phd data science usa

Doctor of Philosophy in Data Science

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.

LEARNING OUTCOMES

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:

• 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
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

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.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Where To Earn A Ph.D. In Data Science Online In 2024

Updated: Apr 3, 2024, 2:15pm

Data science is among the most in-demand skill sets in the modern economy. Data science professionals help businesses make decisions by creating analytical models, combining elements of math, artificial intelligence, machine learning and statistics.

If you want to pursue a high-paying data science career or teach data science at the college level, you may want to earn a terminal degree in the field. Online Ph.D. in data science programs allow you to advance your career while balancing other responsibilities at work or home.

We found two online data science programs that met our ranking criteria. Read on to learn more about these schools and find answers to frequently asked questions about data science.

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• Over 3,868 accredited, nonprofit colleges and universities analyzed nationwide
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Online Ph.D. in Data Science Option

Capitol technology university, national university.

Located just outside Washington, D.C., in South Laurel, Maryland, Capitol Technology University offers an online doctoral degree in business analytics and data science. The program includes a limited residency requirement: Students must complete a course in contemporary research in management on campus, during which they take a qualifying exam. The degree requires 54 to 66 credits, and students can graduate within three years.

All students must also complete a dissertation and an oral defense of their work. The program costs $950 per credit for both in-state and out-of-state learners. Retired and active duty military receive a tuition discount.

At a Glance

• School Type: Private
• Application Fee: $100
• Degree Credit Requirements: 54 to 66 credits
• Program Enrollment Options: Part-time
• Notable Major-Specific Courses: Management theory in a global economy; analytics and decision analysis
• Concentrations Available: N/A
• In-Person Requirements: Yes, for residency

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. NU aims for flexibility, delivering coursework asynchronously and offering a new start date each Monday. The curriculum comprises 20 courses covering data science principles and data preparation methods.

NU runs on the quarter system and charges $442 per quarter unit for graduate courses. The program does not include any in-person requirements.

• Application Fee: Free
• Degree Credit Requirements: 60 credits
• Notable Major-Specific Courses: Principles of data science, data preparation methods
• In-Person Requirements: No

How To Find the Right Online Ph.D. in Data Science for You

Consider your future goals.

A Ph.D. in data science makes sense if you want to become a college professor , conduct original research or compete for the highest-paying and most cognitively demanding business analytics and machine learning positions. If you plan to pursue other careers, you may not need a terminal degree in this field.

If you want to work in academia, make sure your chosen doctorate in data science includes a dissertation requirement. A dissertation allows you to perform original research and contribute to scholarship in your field before you graduate. In turn, you’ll get a sense of your chosen career and a head start on professional publication.

Understand Your Expenses and Financing Options

Per-credit tuition rates for the programs in our guide ranged from $442 to $950. A 60-credit degree from NU totals about $26,500, while the 66-credit option at Capitol Tech costs more than $62,000.

Private universities, including NU and Capitol Tech, tend to cost more than public schools. Graduate students at nonprofit private universities paid an average of $20,408 per year in 2022-23, according to the National Center for Education Statistics . Over the course of a typical three-year Ph.D. program, this translates to about $61,000. This roughly matches Capitol Tech’s tuition, while NU offers a more affordable program.

While a Ph.D. might help you land a lucrative role in the long run, the upfront investment is still significant. Make sure to fill out the FAFSA ® to access federal student aid. This application is the gateway to opportunities like scholarships, grants and loans. You can pursue similar opportunities through schools and nonprofit organizations.

As a doctoral student, you may be able to access graduate assistantships or stipends, but these are often reserved for on-campus students who teach undergraduates or assist professors with research.

Should You Enroll in a Ph.D. in Data Science Online?

Pursuing a Ph.D. in data science online suits a specific kind of learner. To decide if that’s you, ask yourself a few key questions:

• What’s my budget? In some cases, public universities allow students who exclusively enroll in online courses to pay in-state or otherwise discounted tuition rates. Even if you have to pay full price, distance learners generally save on costs associated with housing and transportation.
• What are my other commitments? Distance learning is often a good fit for parents and students who need to work full time while pursuing their degree. Learners with outside responsibilities might pursue a program with asynchronous course delivery, which eliminates scheduled class sessions.
• What’s my learning style? Distance learning requires a great deal of discipline, organization and time management. If you need external accountability or prefer the structure of a peer group or physical classroom, on-campus learning might offer a better fit.

Accreditation for Online Ph.D.s in Data Science

There are two important types of college accreditation to consider: institutional and programmatic.

Institutional accreditation is essential; it involves vetting schools to ensure the quality of their finances, academics, and faculty, among other areas. The Council for Higher Education Accreditation (CHEA) and U.S. Department of Education oversee the regional agencies that administer this process.

You should only enroll at institutionally accredited schools. Otherwise, you will be ineligible for federal financial aid. You can check a school’s accreditation status on its website or by visiting the directory on CHEA’s website .

Individual departments and degrees earn programmatic accreditation based on their curriculum, faculty and learner outcomes. However, this process has not been widely established for data science programs, so it shouldn’t make or break your enrollment decision. However, you can still keep an eye out for accreditation from the Data Science Council of America (DASCA).

Our Methodology

We ranked two accredited, nonprofit colleges offering online Ph.D.s in data science in the U.S. using 15 data points in the categories of student experience, credibility, student outcomes and affordability. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites.

Data is accurate as of February 2024. Note that because online doctorates are relatively uncommon, fewer schools meet our ranking standards at the doctoral level.

We scored schools based on the following metrics:

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Availability of online coursework
• Total number of graduate assistants
• Proportion of graduate students enrolled in at least some distance education

Credibility:

• Fully accredited
• Programmatic accreditation status
• Nonprofit status

Student Outcomes:

• Overall graduation rate
• Median earnings 10 years after graduation

Affordability:

• In-state graduate student tuition
• In-state graduate student fees
• Alternative tuition plans offered
• Median federal student loan debt
• Student loan default rate

We listed the two schools in the U.S. that met our ranking criteria.

Find our full list of methodologies here .

Frequently Asked Questions (FAQs) About Earning a Ph.D. in Data Science Online

Can i do a ph.d. in data science online.

Yes, you can. National University and Capitol Technology University both offer Ph.D. programs in data science that you can complete mostly or entirely online.

Is a Ph.D. worth it for data science?

It depends on your goals and circumstances. A Ph.D. in data science may be a good fit if you want to pursue a career in research or academia or compete for advanced, lucrative positions in business analytics, artificial intelligence or machine learning.

Is it okay to get a Ph.D. online?

Yes, as long as the program is accredited. Distance learning requires strong motivation and self-discipline, so it suits some students better than others.

Can you become a professor with an online Ph.D.?

Yes, you can. Online diplomas feature the same coursework and degree requirements as in-person degrees, and your degree won’t say “online”.

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PhD in Data Science

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 program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and four graduate electives that can come from proposed courses in data science as well as existing courses in Computer Science or Statistics. Some students, after consulting with the graduate committee advisor, might decide to take the nine courses over the first two years:

Required Courses:

• Foundations of Machine Learning and AI Part 1
• Responsible Use of Data and Algorithms
• Data Interaction
• Systems for Data and Computers/Data Design
• Foundations of Machine Learning and AI Part 2
• Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science (CODAS) .

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee in order to be advanced to candidacy. The proposal presentation is typically an hour-long meeting that begins with a 30-minute presentation by the student followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded to candidates following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

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

About the Doctoral Degree 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 One: Pre-Program Requirements

• Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
• Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
• Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

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: 

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives/concentration

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.

