PhD students will be reviewed annually. Students are also required to pass a Comprehensive Exam, the Depth Qualifying Exam (DQE), a teaching requirement (at least two courses at the Center for Data Science by the end of the fourth year of study), the Dissertation Proposal presentation, Defense, and the Dissertation. All Graduate School of Arts & Science doctoral candidates must also be approved for graduation by their department for the degree to be awarded.
1st Semester/Term | Credits | |
---|---|---|
Introduction to Data Science | 3 | |
Probability and Statistics for Data Science | 3 | |
Research Rotation | 1-3 | |
Credits | 9 | |
2nd Semester/Term | ||
Machine Learning | 3 | |
Big Data | 3 | |
Research Rotation | 1-3 | |
Credits | 9 | |
3rd Semester/Term | ||
Inference and Representation | 3 | |
Research Rotation | 1-3 | |
General Elective | 3 | |
Credits | 9 | |
4th Semester/Term | ||
Research Rotation | 1-3 | |
General Elective | 3 | |
General Elective | 3 | |
Credits | 9 | |
5th Semester/Term | ||
Research Rotation | 1-3 | |
General Elective | 3 | |
General Elective | 3 | |
Credits | 9 | |
6th Semester/Term | ||
Research Rotation | 1-3 | |
General Elective | 3 | |
General Elective | 3 | |
Credits | 9 | |
7th Semester/Term | ||
General Elective | 3 | |
General Elective | 3 | |
General Elective | 3 | |
Credits | 9 | |
8th Semester/Term | ||
General Elective | 3 | |
General Elective | 3 | |
General Elective | 3 | |
Credits | 9 | |
Total Credits | 72 |
Following completion of the required coursework for the PhD, students are expected to maintain active status at New York University by enrolling in a research/writing course or a Maintain Matriculation ( MAINT-GA 4747 ) course. All non-course requirements must be fulfilled prior to degree conferral, although the specific timing of completion may vary from student-to-student.
Upon successful completion of the program, graduates will have:
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 .
Send Page to Printer
Print this page.
Download Page (PDF)
The PDF will include all information unique to this page.
If you have always been fascinated by science, especially if you are interested in statistics and the scientific method, then a PhD in Data Science might be for you.
Data science is a field of study dedicated to applying the science of statistics to the problem areas of data visualisation, data science and machine learning. In this field, the challenge is to use data analysis and mathematical formulas to predict data patterns and draw conclusions from them.
Data science has become popular because it covers a wide range of topics, including the use of statistical methods for analysing and interpreting data. The primary goal of the discipline is to explain the way data enters the scientific community and influences decisions. Data is analysed to find patterns and connections, and then possible solutions are explored. With big data and new statistical computing methods, patterns can be uncovered, and relationships can be tested.
As more and more industries rely on information generated by computers, data science will be one of the key players in the future.
Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, what does a phd in data science focus on.
The primary focus for a PhD in Data Science is statistical methods. This means that you would study statistics in all its forms at the macroscopic and microscopic level, including statistical computer science, theory and applied mathematics. The advantage is that you get an insight into how large-scale data works. Thus, a position in a company where you are analysing large amounts of project data can be made available through a PhD.
PhD programs in data science provide university students with a thorough grounding in the theoretical aspects , but they are also taught the practical aspects of the discipline. PhD students are taught how to conduct proper experiments and interpret the results of scientific studies.
The importance of data and its interpretation is of paramount importance in all fields, and a PhD programme in data science addresses this topic, with some institutions also offering taught modules that doctoral students can use to deepen their knowledge.
Within a data science field, there are several areas of focus. One of them is the analysis of large databases and their effective interpretation. With this doctoral qualification, you could conduct statistical analysis, research studies and even exploratory data analysis. You could see what kinds of relationships exist between variables. You can explore areas such as Databases, Human Resource Management Machine Learning, or Information Technology during your studies.
A PhD in Data Science involves conducting original research in this area; therefore, applicants must have a good knowledge of statistical methods, computing, probability calculation, statistics and other related topics.
Basic requirements are typically a strong Master’s degree in mathematics, computer science or statistics from an accredited university. International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.
Although there are many advantages to obtaining a PhD in Data Science, it requires hard work and perseverance to master the techniques of analysis; to become an effective researcher, you will need strong mathematical and logical skills.
If you are interested in a PhD in Data Science but are unsure whether you have the background or resources available, consider taking a Master’s degree in this subject, or if you are a prospective student, contact the department you are interested in to see if they have any advice for you.
You can earn a PhD in data science in as little as 3 years full-time or 6 years part-time at a leading university. There are also online courses; many universities offer online PhD programmes which allow you to complete your entire doctoral programme from home. You still need to meet your course requirements by attending lectures and doing laboratory work, but your work can be completed at your own pace and off-campus.
The cost of a PhD in Data Science will depend on the university you study with, but average tuition fee is £4000-£6000 per academic year for UK/EU students and £16,000-£19,000 per academic year for international students.
