MOS) every fall, spring and summer, up to the program completion (limited to total of 5 years from start of the program).

Core Courses (9 credits)

Urban Infrastructure Systems; CE-GY 7843

Monitoring Cities; CE-GY 6053

Introduction to Applied Data Science; CUSP-GX 7013

Other required courses (6 credits)

Urban Systems Studio CE-GY; 7815

Urban Systems Immersion for Social Good; CE-GY 7915 (Alternative: CP-GY 9941)

Writing and Communication for Engineers and Scientists; GA-GY 9993

Below are only selected options, other courses are permissible; please consult the program director for feedback.

Urban Systems:

Building Information Modeling: (BIM) CE-GY 8383

Disaster Risk Analysis: CE-GY 7993

Selected Topic - Climate and Energy; CE-GY 7713 / TECH-GB 2384

Urban Ecology; ENYC-GE 2070

Water, Waste and Urban Environment; FOOD-GE 2036

Data-driven Mobility Modeling and Simulation; TR-GY 7353

Forecasting Urban Travel Demand; TR-GY 6113 / CE-GY 804

Statistics and Data Science:

Artificial Intelligence I; CS-GY 6613

Algorithmic Machine Learning and Data Science; CS-GY 6763

Introduction to Data Science; DS-GA 1001

Probability and Statistics for Data Science; DS-GA 1002

Programming for Data Science; DS-GA 1007

System Optimization Methods; ECE-GY 6233

Statistics for Data Analysts; MG-GY 6193

Robotic Perception; ROB-GY 6203

Regression and Multivariate Data Analysis; STAT-GB 2301

Practicum in Applied Statistics: Applied Probability; APSTA-GE 2351

Urban Informatics:

Machine Learning for Cities; CUSP-GX 5003

Big Data Management and Analysis; CUSP-GX 6002

Applied Data Science; CUSP-GX 6001

Urban Spatial Analytics; CUSP-GX 7002

Big Data Analytics for Public Policy; CUSP-GX 2505/PADM-GP 2505

Urban Decision Models; CUSP-GX 7004

Large-scale Visual Analytics; CS-GY 6323

Geographic Information Systems and Analysis; URPL-GP 2618

Advanced GIS: Interactive Web Mapping and Spatial Data Visualization; URPL-GP 4650

Finance, Governance, Society:

Financing Urban Government; PADM-GP 4443

Project Finance and Investment; FINC-GB 3186

Citizenship Culture: Art, Urban Governance; ELEC-GG 2840

Adapting the Physical City; URPL-GP 2612

Planning for Emergencies and Disasters; URPL-GP 2645

Environmental Infrastructure for Sustainable Cities; URPL-GP 2625

History and Theory of Planning; URPL-GP 2660

Research Methods; PHD-GP 5902

Qualifying Exam

The qualifying exam will be administered shortly after the completion of the second semester of the programs first year. The exam will be in two parts, written and oral. The written portion will be based on the program core courses, while the oral portion is meant to judge student’s skills in critical thinking and to assess the student’s ability to carry out independent research. This exam has a pass/fail grade, and may not be retaken.

Dissertation

Students declare a dissertation/research advisor during the fall semester of year two, shortly after passing the Ph.D. qualifying exam. The student and the advisor will subsequently select a dissertation guidance committee by start of the spring semester of the same academic year. The guidance committee will be composed of the research advisor and three other faculty members including one external advisor (from another institution or from an NYU school other than the primary advisor). The function of the dissertation guidance committee is to monitor and support the student’s progress on an ongoing basis, starting from the dissertation proposal planning. Declaration of the primary advisor and the dissertation committee is done by submitting the designated forms according to the timeline described above.

The research proposal examination, overseen by the dissertation guidance committee, must be passed by the spring midterm of program’s second year. The objective of this exam is to ensure the student has chosen an appropriate Ph.D. research topic and that the research plan is rigorous with a high likelihood of success. The results of each student’s proposal examination must be submitted by the primary advisor no later than one week following the exam, along with the proposed scope of work, the student and the dissertation committee copied. A memo on passing of this exam and the committee composition will be documented at the NYU Tandon graduate affairs office. Failing to pass this exam in a timely fashion may result in the student being placed under probation.

At end of each term, the student submits a progress report outlining the term’s academic progress. Subsequent to passing the proposal examination, the progress report should be signed off by the dissertation guidance committee prior to submission. 