• at least 15 credit hours in CS courses at 8000 or 9000 levels (except CS 9900)
• at least 15 credit hours in STAT courses at 8000 or 9000 levels
• HHS 8000 - Introduction to mHealth
• HHS 8010 - Ethical Issues in mHealth, Healthcare and Human Subjects Research
• STAT 8235 - Advanced Longitudinal Data Analysis
• HHS 8050 - Advanced Research in mHealth
• HHS 8020 - mHealth Applications or HHS 8030 - Advanced Special Topics in mHealth
• Develop Dissertation Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

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.

Program Student Learning Outcomes

At the end of the program, students will be able to:

• Demonstrate their understanding of the research process
• Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
• Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

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?

• Applied Computer Science
• Applied Economics and Statistics
• Applied Statistics
• Applied Mathematics
• Bioinformatics
• Business Analytics
• Chemical Biology
• Computer Science
• Data Science
• Forecasting & Strategic Management
• Integrative Biology
• Public Admin in Economic Policy Mgmt
• Mathematics
• Mechanical Engineering
• Software Engineering

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?

• Ajou University, South Korea
• Albert-Ludwigs University of Freiburg
• Auburn University
• Bowling Green State University
• Clemson University
• Columbia University
• Columbus State University
• Florida State University
• Georgia Southern University
• Georgia State
• Georgia Tech
• Iran University of Science and Technology
• Kennesaw State University
• Marshall University
• Michigan State University
• Murray State University
• North Carolina State University
• St. Petersburg State University, Russia
• University of KwaZulu-Natal, South Africa
• University of Michigan
• University of North Carolina
• University of Toledo

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: 

• Graduate Research Assistant and Teacher of Record, Kennesaw State University
• Mathematics Instructor, Al-Ihsan Group of School, Offa Kwara Nigeria
• Data Entry Officer, National Examination Council (NECo), Niger State Nigeria

Courses Taught:  Introduction to Data Science

Publications:  

• Muritala, F., Olayiwola, R.O., Oyedeji, A.A., Akande, S.O. (2021). “ Mathematical Modeling of Heat Transfer in Micro-channels ”. Journal of Science, Technology, Mathematics, and Education (JOSTMED), 17(3), September 2021.
• Muritala, F. (2022). K-Step Block Hybrid Method for Numerical Solution of Fourth Order Ordinary Differential Equations (Accessed ProQuest). Kwara State University, Malete, Nigeria.
• Muritala, F., Kolawole, M.K., Oyedeji A.A., Lawal, J.O., Alaje A.I., “ Development of an Order (K+3) Block-Hybrid Linear Multistep Method for Direct Solution of General Second Order Initial Value Problems ”, UNIOSUN Journal of Engineering and Environmental Sciences. Vol. 4 No. 2. Sept. 2022. DOI: 10.36108/ujees/2202.40.0230.
• Muritala F., Jimoh A.K., Ogunniran M.O., Oyedeji A.A., Lawal J.O., “ Coherent Hybrid Block Method for Approximating Fourth-Order Ordinary Differential Equation”, Journal of Amasya University the Institute of Sciences and Technology 2023, DOI: 10.54559/jauist.1262994.
• Lawal J., Zhiri A., Muritala F., Ibrahim R., Lukonde A., “Mathematical Modeling for Mycobacterium Tuberculosis” Journal of Balkan Science and Technology 2024. DOI: 10.55848/jbst.2024.41
• Austin. R.B., Muritala F., “A Nonparametric Process Capability Index for Multiple Stream Processes”. 2024 Joint Statistical Meeting Conference Paper, August 2024, Portland, Oregon. 

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)

• Limited Term Instructor, Kennesaw State University
• Reporting Business Analyst, Northeast Georgia Health System
• Virtual Mathematics and Computer Science Instructor, Imagine Learning
• Mathematics Instructor, Dekalb County School District

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:  

• Credit Modeling Analyst, Agricultural Development Bank of China
• Research Assistant, Changjiang Securities
• Graduate Assistant, Syracuse University

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

• Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
• Software Engineer II , Xgrid Pakistan
• Senior Research Coordinator, Aga Khan University Pakistan
• Machine Learning Engineer, Agoda Thailand

Publications:  National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan 

Service and Awards:

• Fulbright Scholarship award for Master’s degree in Data Science
• Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering 

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

• Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
• Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

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

• An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
• A Conversational Recommender System for Exploring Pedagogical Design Patterns
• An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning  

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

• Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829.   doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
• Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821.   doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

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:

• from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
• Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
• Consultant, The World Bank, Washington D.C.

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

• Research Assistant: US Army Research Lab, Kennesaw State University
• Consultant, Statistical Consulting Service, University of Toledo
• Graduate Teaching Assistant, University of Toledo

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

• T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
• UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
• Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

• Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham -   https://doi.org/10.1007/978-3-031-04321-5_3
• An empirical analysis of image augmentation against model inversion attack in federated learning -   https://doi.org/10.1007/s10586-022-03596-1
• M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
• M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
• Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
• Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
• Winners of 2021 Analytics Day Ph.D. level research poster presentation 

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

• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
• University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
• South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

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

• Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
• Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
• Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
• Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught:   Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

• Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
• Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
• Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
• Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

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 

• Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health.  JAH-2021-00601R1
• Symon K Kimitei,   Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks 

Poster Presentations:

• Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation.  11 Nov. 2019. Poster presentation.
• Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University.  19 April. 2018. Poster presentation.

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

• Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
• Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

• Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’,   International Journal of Machine Learning and Cybernetics .
• Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
• Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

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

• Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
• Graduate Lab Assistant - Tutored students with statistics and math subjects.

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:

• Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
• Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
• Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

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

• Graduate Research Assistant, Kennesaw State University, 2017-present
• Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

• T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

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

• Graduate Research Assistant, Kennesaw State University, 2016-2018
• Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
• Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

• S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
• Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
• Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
• Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
• Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
• Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
• S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
• 1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
• People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
• CCSE Dean’s 4.0 Club, Jan 2018
• 3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
• Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
• Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
• Member of HandsOnAtlanta, since 2014
• SOA Associate, Certified by IBM, Jun 2008

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

• Georgia Tech Research Institute, Sr. Research Engineer, 2001-
• APower Solutions, Vice President, 1999-2001
• Georgia Tech Research Institute, Research Engineer II, 1990-1999
• Georgia Tech, School of Electrical Engineering, Research Engineer I, 1986-1990

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

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Ph.D. Specialization in Data Science

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.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

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• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• Data Science Institute Avanessians Director of the Data Science Institute
• The Fu Foundation School of Engineering and Applied Science Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Research

Department of Data Science

• Graduate Programs

Ph.D. in Data Science

(Qualifying students may be eligible for an application fee waiver. Contact Dr. Hai Phan, program director, at [email protected] for further information.)

Considering the Ph.D. in Data Science

Why pursue a ph.d..

You are the master of your professional destiny.

The NJIT Advantage

Our renowned research makes a world of difference

The world is waiting for people like you. Take the next step ahead.  

The Ph.D. in Data Science is jointly administered by the Department of Data Science in the Ying Wu College of Computing and the Department of Mathematical Sciences in the College of Science and Liberal Arts. To accommodate different interest profiles of students, the program offers two options. There is significant overlap between the two options.

Computing Option

Explore the path to innovation

Statistics Option

Formulate the solution for transformation

Contact the Program Director

Students graduating with a PhD degree in Data Science should anticipate the acquisition of skills, knowledge, and professional training that will enable them to pursue data science careers such as data scientist, data analyst, data engineer, data miner, and academic data science researcher in a broad range of industrial sectors, startups, academia, and government institutions. The primary goal of the PhD degree in Data Science is to educate students who have the necessary skills and knowledge to pursue competitive professional and academic careers, swiftly advancing to leadership positions and to contribute to the creation of novel insights and knowledge in the field.