Due to the popularity of Data Science PhD projects and the increasing demand for individuals who can elaborately analyse large data sets , it is not difficult to obtain PhD funding in this area. In many cases, funding for full-time research can be obtained from the university’s Centre for Doctoral Training (CDT), covering tuition fees and living costs.
A PhD in Data Science will enhance your data analysis skills and allow you to specialise in areas not available to others. A PhD offers many opportunities for those interested in statistics; you could become an engineer, statistician, consultant or academic lecturer. There are even PhDs in Data Science that offer internships in financial institutions or government agencies. Upon completing your doctorate, you can enter the workforce in many areas depending on your aptitude and experience.
A PhD in Data Science can lead to a wide range of jobs in many fields. If you are interested in working for a company that uses data one way or another, a PhD would be the perfect choice for you. If you are interested in independent research and studying various scientific methods and data, you will do well with a PhD. You could also spend your time teaching or doing your own research.
A person who has a PhD in data science can work in many industry-related positions. For example, you may work in the financial industry as an analyst for mergers and acquisitions, in healthcare, as a statistician, or as an information systems administrator. You can even get a job as an IT analyst, project manager, and software designer.
You can use your knowledge in the workplace to start up your own small business. Many small businesses today are founded on the back of a PhD. In fact, many Fortune 500 companies started as a result of a doctor trying to solve a problem or answer a long-standing question plaguing their industry.
Join thousands of students.
Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.
Phd program, phd program overview.
The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers as well as research statisticians and data scientists in industry, government and the non-profit sector.
Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).
From the Graduate School’s webpage outlining the general requirements for a PhD :
In order to receive a doctoral degree, students must:
PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.
The Department requires that students in the Statistics and Data Science PhD program:
Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.
Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.
Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be approved non-STAT courses.
*Prior to 2021-2022, the course requirements for the PhD were:
The Ph.D. in Data Science Studies at William & Mary is offered as a specialization within Applied Science, with the core mission of training students in the use of exceptionally large, heterogeneous datasets to drive decisionmaking across a wide range of fields (from Physics to the social sciences). Graduate students complete a core sequence of coursework as a cohort, and then work closely with an advisory committee to complete the degree program. Competitive stipends and tuition are provided to selected students (stipends for AY24-25 are about $31,000).
To receive a Doctor of Philosophy in Applied Science with a Specialization in Data Science, the candidate must:
Be prepared for a rigorous program that emphasizes the analysis of large datasets, frequently in applied domains using machine learning techniques. You will take courses in both the underlying mathematical foundations and computational techniques used to define, implement, and validate models across a range of disciplines.
Generally, we expect students applying to this program will have a background in computer programming, probability and statistics. Most successful candidates will also have some experience working with large datasets in applied contexts. Python is the most commonly used language, though some courses and laboratories use alternatives such as R, Scala, or compiled languages.
Most students will start their program during the Fall semester (while spring admissions are possible, they will only occur under exceptional circumstances). In many cases, a Ph.D. student might expect a schedule similar to:
See the Graduate Catalog for details.
Data Science Ph.D. students are admitted to the Graduate program in Applied Science , in which they will earn a specialization of Data Science. Applications can be submitted online at the link below, or by clicking here . Note that we do not today require GRE scores, but they can be optionally submitted for consideration.
Applications are accepted on a rolling basis, but we aim to make our first round of decisions during the spring semester each year. To be as competitive as you can be, we recommend your application be submitted by February 15th.
The application process includes:
If you have any questions about the admissions process, you can contact our office at [email protected].
Follow W&M on Social Media:
Williamsburg, Virginia
The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..
Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.
Applied Mathematics Doctoral Program
Computer Science Doctoral Program
Decision, Risk, and Operations (DRO) Program
Electrical Engineering Doctoral Program
Industrial Engineering and Operations Research Doctoral Program
Statistics Doctoral Program
The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.
Ph.d. specialization committee.
Rocco a. servedio, clifford stein.
Developing future pioneers in data science
The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics.
Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.
Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:
Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.
Get the latest news.
Subscribe to receive updates from the School of Data Science.
Boston University
For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .
The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solutions of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.
Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented backgrounds in computing and data science disciplines.
Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.
For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.
Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).
The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.
Sixteen term courses (64 units) are required for post-BA/BS students and 12 term courses (48 units) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 units) as long as these units were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.
Of the 16 courses, up to 3 undergraduate courses (12 units) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.
The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:
A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.
The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.
During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-term training course (4 units) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-term doctoral seminar (4 units) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 units) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.
A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred units. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.
There is no foreign language requirement for the PhD degree in CDS.
No later than the end of the sixth term (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one term prior to the exam.
Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth term (fourth year) of study.
Candidates must undergo a final oral examination no later than the end of the 10th term (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.
Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.
Note that this information may change at any time. Read the full terms of use .
Boston University is accredited by the New England Commission of Higher Education (NECHE).