With the dissertation research advisor and the dissertation guidance committee’s approval, the student will submit a written dissertation, in compliance with all requirements of NYU Tandon. It is expected that the student has published at least three articles in a reputable peer reviewed journal (two accepted and one under review). The dissertation must be provided to the guidance committee members who also serve as the examination committee, at least two weeks prior to the defense. The defense includes a public presentation by the student and with questions from the audience. Following the public presentation, the student meets privately with the committee members for comments and/or further questions. The committee makes a decision that is then transmitted, in writing, to the program director and there from to the registrar.

Interruptions in the Course of Study

Given unforeseen circumstances that warrants an interruption in the course of study, the student may request a leave of absence, term withdrawal, or total withdrawal. Further information on leave of absence may be found HERE .

Application for Graduation

Students should apply for graduation via Albert. The application must be completed before the deadline set by the Registrar’s office, which can be found on the NYU Academic Calendar.

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NYU Shanghai Establishes New Data Science PhD

NYU Shanghai is partnering with the NYU Graduate School of Arts and Science (GSAS) and the NYU Center for Data Science (CDS) to launch a new PhD program in Data Science in the fall of 2020. The five-year program is NYU Shanghai’s eighth PhD offering , and will combine coursework with research in New York City and Shanghai. 

“Data science will become increasingly important in the years to come,” says Assistant Professor Faculty Fellow of Data Science Ling Shuyang , who will serve as a supervising faculty member in the program. “Students will have access to the rich resources at the Center for Data Science in New York and conduct research in Shanghai, a global hub in data science, IT, finance, and manufacturing.”

Ling Shuyang, the Assistant Professor Faculty Fellow of Data Science

NYU Shanghai’s Data Science PhD students will build a solid mathematical foundation in data science, apply algorithms to solve practical problems, and most importantly, be equipped to conduct independent research, says Ling.

Like many of NYU Shanghai’s Master’s and PhD offerings, the program takes advantage of the academic resources and research communities within NYU’s global network. During their first two years in the program, PhD candidates will spend fall and spring semesters in New York, joining their peers at NYU GSAS and studying at the NYU Center for Data Science. In the summer, students will be based in Shanghai, conducting research under the supervision of NYU Shanghai faculty such as Ling. After their first two years, students will pursue the remainder of their program full-time in Shanghai. Upon graduation, students will receive their doctorate from NYU.

PhD candidates will remain a vital part of the NYU academic community throughout their five years in the program, with opportunities to attend seminars, meet established academics in the field, and network with students and faculty from other universities. They will also engage with faculty, researchers, and PhD students at NYU Shanghai’s Institute for Data Science and Artificial Intelligence , as well as graduate students enrolled in NYU Shanghai’s Master of Science degree in Data Analytics and Business Computing.

Dean of Engineering and Computer Science Keith Ross says that interdisciplinarity is a hallmark of the program. “NYU Shanghai has great faculty and students in mathematics, computer science, and artificial intelligence,” says Ross. “Because data science is at the nexus of these three fields, and because data science and its applications to finance, science, social science, and artificial intelligence are the focus of many of our faculty, NYU Shanghai is a great place to pursue data science.” 

Based in New York, the NYU CDS PhD program in Data Science is among the top-ranking doctoral programs in the field and has been recognized by the National Science Foundation. 

The program boasts a world-renowned faculty team, including leaders in the field of data science. The team consists of 16 joint faculty, 11 associated faculty, and over 50 affiliated faculty who possess strong interdisciplinary academic backgrounds, and engage in research spanning a broad spectrum of subjects such as computer science, mathematics, neuroscience, linguistics, politics, physics, biology, engineering, and business. 

Applicants accepted into the program will be fully-funded under the NYU Shanghai Doctoral Fellowship, which covers all tuition and fees, international health insurance, travel funds, and an annual stipend for PhD students at NYU Shanghai. The deadline for applications for the school year beginning fall 2020 is December 12, 2019. 

For more information about the PhD in Data Science program, visit the PhD Program in Data Science webpage and the NYU Center for Data Science PhD webpage. Read the NYU GSAS PhD in Data Science requirements to find out if you qualify, and submit your application through the NYU GSAS Application portal . For further questions and inquiries, please email [email protected] .

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Data Science (MS)

Program description.

The Master of Science in Data Science is a highly-selective program for students with a strong background in mathematics, computer science, and applied statistics. The degree focuses on the development of new methods for data science.

We live in the “Age of the Petabyte,” soon to become “The Age of the Exabyte.” Our networked world is generating a deluge of data that no human, or group of humans, can process fast enough. This data deluge has the potential to transform the way business, government, science, and healthcare are carried out. But too few possess the skills needed to use automated analytical tools and cut through the noise to create knowledge from big data.