Application deadlines are October 15 for spring and December 15 for fall. However, we will continue to accept applications after the deadline for qualified candidates.

Prospective applicants are expected to have software development experience, computational skills, and an understanding of statistical methods. The minimum requirements for admission to the PhD program are within the guidelines and policies approved by the University and include:

• A Bachelor’s degree in data science, computer science, informatics, mathematics/statistics, engineering, or another closely related discipline (as approved by the PhD directors) from a college or university accredited in the United States, or its equivalent, with an expected overall GPA of 3.5 out of 4.0.
• GRE scores are required. They will be evaluated in agreement with other Ph.D. programs at NJIT.
• Prepared students shall have a good background in programming and data structures (corresponding to NJIT CS 280 and CS 435), multivariate calculus (e.g. NJIT Math 211), and Probability and Statistics (e.g. Math 333/341). Admitted students lacking competencies in one or more of these areas shall consult with the academic advisor to take relevant preparatory courses. 
• International student applicants shall demonstrate proficiency in English if it is not their first language, following the NJIT admission standard. Exemptions can be granted to applicants who have earned (or will earn, before enrolling at NJIT) a Bachelor’s, Master’s, or Doctoral degree from a university of recognized standing in a country in which all instruction is provided in English.

Progression of Students

To continue in the Ph.D. program, a student must fulfill the following requirements/milestones:

Maintain a cumulative GPA of 3.0 or better. Students will need a cumulative GPA of 3.5 if they wish to be considered for financial support of any kind.

End of year one: Students must take the written part of the Ph.D. qualifying exam.

Every student (in both options) will have to pass qualifying exams in these two courses:  

CS 675     Machine Learning

MATH 644    Regression

• CS 644   Introduction to Big Data OR   IS 650   Data Visualization & Interpretation
• MATH 631    Linear Algebra

Upon the approval of the PhD program director, students must file a program of study that lists the courses to be taken and the timeline of study. 

Dissertation

Students are recommended to choose a dissertation advisor as soon as possible, but no later than 3 months after passing the qualifying exam. A student needs to inquire who among the tenured/tenure track faculty is closest to their area of research interest. The Ph.D. program director should be consulted for this purpose, unless the student has already determined who they wish to work with, e.g., based on class offerings or publication records. 

Students will have to pass the oral part of the qualifying exam, followed by registering for research credits. They have to present, orally and in writing, a Dissertation Proposal and, before graduating, have to write and orally defend a state-of-the-art research dissertation in front of a committee of faculty members.  Individual professors will impose publication requirements in conferences and/or academic journals as a condition for graduating. 

“Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity. So must data be broken down, analyzed for it to have value.” - The British mathematician Clive Humbly

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Discover novel solutions to data research problems

There’s no choice but to lead when you’re breaking new ground. Guide rapid development in an emerging field when you earn our Ph.D. in Data Science.

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Data Science Ph.D.

A dynamic data science environment.

Graduates of our program—the first of its kind in both Indiana and the Big Ten—develop the skills to make pioneering research contributions to data science theory and practice in academic and the industrial sectors.

Our students acquire the skills to develop inventive and creative solutions to data research problems—solutions that demonstrate a high degree of intellectual merit and the potential for broader impact. The Ph.D. curriculum also prepares students to make research contributions that advance the theory and practice of data science.

A leader in data science research

The Data Science Ph.D. Program at IU Indianapolis provides a world-class education and research opportunities. Ph.D. students in the program learn fundamental Data Science methods while pursuing independent, original research in a broad variety of topics, including:

• Novel techniques for Natural Language Processing and Text Analytics.
• Applications of AI to social welfare, digital governance, cultural heritage, biomedical sciences, and environmental sustainability.
• Intelligent conversational agents and models of Human-AI collaboration.
• Data Visualization and Human-Data Interaction.

Meet our faculty

The program is in the midst of a major expansion, with over 50 graduate students joining the program in the past year alone. Multiple faculty in our department have secured high-profile research grants, including three    active   CAREER awards, the National Science Foundation’s most prestigious award for early-career faculty. The IU Indianapolis campus hosts the newly created Institute of Integrative Artificial Intelligence, providing an interdisciplinary nexus between Data Science, AI, and various science and engineering fields.

Sunandan Chakraborty

Associate Professor, Data Science

Sarath Chandra Janga

Associate Professor, Bioinformatics, Data Science

Assistant Professor, Data Science

Leon Johnson

Lecturer, Data Science

Kyle M. L. Jones

Associate Professor, Library and Information Science, Data Science

Bohdan Khomtchouk

Assistant Professor, Bioinformatics, Data Science

Angela Murillo

Assistant Professor, Library and Information Science, Data Science

Saptarshi Purkayastha

Associate Professor, Data Science, Health Informatics

Khairi Reda

Associate Professor, Data Science, Human-Computer Interaction

Elie Salomon

Lecturer, Data Science; Library and Information Science

Ayoung Yoon

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Ph.D. in Data Science

The ph.d. in data science at smu is distinctive because of its highly interdisciplinary nature..

Most existing Data Science Ph.D. programs are either housed in a single department, such as Statistics, Computer Science, Operations Management or Business Analytics; or they focus on a single disciplinary area of research, such as Business or Medicine.

The program’s core curriculum consists of courses in Computer Science, Operations Management, Statistics, and Data Science, and elective courses go beyond those disciplines to include Mathematics, Finance, Marketing, Education, Psychology, Chemistry, Game Design, Economics, and more. Student and faculty interest will continue to set directions for how the program evolves in the future.

Another distinctive feature are the research rotations that students engage in after having completed 4 semesters of coursework.

The goal of this program is to recognize that data science research can inform nearly every discipline at the university and beyond; and that the future of research and work in data science will not be limited to specific and restricted areas.

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

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.

Requirements

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:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

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:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, 2 Probability for Statistical Inference 1, 2
• STAT 440 Applied Stochastic Processes for Statistics
• STAT 457-0 Applied Bayesian Inference

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 Qualifying Exam. This comprehensive examination covers basic topics in statistics and data science and and is typically taken in fall quarter of the second year.

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.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

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:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

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PhD in Computing & Data Sciences

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).

Learning Outcomes

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.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

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:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

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.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

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.

Dissertation and Final Oral Examination

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.

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• BS/MS in Data Science
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• PhD in Computing & Data Sciences
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• BS in Data Science/MS in Bioinformatics
• MS in Bioinformatics
• PhD in Bioinformatics

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Top 10 Universities in USA Offering Ph.D In Data Science

In 2011, McKinsey Global Institute called Big Data as the Next Frontier. From then to now, data science has evolved to form an integral part of digital transformation and technological innovation for the next futuristic world. This implies that not only is the demand for data scientists is growing everyday but also they receive lucrative salaries due to high market demand. Every industry from the fields of business, finance, government, healthcare, social networking, and technology, are looking for minds with a data science degree and complementary skills. Further, earning a Post-Doctoral degree in this field is beneficial because it can give students the research skills needed to make an impact in their field of choice. Depending on the university attended, a student might have to take classes such as machine learning and computational statistics, big data, probability and statistics for data science, and inference and representation along with major in data science.

Below is a list of top 10 of universities in the USA that have some excellent data science programs and courses .