Degrees & Programs
We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection ofcomputer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.
Herman Ray , Director, Ph.D. in Data Science and Analytics
This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.
Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.
Information Sessions for Fall 2025 Admission
To be announced
Data Science and Analytics PhD Curriculum
Stage Two: Coursework
The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study:
Students take up to 9 credit hours of 6000- or 7000-level courses in DS, STAT, or CS with permission of the program director. Students take any 8000- or 9000-level course in DS, STAT, MATH, CS or IT, or the HHS courses in the mHealth concentration.
Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.
At the end of the program, students will be able to:
Frequently Asked Questions (FAQ)
How long will the program take?
How much does the program cost?
Who would be successful in the program?
Where do these graduates work after graduation?
What are the publication/research requirements?
What did Science Doctoral Students Study?
What is the Project Engagement requirement?
Can I pursue the program part- time while I am working full-time?
Can I live on campus?
Are the courses online?
Do I have to have a masters degree to apply?
Where did Data Doctoral Students Study?
Ph.D. in Data Science and Analytics Student Cohorts
2024 - 2025
Sharanya Dv
Bachelor's Degree: Physics, Computer Science and Mathematics, St. Aloysius College, Mangalore, India
Master's Degree: Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India
Work History: Data Modernization Team Intern, Google Cloud Partner, Niveus Solutions Pvt. Ltd.
Professional Objective: My goal is to contribute effectively to data-driven decision-making processes and to continuously develop my expertise in the ever-evolving field of data science.
Charles Fanning
Bachelor's Degree: Mathematics, Lewis University
Master's Degree: Data Science from Lewis University
Professional Objective: to make meaningful contributions to a wide breadth of interdisciplinary fields through the medium of data science, both in academia and industry. Right now, I am interested in the applications of deep learning on medical imaging data across various modalities and in topological data analysis.
Mohsin Md Abdul Karim
Bachelor's Degree: Mathematics, Jahangirnagar University, Dhaka, Bangladesh
Master's Degrees: Mathematics, Jahangirnagar University, Dhaka, Bangladesh; Mathematics, Eastern Illinois University; Mathematics, University of Louisiana at Lafayette
Work History: Export Officer, Jamuna Bank Ltd., Dhaka, Bangladesh; Business Intelligence Team, Nagad Ltd., Dhaka, Bangladesh
Professional Objective: Create a distinct value for myself in an organization so that I can be treated as an asset to them.
Faruk Muritala
Bachelor's Degree: Mathematics, Federal University of Technology, Minna, Niger State Nigeria
Master's Degrees: Mathematics, Kwara State University, Malete, Nigeria; Data Science and Analytics, Kennesaw State University
Work History:
Courses Taught: Introduction to Data Science
Publications:
Award: J. Stephen and Jennifer Lewis Priestley Doctoral Endowed Scholarship, Kennesaw State University. August 2024.
Professional Objective: to utilize my mathematics, data science, and educational skills to become a leading data scientist and researcher and make a positive impact. I am open to exploring opportunities in academia and industry and eager to learn, relearn, and grow in a dynamic research environment.
Joseph Richardson
Bachelor's Degree: Actuarial Science (Statistics), University of West GA
Master's Degree: Analytics, Georgia Tech
Professional Objective: I aim to teach and conduct research at an academic institution while also consulting privately.
David Stabler
Bachelor's Degree: Computer Science, Southern Polytechnic State University
Master's Degree: Computer Science, Southern Polytechnic State University/Kennesaw State University
Work History: Four decades of IT, currently in the Research Division of the Federal Reserve Bank of Atlanta
Professional Objective: prepare to be a more effective professor
Benjamin Watson
Bachelor's Degree: Mathematics, Morehouse College
Master's Degrees: Mathematics Education, Georgia State University; Data Science and Analytics, Kennesaw State University (in progress)
Professional Objective: I am seeking to apply combinatorial data analysis to drive innovation in healthcare through patient subtyping, drug discovery, and generative AI. I am also committed to advancing data science research and contributing to academic instruction.
Qiuyuan Zhang
Bachelor's Degree: Electronic Information Engineering, Xidian University, China
Master's Degree: Data Science, Georgia State University
Work History: Graduate Research Assistant, Georgia State University
Professional Objective: to evolve into a research-oriented professional contributing significantly to academia or industry
Venkata Abhiram Chitty
Bachelor's Degree: Mathematics, Statistics and Computer Science, Osmania University, Telangana, India
Master's Degree: Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India
Professional Objective: To apply my Data Science skills in public health domain and help the society
Caleb Greski
Bachelor's Degree:
Master's Degree:
Courses Taught:
Publications:
Professional Objective:
Bachelor's Degree: Civil Engineering, Huazhong University of Science and Technology, China
Master's Degree: Business Analytics, Syracuse University
Work History:
Courses Taught: Calculus I, Marketing Analytics, Data Mining
Awards: Merit-Based Scholarship, Syracuse University
Professional Objective: To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.