A new discipline has emerged to address the need for professionals and researchers to deal with the “data tidal wave.” Its object is to provide the underlying theory and methods of the data revolution. This emergent discipline is known by several names. We call it “data science,” and we have created the world’s first MS degree program devoted to it.

All applicants to the Graduate School of Arts and Science (GSAS) are required to submit the  general application requirements , which include:

• Academic Transcripts
• Test Scores  (if required)
• Applicant Statements
• Résumé or Curriculum Vitae
• Letters of Recommendation , and
• A non-refundable  application fee .

Admission to the Master of Science in Data Science requires substantial but specific mathematical competencies, typical of a major in mathematics, statistics, engineering, physics, theoretical economics, and computer science with sufficient mathematical training. In addition, applicants should have some training in programming and basic computer science. Preference is given to applicants with prior exposure to machine learning, computational statistics, data mining, large-scale scientific computing, operations research (either in an academic or professional context), as well as to applicants with significantly more mathematical and/or computer science training than the minimum requirements listed above.

See Data Science for admission requirements and instructions specific to this program.

Program Requirements

The program requires the completion of 36 credits, and offers a concentration in Industry.

Industry Concentration

Students have the opportunity to pursue a specialization through the Industry Concentration. This concentration is specifically targeted to respond to the needs and inputs from companies and allows MS in Data Science students to apply the knowledge and skills obtained in their coursework to industry during the degree program. It requires more industry-targeted coursework and a Practical Training experience. Students in this concentration will be required to take the following courses for the degree as a part of the 36 credit requirement:

• DS-GA 1009 Practical Training for Data Science Practical Training for Data Science within the first year of the program (3 credits in fall, spring, or summer), and
• Two electives within the Big Data or Natural Language Processing subject areas (6 credits).

Additional Program Requirements

One of the key features of the MS in Data Science curriculum is a capstone project that makes the theoretical knowledge gained in the program operational in realistic settings. During the project, students go through the entire process of solving a real-world problem; from collecting and processing real-world data, to designing the best method to solve the problem, and finally, to implementing a solution. The problems and datasets come from real-world settings identical to what might be encountered in industry, academia, or government.

Sample Plan of Study

Learning outcomes.

Upon successful completion of the program, graduates will have:

• The knowledge and skills needed to develop new methods for data science.
• The skills needed to use automated analytical tools and cut through the noise to create knowledge from big data.
• The ability to solve a real-world problem from collecting and processing real-world data, to designing the best method to solve the problem, and finally, to implementing a solution.
• An awareness of the social and ethical implications of data-driven methods, alongside tools to address and mitigate associated biases.

NYU Policies

Graduate school of arts and science policies.

University-wide policies can be found on the New York University Policy pages .

Academic Policies for the Graduate School of Arts and Science can be found on the Academic Policies page . 

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Data Sources for Physical and Earth Sciences This guide covers data sources for astronomy & space, climate & weather, environment & ecology, geology & oceanography, and social issues such as human interactions in the environment.

• Data Citation Index This link opens in a new window Searches across research data sets contained in subject-specific and institutional data repositories.
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• NOAA National Climatic Data Center (NCDC) NOAA's National Climatic Data Center (NCDC) is the world's largest repository of weather and climate data and information. Data sources are as varied as paleoclimatic samples to handwritten observations to satellite and radar imagery less than an hour old.
• Sloan Digital Sky Survey (SDSS) The Sloan Digital Sky Survey (see www.sdss.org for general information) will map one-quarter of the entire sky and perform a redshift survey of galaxies, quasars and stars.
• Smithsonian/NASA Astrophysics Data System The Astrophysics Data System (ADS) is a NASA-funded project which maintains three bibliographic databases containing more than 4.8 million records in Astronomy and Astrophysics, Physics. Also includes ArXiv e-prints.
• WebCSD This link opens in a new window WebCSD is the web-based interface to the Cambridge Structural Database. It includes chemical, physical, and bibliographic information for small-molecule organic and metal-organic crystal structures.
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State of Affairs: Spring Updates for Biomedical Informatics & Data Science

Updates from the chair, lucila ohno-machado, md, mba, phd.

In this newsletter, we are focusing on new educational programs organized by BIDS in collaboration with faculty from multiple departments. These programs consist of formal graduate programs, certificate programs, and trainings to enhance data science literacy in our community.

Dr. Cynthia Brandt reorganized a master’s degree in Computational Biology and Biomedical Informatics (CBB). Even with minimal advertisement this program already attracted 103 applications, and the admissions committee selected applicants with a hope that 20 students will accept for this inaugural class. A master’s degree in health sciences, targeting primarily junior faculty and fellows at Yale, now has a track in clinical informatics and data science, also organized by Dr. Brandt. We continue to participate in the CBB doctoral program, which attracted 371 applications for a class size of 10 this year.