Brown University – Providence, Rhode Island

Course: PhD in Computer Science – Concentration in Data Science

Brown University's database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL. To gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project. Coding well in C++ is preferred. All students must also train as teaching assistants for at least one semester.

2019-2020 Tuition: $66,702 per year

Length: 6 Credit Hours

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana

Course: PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree. The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within seven years. Applicants are generally expected to have a master's in social science, health, data science, or computer science. Currently, a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. All students receive scholarships. IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

2019-2020 Tuition: $368 per credit (Indiana Resident), $1,006 per credit (Non-resident)

Length: 60 credits

New York University – New York, New York

Course: PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with ten years of entering the program. Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

2019-2020 Tuition: $1,856 per unit

Length: 72 Credits

Yale University – New Haven, Connecticut

Course: PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students need to complete 12 courses in the first year on these topics. Students are required to teach one course each semester of their third and fourth years. Most students complete and defend their dissertations in their fifth year.

2019-2020 Tuition: $43,300 per year

The University Of Maryland, College Park, Maryland

Course: PhD in Information Studies – Concentration in Big Data/Data Science

PhD students can take part in research in a variety of areas, including big data, data science, and informatics. The program is designed for students who want a research-oriented career, and faculty members mentor students in a variety of disciplines. The program does not list any specific prerequisites for the doctoral program, and the interdisciplinary program accepts students with varied academic backgrounds. Students can go for either Full-time or part-time program.

2019-2020 Tuition: $731 per credit (Maryland Resident), $1,625 per credit (Non-resident)

Length: 27 Credit Hours

Kennesaw State University – Kennesaw, Georgia

Course: 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 six electives (spread over four years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship. Before dissertation research, a comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics. Successful applicants will have a master's degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

2019-2020 Tuition:  $1,066 per credit

Length: 4 years

University of Massachusetts Boston – Boston, Massachusetts

Course: PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies. Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4. Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary. Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study. During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year, students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds.

2019-2020 Tuition: $768 per credit (Massachusetts Resident), $1,499 per credit (Non-resident)

California Institute of Technology, Pasadena, California

Course: PhD in Computing and Mathematical Science focusing on Data Sciences

Caltech has a PhD in Computing and Mathematical Sciences that is multidisciplinary and brings together faculty and students from fields including computer science, electrical engineering, applied math, operations research, economics, and the physical sciences. In their first year, all students take courses in math and computing fundamentals, and each student must take three courses in a focus area and meet breadth requirements. All candidates must complete a dissertation.

Tuition: $54,537 per year

Length: 3 Years

University At Buffalo, Buffalo, New York

Course: PhD in Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo's PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data-intensive computing. Nine credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours and should be completed in 4-5 years. A master's degree is required for admission; courses taken during the master's may be able to count toward some of the core coursework requirements.

Tuition: $5,655 per semester (New York Resident), $11,550 per semester (Non-resident)

Length: 72 Credits

Clemson University / Medical University of South Carolina (MUSC) – Joint Program– Clemson, South Carolina & Charleston, South Carolina

Course: Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

Students can choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students can take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research. Applicants must have a bachelor's in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas. Program requirements include a year of calculus and college biology, as well as experience in computer programming.

2019-2020 Tuition: $668 per credit (South Carolina Resident), $995 per credit (Non-resident)

Length: 65-68 credit hours

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Natural Sciences and Mathematics

Mathematical sciences, doctor of philosophy in data science and statistics.

The program offers extensive coursework and intensive research experience in theory, methodology, and applications of statistics (see  degree requirements ). 

• Faculty members with broad and diverse research interests are available to supervise doctoral dissertations .
• Financial support in the form of assistantships, full tuition support, and scholarships and awards are provided. Additional scholarships are available for US citizens and permanent residents.
• Our students, both domestic and international, have a strong record of starting in full-time jobs right after graduation .
• Students have opportunities to participate in active  Statistics Seminar  series and the departmental  Colloquium series.
• To enhance career prospects, students can pursue  Graduate Certificate in Data Science , and possibly use the certificate courses to fulfill the PhD degree elective requirements.
• NSM Career Success Center  is available to support professional development and experiential learning of students.  
• GRE test score is not required for admission.

100% of our 22 PhD graduates since 2020, both domestic and international, secured full-time employment within a few months of receiving their degrees. 

Placement of 2022 & 2023 PhD Graduates

See a more complete list

Assistantships

Graduate Teaching Assistantships are offered to qualified PhD students on a competitive basis. These assistantships include a monthly stipend (currently set at $2,400) along with a full tuition waiver (covering 9 credit hours per term in the Fall and Spring semesters). The assistantship additionally covers the cost of health insurance purchased through the university and most fees. Graduate Research Assistantships for advanced PhD students are also available on some faculty members’ research grants. Typically, assistantship support is provided for five years and encompasses the Summer semester as well.

All admitted students are considered for assistantships; no separate application is necessary. 

Scholarships, Fellowships & Awards

PhD students are additionally supported through the following awards:

• NSM McDermott PhD Admission Fellowship  (for highly qualified new students, offered at the time of admission)
• Dean’s Fellowship  and  EEF Scholarship  (for highly qualified new students who are U.S. citizens and permanent residents, offered at the time of admission)
• Julia Williams Van Ness Merit Scholarship  and  Mei Lein Fellowship
• Outstanding Teaching Assistant of the Year Award
• Dean of Graduate Education Dissertation Research Award
• Best Dissertation Award ,  David Daniel Thesis Award , and  Outstanding Graduate Student Award

Conference Travel Support

NSM Conference Travel Award  and  Betty and Gifford Johnson Travel Award  are available to provide financial support to PhD students to present their research at professional conferences.

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

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

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Data Science Doctoral Program

Program details.

Gain in-demand skills in emerging areas like artificial intelligence, machine learning and language processing in a Ph.D. program designed with input from industry leaders.

An interdisciplinary degree program of the Schaefer School of Engineering and Science and the Stevens School of Business, the data science Ph.D. curriculum drives students to master the bedrock principles, methods and systems for extracting insights from rich data sets. Then, you’ll apply those theories, techniques and applications in practical research alongside Stevens faculty who are working at the forefront of the data science field. Our graduates go on to pursue research careers in academia and secure important positions in industries like business, financial services and life sciences.

The Department of Computer Science offers dynamic opportunities to explore leading-edge research within a close community of faculty mentors. You'll be able to study under a faculty mentor in the area that you find most exciting:

Theoretical underpinnings of data science, including machine learning and artificial intelligence

Applications of data science to financial services

Applications of data science to the life sciences

Areas of Focus

Mathematical and statistical modelling including multivariate analytics, financial time series and dynamic programming techniques

Machine learning and artificial intelligence applications for statistical learning and financial analytics

Computational systems, exploring advanced algorithm design, distributed systems and cloud technologies

Data management at scale, involving a deeper dive into data technologies, mobile systems and data management

The Stevens Advantage: Widen Your Career Options

Learn more about what makes graduate education from Stevens a unique experience:

Graduate Cooperative Education Program : Available with two tracks, your co-op experience can serve as a starting point for a research project or augment your on-campus research with complimentary experience.

International Student Experience : Tap into our expanding worldwide network of research, academic and alumni partners and mentor with our expert faculty in a number of federally-designated STEM degree programs. Optional Practical Training (OPT) or Curricular Practical Training (CPT) is available to gain work experience in your major/field of study.

State-of-the-Art Research Labs and Facilities : Build, tinker and test your designs in Stevens' MakerCenter, Prototype and Object Fabrication Lab, or numerous other research facilities.