Kausar Perveen
Bachelor's Degree: Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan
Master's Degree: Masters in Data Science, Illinois Institute of Technology, Chicago
Publications: National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan
Service and Awards:
Professional Objective: My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health
Bachelor's Degree: Statistics, University of Dhaka, Dhaka, Bangladesh
Master's Degree: Mathematics (Statistics Concentration), University of Toledo, Ohio
Courses Taught: Introduction to Statistics
Professional Objective: I am interested to work as a data scientist in the industry
Ayomide Isaac Afolabi
Bachelor's Degree: Chemical Engineering, Ladoke Akintola University of Technology
Master's Degree: Data Science, Auburn University
Work History: Graduate Research Assistant, Auburn University
Courses Taught: Python Programming
Publications: Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402. doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190
Professional Objective: To work as a research data scientist in the industry
Dinesh Chowdary Attota
Bachelor's Degree: Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India
Master's Degree: Computer Science, Kennesaw State University
Work History: Associate Consultant, SL Techknow Solutions India Pvt Ltd, India 2018 - 2020
Professional Objective: I'd like to be a faculty member at a university so that I can continue to do research.
Nzubechukwu Ohalete
Bachelor's Degree: Mathematics,University of Nigeria, Nsukka
Master's Degree: Applied Statistics, Bowling Green State University
Work History: Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department
Courses Taught: Elementary Mathematics, Mathematical Methods
Awards: James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022
Professional Objective: To use data science techniques to solve problems which makes our lives better and also makes our world a better place
Ryan Parker
Bachelor's Degree: Microbiology, University of Tennessee - Knoxville
Master's Degree: Integrative Biology, Kennesaw State University
Work History: Instructor of Biology, Kennesaw State University
Courses Taught: Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs
Awards: Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"
Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.
Askhat Yktybaev
Bachelor's Degree: Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia
Master's Degree: Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University
Work History:
Courses Taught: Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism
Service and Awards: Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast
Professional Objective: I want to found a successful Fintech startup one day.
Sanad Biswas
Bachelor's Degree: Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh
Master's Degree: Statistics, University of Toledo, OH
Courses Taught: Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.
Professional Objective: To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.
Mallika Boyapati
Bachelor's Degree: Electronics and Computer Engineering, K L University, India
Master's Degree: Applied Computer Science, Columbus State University
Courses Taught: DATA 4310 - Statistical Data Mining
Publications:
Professional Objective: To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science
Nina Grundlingh
Bachelor's Degree: Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa
Master's Degree: Statistics, University of KwaZulu-Natal, South Africa
Courses Taught: Introduction to Statistics, University of KwaZulu-Natal
Professional Objective: To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems.
Namazbai Ishmakhametov
Bachelor's Degree: Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University
Master's Degree: Analytics, Institute for Advanced Analytics at North Carolina State University
Courses Taught: Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan
Professional Objective: To apply my quantitative skills in the field of biotech either in corporate or government sector
Symon Kimitei
Bachelor's Degrees: Mathematics, Kennesaw State University, and Computer Science, Kennesaw State University
Master's Degree: Mathematics (Scientific Computing Concentration), Georgia State University
Work History: Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University
Courses Taught: Calculus 1, Precalculus, Applied Calculus & College Algebra
Poster Presentations:
Professional Objective: As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.
Jitendra Sai Kota
Bachelor's Degree: Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India
Master's Degree: Computer Science, Florida State University
Work History: Teaching Assistant Professor in Computer Science at an Engineering College in India
Courses Taught: Problem Solving & Program Design through C, Artificial Intelligence, Data Mining
Publications: Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237. DOI: 10.5573/IEIESPC.2020.9.3.230
Professional Objective: to work in Data Science in a Corporate Environment
ResearchGate
Catrice Taylor
Bachelor's Degree: Economics, Clemson University
Master's Degrees: Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University
Professional Objective: To work as an industry data scientist in a corporate environment
Sahar Yarmohammadtoosky
Bachelor's Degree: Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran
Master's Degree: Applied Mathematics, Iran University of Science & Technology, Tehran, Iran
Courses Taught: Numerical Analysis and Linear Algebra, Iran University of Science & Technology
Publications: Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan
Professional Objective: My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university
Martin Brown
Graduation Date: Spring 2024
Dissertation: A Holistic and Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives
Dissertation Advisors: Dr. Dominic Thomas and Dr. Md Abdullah Al Hafiz Khan
Inchan Hwang
Graduation Date: Summer 2024
Dissertation: Next-Generation Medical Imaging Dataset Management Leveraging Deep Learning Frameworks in Breast Cancer Screening
Dissertation Advisor: Dr. MinJae Woo
Current Position: Assistant Professor of Cybersecurity, Montreat College
Duleep Prasanna Rathgamage Don
Bachelor's degree: Physics and Mathematics, The Open University of Sri Lanka
Master's degree: Mathematics, Georgia Southern University
Courses Taught: Trigonometry, and Calculus I & II
Publications/Presentations:
Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and medicine
Linglin Zhang
Graduation Date: Summer 2024
Dissertation: Innovative Approaches for Identifying and Reducing Disparity in Machine Learning Model Performance – Bridging the Gap in Binary Classification for Health Informatics
Current Position: Data and Analytics RDP Associate, Equifax
Yihong Zhang
Bachelor’s Degree: Psychology Mathematics Interdisciplinary, Chatham University
Master’s Degree: Mathematics and Statistics Allied with Computer Science, Georgia State University
Professional Objective: Make better use of data in healthcare and bioinformatic industry as a data scientist.