Formal graduate degrees will continue to evolve in collaboration with other departments. In addition, it is also important that we fill educational gaps with non-degree programs.

With an agile curriculum that can be followed online from anywhere in the world, Dr. Xenios Papademetris developed a Certificate Program in Medical Software and Medical Artificial Intelligence (AI) , which was open to applications in early 2024. The goal is to offer the most up to date information and best practices to those who need it immediately. With faculty from multiple departments, this new program will cover not technical and regulatory aspects of medical computer-based systems in practical manner, and it will give an introduction to downstream effects of these systems in real life. Out of around 70 applicants, 16 were selected to compose the inaugural cohort. Several participants come from industry, and BIDS faculty help teach an exciting curriculum covering both technical and regulatory aspects of medical software and medical AI. Some course materials are publicly available on the BIDS YouTube channel .

A summer intern program involving students from backgrounds traditionally under-represented in informatics and data science is another new offering this year. Dr. Annie Hartley has organized a curriculum focused on medical applications of AI, with emphasis on fairness and accessibility. BIDS will also participate in Yales’ SURF program this year.

Trainings for hands-on use of clinical and biomedical data are now being coordinated with the Cushing Library, with the goal of familiarizing the Yale community with data from the NIH All of Us Research Program , the Epic Cosmos Research Program (containing electronic health records from over 200 institutions in the US and abroad), and with other relevant datasets licensed for research (e.g., Marketscan). Informal training also happens when BIDS faculty collaborate with different investigators and centers across Yale.

BIDS’ role in Yale’s community of scholars will expand as we continue to recruit faculty, staff, and students from various backgrounds who have a common mission: enable clinicians and researchers make maximal use of data and algorithms to promote human health. As we complete one year, we reflect on our initial achievements, which would not be possible without an outstanding team and multiple collaborators. In addition to 12 primary ladder track faculty, we are jointly recruiting with several departments, and have 13 non-ladder faculty and over 40 secondary faculty appointees in BIDS. Our growing staff specialized in various research, education, service and administrative functions, and our faculty, staff, and student teams represent cultures from all over the world. We are grateful to various offices who supported our work, and eager to expand our outreach even more this coming year.

Related Links

• How to Train a Biomedical Data Scientist (BIDS Spring 2024 Newsletter)
• At the Intersection of AI and Medicine (BIDS Spring 2024 Newsletter)
• Branching Out: Annie Hartley Envisions New D-tree Collaborations (BIDS Spring 2024 Newsletter)
• MD Students Explore the Big Data Issue (BIDS Spring 2024 Newsletter)
• Welcome to New BIDS Faculty, Fellows & Staff (BIDS Spring 2024 Newsletter)

Feb. 29, 2024

Abhay Joshi, PhD, accomplished data science leader, has been named Emory University’s associate vice provost for university data, effective March 1, 2024. Joshi brings a wealth of experience in data science, advanced analytics and strategic leadership for both higher education and industry.

In this role, Joshi will serve as the senior university executive responsible for supporting the effective use of student, faculty, academic and administrative data across Emory University. This includes overseeing the implementation of an enterprise data strategy that will maximize the value of Emory’s data assets to achieve strategic goals.

“Emory University data is a key resource, and it is essential that we approach this resource from a united governance model,” says Lanny Liebeskind, senior vice provost for academic affairs. “Dr. Joshi will be instrumental in setting and implementing policies that elevate our operations and identifying innovative and responsible data usage methods for the benefit of the enterprise as a whole."

Joshi will report to Joe Depa, chief data and AI officer for Emory University and Emory Healthcare, and John Ellis, interim enterprise chief information officer and senior vice provost for information technology. Joshi will partner with academic leaders across the university and will work closely with schools and Office of the Provost leadership to support the academic mission. Joshi will also partner with university data stewards, analysts and IT professionals responsible for data warehousing and reporting to build a culture of trust and shared responsibility for university data.

“Dr. Joshi’s experience in data management and governance, products and solutions development and building and scaling high-performing teams will be instrumental in improving the faculty and student experience and advancing Emory’s mission,” says Depa. “His leadership skills and business acumen will be tremendous assets to our overarching data strategy and organizational goals.”

Prior to joining Emory, Joshi served as the director of analytics in the Office of Analytics and Institutional Effectiveness at Virginia Tech. In this role, he provided expert guidance and strategic direction for the institution’s data and analytics processes and data strategies. He was a driving force behind the operationalization of a well-governed, centralized analytics platform, an initiative that transformed the institution's decision-making processes.

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