Research Opportunities : Renowned faculty, labs and research centers – as well as industry partnerships and funding from leading national agencies – support strategic and interdisciplinary research in engineering and science.

Assistantships and Fellowships: Stevens offers funding to select graduate students in the form of teaching assistantships, research assistantships and fellowships. Limited in number, these highly competitive opportunities are awarded to exceptional candidates based on merit.

Expanded Learning Options : The Schaefer School offers new opportunities for doctoral students to do coursework at universities in the New York City area – and around the world – through our growing list of academic partnerships with other prestigious universities. Learn more about our latest partnerships.

Computer Science Research

The computer science department at Stevens offers you a maximum amount of flexibility to pursue research opportunities in cutting-edge, competitive areas of exploration like secure systems, machine learning, cryptography and visual computing. You’ll work with recognized leaders in the field, gain exposure to top industry labs and learn sought-after principles that will help propel your career. Learn more about research in the Department of Computer Science.

Program Admission Requirements

We welcome applicants with a master’s degree in a technical discipline (such as computer science, business intelligence and analytics, financial analytics, financial engineering or biomedical engineering and chemical biology). However, exceptional applicants with a bachelor’s degree and relevant work experience will also be considered.

Students may begin this Ph.D. program in the fall semester only. Therefore, applications must be submitted by February 1 for admission the following fall. Applicants are generally notified of their admission status around February 15.

Prerequisite courses in calculus, statistics, probability, algebra and database management

Fluency in at least one programming language, like C++ or Java

Transcripts from all post-secondary institutions attended

Two letters of recommendation (academic or professional only; Select Ph.D. programs require a third letter of recommendation)

Statement of Purpose

$60 non-refundable Application Fee

Proof of English language proficiency

A competitive GRE or GMAT score (required for both part-time and full-time applicants)

Writing sample(s). All applicants are encouraged to submit a lab report (preferable) or paper that they wrote, individually, for an engineering course. Applicants who have published a journal article are also encouraged to submit a copy of their article.

For more complete details, visit our General Admissions Requirements page .

Apply Online >

View objectives, outcomes, and other Ph.D. curriculum details in the most recent academic catalog.

View Academic Catalog >

Each Ph.D. curriculum must also adhere to the institute wide standards listed in the doctoral handbook.

View Doctoral Handbook >

If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.

Information about assistantships and fellowships can be found here .

The four fields comprising STEM – science, technology, engineering and mathematics – offer a wide variety of professions that are classified as some of the highest-growing and highest-paying jobs right now and in the future. And for international students, the demand for STEM-related professionals in the United States can open the door for an extended stay. An ever-growing list of eligible programs across all levels is available here .

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Prepare to make an enduring impact in fields like machine learning, artificial intelligence and cybersecurity with a Ph.D. in computer science from Stevens.

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The challenges facing today's scientists and engineers often exist at the intersection between various disciplines–whether between engineering and science or fields within individual disciplines. At Stevens, engineering and science come together under one roof, fostering a proactive, interdisciplinary environment that encourages results-driven collaboration and unique, innovative problem solving.

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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

If you’re passionate about big data and interested in an advanced degree, you may be wondering which degree is right for you. Should you go with a Master of Science (M.S.) or a PhD in data science?

Our guide to getting a PhD in data science is here to help. Here, we’ll break down potential pros and cons of choosing either option, related job opportunities, dissertation topics, courses, costs and more.

SPONSORED SCHOOLS

Syracuse university, master of science in applied data science.

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

• Complete in as little as 18 months
• No GRE scores required to apply

Southern Methodist University

Master of science in data science.

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

• No GRE required.
• Complete in as little as 20 months.

University of California, Berkeley

Master of information and data science.

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

• Complete in as few as 12 months
• No GRE required

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Just want the schools? Skip ahead to our  complete list of data-related PhD programs .

Why Earn a PhD in Data Science?

A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The keyword here is  research . Throughout the course of your studies, you’ll likely:

• Conduct your own experiments in a specific field.
• Focus on theory—both pure and applied—to discover why certain methodologies are used.
• Examine tools and technologies to determine how they’re built.

PhD Benefits vs. Downsides

There are a number of benefits and downsides to earning a PhD in data science. Let’s explore some of them below.

Benefits of a PhD in Data Science

In a PhD in data science program, you may have the opportunity to:

• Research an area in data science that may potentially change the industry, have unexpected applications or help solve a long-standing problem.
• Collaborate with academic advisors in data science institutes and centers.
• Become a critical thinker—knowing when, where and why to apply theoretical concepts.
• Specialize in an upcoming field (e.g.  biomedical informatics ).
• Gain access to real-world data sets through university partnerships.
• Work with cutting-edge technologies and systems.
• Automatically earn a master’s degree on your way to completing a PhD.
• Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, some PhDs in data science programs may:

• Take four to five years on a full-time schedule to complete. These are years you could be earning money and learning real-world skills.
• Be expensive if you don’t find or have a way to fund it.
• Entail many solitary hours spent reading and writing
• Not give you “on-the-job” knowledge of corporate problems and demands.

Is a PhD in Data Science Worth It?

A PhD in data science may open the door to a number of career opportunities which align with your personal interests. These include, but aren’t limited to:

• Data scientist.   Data scientists  leverage large amounts of technical information to observe repeatable patterns which organizations can strategically leverage.
• Applications architect.  When you work as an applications architect, your main goal is to design key business applications.
• Infrastructure architect.  Unlike an applications architect, infrastructure architects monitor the functionality of business systems to support new technological developments.
• Data engineer.   Data engineers  perform operations on large amounts of data at once for business purposes, while also building pipelines for data connectivity at the organizational level.
• Statisticians :  Statisticians  analyze and interpret data to identify recurring trends and data relationships which can be used to help inform key business decisions.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

Do You Need a PhD to Land a Job?

In most cases, you don’t need a PhD in data science to land a job. Most  computer and information research-related careers  require a master’s degree, such as an  online master’s in data science .

As you begin your search, pay attention to prospective employers and qualifications for your desired position:

• Companies and labs that specialize in data science—and tech players like  Amazon  and  Facebook  — may have a reason for specifying a PhD in the education requirements.
• Other industries may be happy with a B.S. or M.S. degree and relevant work experience.

Careers for Data Science PhD Holders

People who hold a PhD in data science typically find careers in academia, industry and university research labs,  government  and tech companies. These places are most likely seeking job candidates who can:

• Research and develop new methodologies.
• Build core products, tools and technologies that are based on data science (e.g.  machine learning  or  artificial intelligence  algorithms for Google or the next generation of  big data management systems ).
• Reinvent existing methods and tools for specific purposes.
• Translate research findings and adopt theory to practice (e.g. evaluating the latest discoveries and finding ways to implement them in the corporate world).
• Design research projects for teams of statisticians and data scientists.

Sample job titles include:

• Director of Research
• Senior Data Scientist/Analyst
• Data/Analytics Manager
• Data Science Consultant
• Laboratory Researcher
• Strategic Innovation Manager
• Tenured Professor of Data Science
• Chief Data Officer (CDO)

PhD in Data Science Curriculum

Typical Program Structure Data science PhDs are similar to most doctoral programs. That means you’ll typically have to:

• Complete at least two years of full-time coursework.
• Pass a comprehensive exam—comprising oral and written portions—that shows you have mastered the subject matter.
• Submit a dissertation proposal and have it approved.
• Devote 2-3 years to conducting independent research and writing your dissertation. You may be teaching undergraduate classes at the same time.
• Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you’ll likely engage in professional activities that may help improve your career prospects. Such opportunities include attending and speaking at conferences, applying for summer fellowships, consulting, paid part-time research and more.