2019 - 2020
Trent Geisler
Graduation Date: Summer 2022
Dissertation: Novel Instance-Level Weighted Loss Function for Imbalanced Learning
Dissertation Advisor: Dr. Herman Ray
Current Position: Assistant Professor, Department of Systems Engineering, United States Military Academy West Point
Srivatsa Mallapragada
Dissertation: Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap
Dissertation Advisor: Dr. Ying Xie
Current Position: Data Scientist, Rue Gilt Groupe (RGG)
Sudhashree Sayenju
Graduation Date: Spring 2023
Dissertation: Quantification and Mitigation of Various Types of Biases in Deep NLP Models
Dissertation Advisor: Dr. Ramazan Aygun
Current Position: Lecturer, Data Science and Analytics, Kennesaw State University
Christina Stradwick
Bachelor’s Degree: Music Performance and Mathematics, Marshall University
Master’s Degree: Mathematics with Emphasis in Statistics, Marshall University
Courses Taught: Prep for College Algebra at Marshall University
Selected Presentations:
Professional Objectives: To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.
2018 - 2019
Md Shafiul Alam
Graduation Date: Fall 2022
Dissertation: Appley: App roximate Shap ley Values for Model Explainability in Linear Time
Dissertation Advisor: Dr. Ying Xie
Current Position: AI Framework Engineer, Intel Corporation
Jonathan Boardman
Dissertation: Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics
Current Position: Data Scientist, Equifax
Tejaswini Mallavarapu
Bachelor’s Degree: Pharmacy, Acharya Nagarjuna University, India
Master’s Degree: Computer Science, Kennesaw State University
Selected Publications:
Professional Objective: To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.
Seema Sangari
Dissertation: Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting
Dissertation Advisor: Dr. Michael Whitman
Current Position: Principal Modeler, HSB
Srivarna Settisara Janney
Bachelor’s Degree: Mechanical Engineering, Visveswaraiah Technological University, India
Selected Publications/Presentations:
Professional Objective: I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.
2017 - 2018
Andrew M. Henshaw
Bachelor’s Degree: Electrical Engineering, Georgia Tech
Master’s Degree: Electrical Engineering, Georgia Tech
Master’s Degree: Business Administration, Georgia State University
Courses Taught: Software-Defined Radio Development with GNU Radio: Theory and Application, Georgia Tech Professional Education
Selected Publications/Presentations: Python Cookbook, Vol 1, 2002, “Sorting Objects Using SQL’s ORDER BY Syntax”
Triangulation Clustering
Lyrical: Complexity Analysis of Pop Song Lyrics
Service and Awards: International Test and Evaluation Association (ITEA) Atlanta Chapter, President, 1995
Graduation Date: Summer 2021
Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design
Dissertation Advisor: Dr. Meng Han
Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business
Mohammad Masum
Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset
Dissertation Advisor: Dr. Hossain Shahriar
Current Position: Assistant Professor, San Jose State University
Lauren Staples
Graduation Date: Fall 2021
Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System
Dissertation Advisor: Dr. Joseph DeMaio
Current Position: Senior Data Scientist, Microsoft
2016 - 2017
Shashank Hebbar
Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction
Current Position: Data Scientist, Credigy
Jessica Rudd
Graduation Date: Summer 2020
Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies
Dissertation Advisor: Dr. Herman Ray
Current Position: Senior Data Engineer, Intuit Mailchimp
Graduation Date: Spring 2020
Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring
Dissertation Advisor: Dr. Sherry NI
Current Position: Applied Scientist II, Amazon
Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem
Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise
Dissertation: Attack and Defense in Security Analytics
Dissertation Advisor: Dr. Selena He
Current Position: NLP Data Scientist, NBME
2015 - 2016
Edwin Baidoo
Graduation Date: Spring 2020
Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data
Dissertation Advisor: Dr. Stefano Mazzotta
Current Position: Assistant Professor, Business Analytics, Tennessee Technological University
Bogdan Gadidov
Graduation Date: Summer 2019
Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles
Dissertation Advisor: Dr. Mohammed Chowdhury
Current Position: Data Scientist, Variant
Dissertation: Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
Dissertation Advisor: Dr. Mingon Kang
Current Position: Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College
Graduation Date: Spring 2019
Dissertation: Deep Embedding Kernel
Current Position: Assistant Professor, Information Technology, Kennesaw State University
Bob Venderheyden
Graduation Date: Fall 2019
Dissertation: Ordinal Hyperplane Loss
Dissertation Advisor: Dr. Ying Xie
Current Position: Principal Data Scientist, Microsoft
Contact Info
Kennesaw Campus 1000 Chastain Road Kennesaw, GA 30144
Marietta Campus 1100 South Marietta Pkwy Marietta, GA 30060
Campus Maps
Phone 470-KSU-INFO (470-578-4636)
kennesaw.edu/info
Media Resources
Resources For
Related Links
470-KSU-INFO (470-578-4636)
© 2024 Kennesaw State University. All Rights Reserved.
Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.
There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.
There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .
Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.
The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.
The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.
Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.
The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage .
The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information.
In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.
Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.
On this page: Admissions • Applications • Standardized Testing • Financial • Miscellaneous
Below you will find a list of frequently asked questions about CDS’ PhD in Data Science:
That depends on how your multiple applications are split up over the NYU schools. You can apply concurrently to as many NYU schools as you wish. Some schools allow you to apply to multiple programs. However, the PhD in Data Science is part of the Graduate School of Arts and Science, which permits only one application at a time. Please note that you can find the full Graduate School of Arts and Science policy on the NYU GSAS General Application Policies page .
No, both programs are housed within the Graduate School of Arts and Sciences, which permits only one application at a time.
No, you will need to apply to the PhD program.
Yes, we do admit non-degree students. For more information on the application process, please see the GSAS Instructions For the Non-Degree and Visiting Student Application for Admission page . There is a limit on the number of such courses you can take as a non-degree student.
Students enrolled in any other NYU graduate program must submit a new application. NYU students enrolled in a Courant graduate degree must also submit a new application. Students in a Courant graduate program cannot apply for a transfer.
See our PhD Admission Requirements page for the prerequisite information.
NYU seeks talented students from every corner of the globe.
For more information for international students interested in the PhD in Data Science program, please visit the NYU Graduate School of Arts and Science website .
Official test scores should be received by the application deadline.
Yes, you may apply again by submitting a new application and supplemental material. See related GSAS Application Policies page .
Unfortunately, due to a high volume of applications, reviewers cannot provide feedback on why an application was rejected
No, unfortunately, there are no dual data science degrees at this time. However, the data science curriculum allows students to take electives within various departments outside of Data Science.
Copies of your official transcripts should be uploaded with your application. If you are accepted, the Graduate School of Arts & Sciences will request a mailed copy of your transcripts. Please note that only electronic submissions will be accepted in the application. See GSAS’ page on academic transcripts for more information.
Please contact [email protected] .
Please review the Graduate School of Arts and Science’s FAQ section for the policies regarding letters of recommendation.
Many factors are taken into account by the admissions committee. There is no minimum cutoff for standardized tests. However, for the IELTS, there is a recommended minimum band score of 7.0. For the TOEFL, there is a recommended minimum score of 100 on the internet-based test.
The GRE is not required for Fall 2023 applicants. We will consider GRE test scores if they are submitted.
The Graduate School requires applicants who are not native English speakers to submit official TOEFL or IELTS score results. The TOEFL/IELTS requirement is waived if your baccalaureate or master’s degree was (or will be) completed at an institution where the language of instruction is English. See the GSAS Test Score page for more information . Please contact [email protected] if you have further questions.
The school code for the TOEFL is 2596 (New York U Grad Arts Sci).
Current and past CDS PhD students admitted to our program have been offered fellowships covering tuition, fees, and health insurance. The fellowship includes a nine-month stipend, which for the 2023-2024 school year is $39,430.38. These fellowship packages have been five-year commitments. In addition to the fellowship, CDS has provided a one-time award for start-up expenses. PhD financial packages are reviewed on a yearly basis for new cohorts.
As per GSAS policy, admitted students may transfer up to 36 credits into the PhD program pending review and approval by the Director of Graduate Studies. Current students who are considering transfer credits should email Kathryn Angeles at [email protected] .
It is not possible to complete the PhD in Data Science program as a part-time student.
Programming languages are decided by the professor of each course. However, over the past few years, Python has been widely used.
The suggested full-time workload is three 3-credit courses per semester totaling 9 credits per semester. We do not recommend more than 3 courses per semester, as most courses have both significant mathematical content and programming assignments.
The NYU Center for Data Science helps to provide robust internship opportunities with business partners in the New York area, including some of the world’s largest companies working in data science, artificial intelligence and machine learning. Many of these companies are within walking distance of the campus.
We do not use the term “Teaching Assistant” but we do sometimes have grading and section leader positions available for qualified students.
No, we do not offer courses online.
Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.
Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.
Earn your MBA and SM in engineering with this transformative two-year program.
A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.
A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.
Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.
A doctoral program that produces outstanding scholars who are leading in their fields of research.
Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.
Apply now and work for two to five years. We'll save you a seat in our MBA class when you're ready to come back to campus for your degree.
The 20-month program teaches the science of management to mid-career leaders who want to move from success to significance.
A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.
A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.
Non-degree programs for senior executives and high-potential managers.
A non-degree, customizable program for mid-career professionals.
Program overview.
Now Reading 1 of 4
Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.
Learn more about the program, how to apply, and find answers to common questions.
Check out our event schedule, and learn when you can chat with us in person or online.
Visit this section to find important admissions deadlines, along with a link to our application.
Click here for answers to many of the most frequently asked questions.
PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous: MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.
PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.
Behavioral & policy sciences.
Economic Sociology
Institute for Work & Employment Research
Organization Studies
Technological Innovation, Entrepreneurship & Strategic Management
Accounting
Information Technology
System Dynamics
Those interested in a PhD in Operations Research should visit the Operations Research Center .
Additional information including coursework and thesis requirements.
MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.
The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.
Now Reading 2 of 4
The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.
MIT Sloan PhD Program Admissions Requirements Common Questions
The 2025 application for admission is live and can be accessed here . The deadline for submission of all materials is December 1, 2024.
More information on program requirements and application components
Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.
Funding Information
Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.
Docnet recruiting forum at university of minnesota.
We will be joining the DocNet consortium for an overview of business academia and a recruitment fair at University of Minnesota, Carlson School of Management.
During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.
MIT Sloan PhD Program will be joining the DocNet consortium for an overview of business academia and a recruitment fair at Utah, David Eccles School of Business.
Complete PhD Admissions Event Calendar
Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.
Now Reading 3 of 4
Profiles of our current students
MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.
Doctoral candidates on the current academic market
Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.
view recent placements
Now Reading 4 of 4
The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.
Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.
Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.
From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.
This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.
“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship
Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.
We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.
The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.
2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker
Watch more MIT Sloan PhD Program Doctoral Forum Videos
Ask a question or register your interest
Meet our faculty.
This program is designed for students who desire academic research careers. The foundation is a sequence of courses in probability, mathematical statistics, linear models and statistical computing. The program also encourages study in a cognate area of application.
Up to four courses per semester may be counted toward the overall requirement of 13 courses. The six core courses are usually taken in the first year.
STAT 9300, STAT 9610, STAT 9700
Two Electives | |
Independent Study Course, Two Electives, Oral Exam/Thesis Proposal Electives or Directed Study Units | |
Independent Study and Dissertation Research |
Electives must include suitable courses numbered 9000 and above, when offered.
STAT 9270 | Bayesian Statistics |
STAT 9300 | Probability |
STAT 9310 | Stochastic Processes |
STAT 9610 | Statistical Methodology |
STAT 9700 | Mathematical Statistics |
STAT 9710 | Introduction to Linear Statistical Models |
More advanced students choose from among various elective courses offered by the faculty of the Statistics and Data Science Department and other departments at the University. There is also considerable opportunity to take individually-structured reading courses with faculty in the department.
In addition to formal coursework, the student is expected to participate in the informal intellectual life of the Department of Statistics and Data Science. This includes attendance at departmental colloquia, where visiting speakers describe current research, plus participation in informal seminars investigating current topics of interest in a non-course setting.
The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686
Phone: (215) 898-8222
Program summary.
Students are required to
The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.
All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in the Department of Statistics PhD Student Handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).
Statistics Department PhD Handbook
All students are expected to abide by the Honor Code and the Fundamental Standard .
During the first two years of the program, students' academic progress is monitored by the department's Director of Graduate Studies (DGS). Each student should meet at least once a quarter with the DGS to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.
Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for PhD Candidacy, a university milestone, by early spring quarter of their second year.
While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.
The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.
The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member.
The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.
For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.
The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.
The Dissertation Reading Committee must also read and approve the thesis.
For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .
The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's Dissertation Reading Committee. Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members.
Biosketch format pages, instructions, and samples.
A biographical sketch (also referred to as biosketch) documents an individual's qualifications and experience for a specific role in a project. NIH requires submission of a biosketch for each proposed senior/key personnel and other significant contributor on a grant application. Some funding opportunities or programs may also request biosketches for additional personnel (e.g., Participating Faculty Biosketch attachment for institutional training awards). Applicants and recipients are required to submit biosketches
NIH staff and peer reviewers utilize the biosketch to ensure that individuals included on the applications are equipped with the skills, knowledge, and resources necessary to carry out the proposed research. NIH biosketches must conform to a specific format. Applicants and recipients can use the provided format pages to prepare their biosketch attachments or can use SciENcv , a tool used to develop and automatically format biosketches according to NIH requirements.
Biosketch (non-fellowship): biographical sketch format page - forms-h.
IMAGES
VIDEO
COMMENTS
The field of data science has emerged from advances in computational speed, data availability, and novel analysis methods. It demands a new type of researcher: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers.
Based in San Diego, California, National University (NU) offers a variety of online programs, including a Ph.D. in data science. NU's program requires 60 credits and takes an estimated 40 months ...
PhD in Analytics and Data Science. Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.
Degree requirements for the PhD in Data Science can be found in the NYU bulletin - Doctor of Philosophy in Data Science. T o be awarded the PhD in Data Science, students must, within 10 years of first enrolling: Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester. Complete the teaching ...
The PhD program in data science, analytics and engineering engages students in fundamental and applied research. ... 3 year programs These programs allow students to fast-track their studies after admission and earn a bachelor's degree in three years or fewer while participating in the same high-quality educational experience of a 4-year option ...
The PhD in Data Science is designed to be completed fully in-person at UChicago's Hyde Park campus. There are no online options at this time. Newly admitted students are guaranteed full-funding for up to 5 years and provided with an annual stipend, contingent on satisfactory progress towards the degree. First-Year Requirements The standard first-year […]
Additional Program Requirements. PhD students will be reviewed annually. Students are also required to pass a Comprehensive Exam, the Depth Qualifying Exam (DQE), a teaching requirement (at least two courses at the Center for Data Science by the end of the fourth year of study), the Dissertation Proposal presentation, Defense, and the Dissertation.
PhD in Data Science: Admissions Requirements. The application deadline for Fall 2024 Admissions was Tuesday, December 5, 2023, 5pm ET. Applications for Fall 2025 Admissions will open in late September 2024. Our Fall 2024 PhD Admissions Information Session took place on Thursday, October 26 at 1pm.
The cost of a PhD in Data Science will depend on the university you study with, but average tuition fee is £4000-£6000 per academic year for UK/EU students and £16,000-£19,000 per academic year for international students. Due to the popularity of Data Science PhD projects and the increasing demand for individuals who can elaborately analyse ...
The Department requires that students in the Statistics and Data Science PhD program: Meet the department minimum residency requirement of 2 years. Complete the following courses: STAT 344-0 Statistical Computing. STAT 350-0 Regression Analysis. STAT 353-0 Advanced Regression. STAT 415-0 I ntroduction to Machine Learning.
Year 3+: Dissertation, full time research in a research lab, annual evaluation of progress to dissertation by the graduate committee. See the Graduate Catalog for details. How to Apply. Data Science Ph.D. students are admitted to the Graduate program in Applied Science, in which they will earn a
Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies. The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and ...
A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will: Understand data as a generic concept, and how data encodes and captures information. Be fluent in modern data engineering techniques, and work with complex and large data sets.
The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solutions of problems and synthesis of knowledge related to the methodical, generalizable, and ...
Learn more about the doctoral degree in Data Science and Analytics at Kennesaw State University! ... We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection ofcomputer science, statistics, mathematics, and business. ... Year 3. DS 9700 Doctoral Internship/Research Lab; DS 9900 Dissertation; Teaching;
Statistics PhD Travel Support. The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences.
However, the PhD in Data Science is part of the Graduate School of Arts and Science, which permits only one application at a time. ... fees, and health insurance. The fellowship includes a nine-month stipend, which for the 2023-2024 school year is $39,430.38. These fellowship packages have been five-year commitments. In addition to the ...
Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world. ... A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems. ... PhD studies at MIT Sloan are intense and ...
Programs » PhD Program. ... Department of Statistics and Data Science. The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686. Phone: (215) 898-8222. PhD Program. Contact Information; Curriculum;
You don't *need* a PhD to do data science. Most of the skills are either included in the a master of statistics, or you cannot learn in academia (like the business understanding and so on). ... PhD takes the maximum of 4 years (officially the grant is given for that time), and it is possible to complete in 2-3 years, if the stars align, and the ...
Doctoral Curriculum. This program is designed for students who desire academic research careers. The foundation is a sequence of courses in probability, mathematical statistics, linear models and statistical computing. The program also encourages study in a cognate area of application. Up to four courses per semester may be counted toward the ...
For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in the Department of ...
Having completed two internships just a couple of years ago, Sara Nóbrega has fresh, ... Leading a data science project involves keeping track of numerous moving parts, but as Hans Christian Ekne explains, there are a number of techniques a manager can leverage to keep things chugging along smoothly: ...
In a research project, this process would be the work of several years and multiple academic publications. In a technical project, this should be the work of a few weeks. It requires a scientific mindset and an agile aptitude for creativity and experimentation. Methodology and Data Science
COMP SCI 220 Data Science Programming I; COMP SCI 300 Programming II; COMP SCI 320 Data Science Programming II; Have a minimum undergraduate GPA of 3.0 on the last 60 credits of degree; Submit evidence of English language proficiency, if applicable. The required proficiency scores are: TOEFL IBT 92, PBT 580; or IELTS 7.0
A biographical sketch (also referred to as biosketch) documents an individual's qualifications and experience for a specific role in a project.