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re encouraged not to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD program already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor may be your closest ally in this process.

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science may also fall under the related disciplines such as statistics,  computational sciences  and informatics. It is important to evaluate each program’s curriculum. Will the foundation courses and electives prepare you for the research area that you want to explore?

A related degree you may consider is a PhD in Business Analytics (or Decision/Management Sciences). These degree programs are typically administered through a university’s School of Business, which means the curriculum includes corporate topics like management science,  marketing , customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities offer specialty PhD programs. Biostatistics and biomedical/health informatics are two examples, but you’ll also find a number of doctoral programs in machine learning (usually run by the Department of Computer Science) and sub-specialties in fields like artificial intelligence and data mining.

Considerations When Choosing a PhD Program

Typical Admissions Requirements PhD candidates typically submit an application form and pay a fee. Universities often look for applicants who have:

• A  Bachelor of Science (BS) in computer science , statistics or a relevant discipline (e.g. engineering) and a similar master’s degree with an official transcript from an accredited institution
• A GPA of 3.0 or higher on a 4.0 scale
• GRE test scores
• TOEFL or IELTS for applicants whose native language is not English
• Letters of recommendation
• Statement of purpose/intent
• Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Programs for PhD in Data Science – Online vs. On-Campus Online programs may require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

While online learning can be a convenient way of obtaining your PhD from the comfort of home, there are a few important factors to consider.

• Are you  extremely  passionate about an area of research?
• Do you mind committing to 4-5 years of study?
• Does your university have funding sources (private and government) for data science research?
• Will you have access to exciting data resources, labs and industry partners?
• Do you know how you’re going to pay for the program?

How Much Does a PhD Cost?

As you research PhD in data science programs, you’ll probably find information on relevant fellowships on some university websites, as well as advice on financial matters. Here are a few ways that you may be able to fund your education:

• PhD Fellowships:  You’ll find a number of fellowships sponsored by the university, by companies and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
• Teaching/Research Assistantships:  Assistantships are a common way for universities to support PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
• In-State Tuition : Public universities may offer in-state students a much lower cost per credit.
• Regional Discounts:  Many state universities have agreements to offer reduced tuition costs to students from neighboring states (e.g.  New England Board of Higher Education Regional Student Program (RSP) . Check to see if this applies to your PhD.
• Travel Grants:  Doctoral students may have the opportunity to attend research conferences and network with future collaborators. Some grants are designed with this purpose in mind.
• Student Loans:  In addition to grants, you can consider applying for student loans to finance your PhD studies. Remember, a doctorate is a long-term commitment—you may not see a financial return on your education for a number of years.

Some PhD students in data science are  fully funded . For example:

• U.S. citizens and permanent residents in  Stanford’s PhD in Biomedical Informatics  are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants

If you’re coming from overseas, try talking to your school about any differences between funding for citizens and international students.

How Long Does a PhD in Data Science Take?

The length of time it takes to obtain a PhD will likely vary depending on your chosen program. Programs for similar or identical degrees can have differing completion requirements at different schools, meaning how many years your PhD program takes will differ as well.

Of course, the amount of time you spend working toward a PhD in data science can also vary depending on whether you choose to take it part-time or full-time. Assuming you consistently pass your classes, a full-time commitment to your PhD program will expedite your way through it.

But a commitment like that won’t fit everyone’s lifestyles. For example, you might need to work to support yourself financially, or you might be raising a family. These sorts of important commitments are time-consuming and can take a lot of energy. So, in that case, a part-time commitment to your PhD program might make more sense for you.

Interested in STEM Careers? 

If you’re looking for information on  career paths that involve STEM , see our guides below:

Data Science and Analytics Careers:

• Data Scientist
• Data Analyst
• Business Analyst

Computer Science, Computer Engineering and Information Careers:

• Computer and Information Research Scientist

Marketing and User Research Careers:

• UX Designer  

Compare Careers and STEM Fields:

• Cybersecurity vs. Computer Science

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• Sponsored:  Computer Science at Simmons

PhD in Data Science School Listings

We found 57 universities offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings or adding new programs, please send an email to [email protected].

Last updated August 2021. The program’s website is always best for most up to date program information.

PhD in Data Science/Analytics Online

Looking for on-campus programs? See the  full list of on-campus PhD in Data Science/Analytics programs .

Colorado Technical University

Doctor of computer science – big data analytics, colorado springs, colorado.

Name of Degree: Doctor of Computer Science – Big Data Analytics

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

School of computer science, ph.d. program in machine learning, pittsburgh, pennsylvania.

Name of Degree: Ph.D. Program in Machine Learning

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

• Recent transcripts
• Statement of purpose
• Three letters of recommendation
• TOEFL scores if your native language is not English

Chapman University

Schmid college, ph.d. in computational and data sciences, orange, california.

Name of Degree: Ph.D. in Computational and Data Sciences

Enrollment Type: Full-Time and Part-Time

Credits: 70

• GRE required
• Statement of intent 
• Resume or curriculum CV.                                       
• TOEFL score for international students

Indiana University – Indianapolis

School of informatics and computing, ph.d. in data science, indianapolis, indiana.

Name of Degree: Ph.D. in Data Science

Credits: 90

• Bachelor’s degree; master’s preferred
• Transcripts
• TOEFL or IELTS

Kennesaw State University

School of data science analytics, doctoral degree in analytics and data science, kennesaw, georgia.

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

• Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

Looking for online programs? See the  full list of online PhD in Data Science/Analytics programs .

New York University

Center for data science, new york , new york.

Credits: 72

• Resume or curriculum CV
• TOEFL or IELTS (TOEFL Preferred)
• Statement of Academic purpose

Institute for Computational and Data Sciences

Phd computational and data enabled science and engineering, buffalo, new york.

Name of Degree: PhD Computational and Data Enabled Science and Engineering

Computational Data Sciences  

• Master’s degree
• Resume or CV
• GRE scores (Temporarily suspended)

University of Maryland

College of information studies, doctor of philosophy in information studies, college park, maryland.

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

• Transcripts 
• Resume or CV or CV
• academic writing sample
• TOEFL/IELTS/PTE (required for most international applicants)

University of Massachusetts in Boston

College of management, doctor of philosophy in information systemaster of science for data science and management, boston, massachusetts.

Name of Degree: Doctor of Philosophy in Information SysteMaster of Science for Data Science and Management

Credits: 42

• Official transcripts official
• GMAT or GRE scores scores
• Official TOEFL or IELTS score.

University of Nevada – Reno

College of science, ph.d. in statistics and data science, reno, nevada.

Name of Degree: Ph.D. in Statistics and Data Science

Length of Program: 4+ years

• Undergraduate/Graduate Transcripts
• TOEFL/IELTS (only required for international students)

University of Southern California

School of business, ph.d. in data sciences & operations, los angeles, california.

Name of Degree: Ph.D. in Data Sciences & Operations

• Undergraduate/Graduate Transcripts 
• GRE or GMAT
• (3) letters of recommendation
• Passport Copy

University of Washington

Mechanical engineering, doctor of philosophy in mechanical engineering: data science, seattle, washington.

Name of Degree: Doctor of Philosophy in Mechanical Engineering: Data Science

Worcester Polytechnic Institute

Worcester, massachusetts.

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.

Choosing a Field of Study

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 .

Residency Requirements

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.

Your Advisor and Special Committee

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 . 

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. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

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.

• Computing PhD
• Emphasis Areas
• Data Science

Data Science Ph.D.

Application Requirements for Data Science Ph.D.

View Degree Plan

Download Data Science Course Checklist (Excel)

Degree Requirements

• COMPUT 601 – Introduction to Graduate Studies (1 credit)

Required Core Courses (12 credits):

• CS 533 – Introduction to Data Science (3 credits)
• CS 534 – Machine Learning (3 credits)
• MATH 562 – Probability and Statistics II (3 credits)
• MATH 572 – Computational Statistics (3 credits)

Data Science Elective Courses (6 credits):

• 3 credits must be in CS and 3 must be in MATH. Pre-approved data science electives can be found in the student handbook

Elective Courses (10 credits):

• Must be approved by the supervisory committee and Computing Program directors. Pre-approved electives and specific requirements are given in the  student handbook

Comprehensive Exam (1 credit):

• COMPUT 691 Doctoral Comprehensive Examination (1 credit)

Dissertation (30 credits):

• COMPUT 693 Dissertation (30 credits)

Total Credits: 60

Content on this page is provided as a quick reference for planning. All official degree requirements are published on the Graduate Catalog site .

PhD in Computing

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Data Science, Analytics and Engineering, PhD

• Program description
• At a glance
• Degree requirements
• Admission requirements
• Tuition information
• Application deadlines
• Program learning outcomes
• Career opportunities
• Contact information

Analytics, Big Data, Data Engineering, Data Science, approved for STEM-OPT extension, computing, statistics

Learn to meet the need for data-driven discovery of new knowledge and decision-making, which enhances enterprise performance as well as scientific investigation.

The PhD program in data science, analytics and engineering engages students in fundamental and applied research.

The program's educational objective is to develop each student's ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed. This is accomplished through a rigorous education with research and educational experiences.

Students complete a foundational core covering database management, information assurance, statistical learning and statistical theory before choosing to focus on data analytics or data engineering. The program culminates in the production of a dissertation.

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization period may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

• College/school: Ira A. Fulton Schools of Engineering
• Location: Tempe
• STEM-OPT extension eligible: Yes

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (12 credit hours) CSE 511 Data Processing at Scale (3) CSE 543 Information Assurance and Security (3) CSE 572 Data Mining (3) or IEE 520 Statistical Learning for Data Mining (3) or EEE 549 Statistical Machine Learning: From Theory to Practice (3) IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (3)

Electives and Additional Research (39 credit hours)

Research (12 credit hours) DSE 792 Research (12)

Other Requirements (9 credit hours) data engineering coursework or data analytics coursework

Culminating Experience (12 credit hours) DSE 799 Dissertation (12)

Additional Curriculum Information All students must take qualifying exams covering the required core courses within one year of matriculation into the program.

The dissertation prospectus should be submitted and its oral defense completed no later than one year following completion of the 60th credit hour and also no later than the fourth year in the program.

Students must select coursework from either the data engineering or the data analytics requirements. Students should see the academic unit for the approved course list.

Students cannot take a data engineering or data analytics course and have it meet an elective requirement at the same time. Students need to take a different elective course to reach the number of credit hours required for the program. Other coursework may be used with the approval of the academic unit to fulfill these requirements.

Twelve credit hours of DSE 792 Research are required, and up to 24 credit hours are allowed on the plan of study. Students with research hours in excess of 12 will add these credit hours to their electives and additional research.

Electives include:

• additional DSE 792 Research credit hours (up to 12 credit hours allowed beyond the required 12)
• approved elective courses, of which up to three credit hours of DSE 790: Reading and Conference are permitted, with approval.

When approved by the student's supervisory committee and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to be used for this degree. If students do not have a previously awarded master's degree, the 30 hours of coursework are to be made up of electives to reach the required 84 credit hours.

Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in engineering, computer science, mathematics, statistics or a related field from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

Applicants are required to submit:

• graduate admission application and application fee
• official transcripts
• two letters of recommendation
• letter of intent or written statement
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

ASU does not accept the GRE® General Test at home edition.

If the student is assigned any deficiency coursework upon admission, those classes must be completed with a grade of "B" (scale is 4.00 = "A") or higher within two semesters of admission to the program. Deficiency courses do not apply to the total credit hours required to complete the degree program.

Deficiency courses are: CSE 205 Object-oriented Programming and Data Structures IEE 380 Probability and Statistics for Engineering Problem Solving MAT 242 Elementary Linear Algebra or MAT 342 Linear Algebra or MAT 343 Applied Linear Algebra MAT 267 Calculus for Engineers III

Program learning outcomes identify what a student will learn or be able to do upon completion of their program. This program has the following program outcomes:

• Apply the tools and methods from industrial statistics, operations research, machine learning, computer science and computer engineering on solving data analytic problems.
• Manage large, heterogeneous data sets for knowledge discovery.
• Conduct research resulting in an original contribution to knowledge in data sciences.

Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area, enabling them to pursue careers in the following fields:

• advanced research

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Doctoral Degree Data Sciences Ph.D.

Doctorate education focuses on enabling the student to make original contributions to their respective fields of study.

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The mission of the program is to create scientifically minded and technically proficient professionals with a comprehensive background in the methodological diversity of the data sciences and the intellectual depth to offer influential perspectives to analytical teams across disciplines.

There are two phases of the doctoral program at HU: a learning phase that includes coursework, seminars, research, and fieldwork that contributes to the student’s knowledge in the program of study; and a research phase that focuses on the student’s original research culminating in their final examination. Upon a student’s successful completion of all required course work, defense of the dissertation, and completion of all milestones, the student is awarded the doctoral degree in the program of study.

Program Goals

The Data Sciences Program will produce Ph.D. graduates who will have:

• Applied diverse data science methodologies using a scientific process individually or in teams to provide impactful insights from large sets of data;
• Used effective communications to explain insights from analytical processes on data to diverse audiences; and,
• Grown professionally through self-study, continuing education, and professional development.

Doctorate Program Admissions Process

Doctorate program applicants are encouraged to apply at least six months prior to the start of any semester. This application process allows ample time for an admissions decision and development of an academic schedule. The Admission Committee reviews all documents and will request an interview with the applicant prior to making an admission decision for a limited number of applicants to become resident or non-resident candidates for the degree.

Learn More: Graduate Admissions

“This hot new field promises to revolutionize industries from businesses to government, healthcare to academia.”

– The New York Times

Full Time Faculty

Kevin Huggins, Ph.D., CISSP

Professor of Computer and Data Science

• 717-901-5100, ext. 1619
• [email protected]

Program Courses

The following courses comprise the 36 semester hours required for the Ph.D. in Data Sciences. Complete 18 semester hours in upper level courses, 6 semester hours of Doctoral Research Seminars and defend dissertation proposal, and complete 12 hours to complete the dissertation process and defend the dissertation.

ANLY 705 – Modeling for Data Science (3 credits)

This course provides a more in depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.

ANLY 710 – Appld Expmntal & Quasi-Expmnt Des (3 credits)

Methods and approaches used for the construction and analysis of experiments and quasi-experiments are presented, including the concepts of the design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional factorial designs will be covered along with methods for proper analysis and interpretation in quasi-experiments.

ANLY 715 – Applied Multivariate Data Analysis (3 credits)

This course provides hands-on experience in understanding when and how to utilize the primary multivariate methods Data Reduction techniques, including Principal Components Analysis and Exploratory and Confirmatory Factor Analyses, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.

ANLY 720 – Data Science from an Ethical Perspe (3 credits)

This course introduces the power and pitfalls of handling user information in an ethical manner. The student is offered a historical and current perspective and will gain an understanding of their role in assuring the ethical use of data.

ANLY 725 – Research Seminar in Unstructured (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 730 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 735 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. In addition, topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 740 – Graph Theory (3 credits)

This course introduces standard graph theory, algorithms, and theoretical terminology. Including graphs, trees, paths, cycles, isomorphisms, routing problems, independence, domination, centrality, and data structures for representing large graphs and corresponding algorithms for searching and optimization.

ANLY 745 – Functional Prog Mthds for Data Sci (3 credits)

This course is designed to build on the Functional Programming Methods for Analytics course. The student works to extend programming skills to write the student’s own versions of popular statistical functions using a current programming language.

ANLY 755 – Advanced Topics in Big Data (3 credits)

Topics include the design of advanced algorithms that are scalable to Big Data, high performance computing technologies, supercomputing, grid computing, cloud computing, and Parallel and Distributed Computing, and issues in data warehousing.

ANLY 760 – Doctoral Research Seminar (3 credits)

This seminar provides support to doctoral students within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and students can come together to discuss, support, and share the experiences of working in research.

ANLY 761 – Research Seminar in Unstructured (3 credits)

ANLY 762 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 763 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. Topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 799 – Doctorial Studies (6 credits)

Advancement to candidacy is a prerequisite of this course. This is an individual study course for doctoral students. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.

2024–2025 Academic Course Catalogs

Get information about core courses, electives and concentrations in our current academic course catalog.

• Undergraduate Catalog
• Graduate Catalog
• Undergraduate Catalog (PDF)
• Graduate Catalog (PDF)

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Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses
• pass the first-year core program
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year)
• confirm a principal dissertation research advisor and file for candidacy (early spring quarter of 2nd year)
• satisfy the depth and breadth requirements (2nd/3rd/4th year)
• successfully complete the thesis proposal meeting and submit the Dissertation Reading Committee form (winter quarter of the 3rd year)
• present a draft of their dissertation and pass the university oral examination (4th/5th year)

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 .

Doctoral and Research Advisors

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 and Candidacy

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.

Thesis Proposal Meeting and Dissertation Reading Committee 

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.

University Oral Examinations

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 .

Dissertation

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.

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PhD Program

Requirements for doctor of philosophy (ph.d.) in data science.

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Course Requirements

https://datascience.ucsd.edu/graduate/phd-program/phd-course-requirements/ 

Research Rotation Program

https://datascience.ucsd.edu/graduate/phd-program/research-rotation/

Preliminary Assessment Examination

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

The preliminary assessment is an oral presentation that must be completed before the end of Spring quarter of the second academic year. Students must have a GPA of 3.0 or above to qualify for the assessment and have completed three of four core required courses . The student will choose a committee consisting of three members, one of which will be the HDSI academic advisor of the student. The other two committee members must be HDSI faculty members with  0% or more appointments; we encourage the student to select the second faculty member based on compatibility of research interests and topic of the presentation. The student is responsible for scheduling the meeting and making a room reservation. 

The student may choose to be evaluated based on (A) a scientific literature survey and data analysis or (B) based on a previous rotation project. The student will propose the topic of the presentation. 

• If the student chooses the survey theme, they should select a broad area that is well represented among HDSI faculty members, such as causal inference, responsible AI, optimization, etc. The student should survey at least 10 peer-reviewed conference or journal papers representative of the last (at least) 5 years of the field. The student should present a novel and rigorous original analysis using publicly available data from the surveyed literature: this analysis may aim to answer a related or new research question.
•  If the student chooses the rotation project theme, they should prepare to discuss the motivation for the project, the analysis undertaken, and the outcome of the rotation. 

For both themes, the student will describe their topic to the committee by writing a 1-2 page proposal that must be then approved by the committee. We emphasize that this is not a research proposal. The student will have 50 minutes to give an oral presentation which should include a comprehensive overview of previous work, motivation for the presented work or state-of-the-art studies, a critical assessment of previous work and of their own work, and a future outlook including logical next steps or unanswered questions. The presentation will then be followed by a Q&A session by the committee members; the entire exam is expected to finish within two hours. 

The committee will assess both the oral presentation as well as the student’s academic performance so far (especially in the required core courses). The committee will evaluate preparedness, technical skills, comprehension, critical thinking, and research readiness. Students who do not receive a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program . If the lack of preparation is course-based, the committee can require that additional course(s) be taken to pass the exam. If the lack of preparation is research-based, the committee can require an evaluation after another quarter of research with an HDSI faculty member; the faculty member will provide this evaluation. The preliminary assessment must be successfully completed no later than completion of two years (or sixth quarter enrollment) in the Ph.D. program. 

The oral presentation must be completed in-person. We recommend the following timeline so that students can plan their preliminary assessments:

• Middle of winter quarter of second year: Student selects committee and proposes preliminary exam topic.  
• Beginning of spring quarter of second year: Scheduling of exam is completed. 
• End of spring quarter of second year: Exam. 

Research Qualifying Examination and Advancing to Candidacy

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of five or more members approved by the graduate division as per senate regulation 715(D).  One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Special Requirements: Generalization, Reproducibility and Responsibility A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

Course Exceptions: Students with MS in Data Science (or similar field)

If a student has already been granted a Master’s degree in Data Science (or a related field, as determined by the Graduate Program Committee) before entering the HDSI PhD program, the student can submit a “Requirement Substitution” petition for up to 2 courses to be substituted by DSC 299 (up to 8 units).

Further leniency may be granted in exceptional cases in which both the student and their faculty advisor must separately appeal to the Graduate Program Committee. It is up to the Graduate Program Committee to decide whether the appeal is rejected or granted in part or in its entirety.

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Information Science Ph.D. Program

Doctor of philosophy degree.

The Doctor of Philosophy, Ph.D.  is a research degree. It is awarded in recognition of original scholarship and the generation of new knowledge by immersion in a topic, analysis, synthesis and creativity. When a Ph.D. is awarded, the degree carries and bestows certain rights and responsibilities that relate in large measures to serving society by exploring, shedding light upon, and resolving fundamental problems.

The Doctor of Philosophy degree is said to be fundamentally interdisciplinary. All those who pursue the degree, in one sense or another, seek to clarify some portion of our best possible image of the world. Each of those who pursue the Ph.D. seek to provide the most robust understanding and appropriate tools for enabling each member of society to live well, to make the best life decisions—to become most fully human. Doctoral pursuits follow many paths, use different toolsets, invoke different mindsets, and continue testing assumptions by different means. Over the centuries, many of these paths have clustered into discrete departments or schools. An interdisciplinary program attempts to return to an era of broader assumptions, linking paths and cross-fertilizing research. Such an approach provides resources across boundaries.

Each discipline has its foundational notions of what constitutes doctoral studies. Likewise, each institution sets administrative guidelines and constraints for doctoral studies. The goal is to ensure that society is provided with the most capable people and that each person pursuing doctoral studies has every opportunity and resource to flourish.

The University of North Texas Information Science Ph.D. Program, responds to the varied and changing needs of an information age, increasing recognition of the central role of information and information technologies in individual, social, economic, and cultural affairs. Graduates of the program are prepared to contribute to the advancement and evolution of the information society in a variety of roles and settings as administrators, researchers, and educators

UNT IS Ph.D. Program offers

• excellent research faculty across UNT serving as instructors and advisors;
• a variety of course delivery formats, including online and blended;
• a residential experience with a high level of faculty-student interaction;
• a flexible degree plan tailored to individual interests;
• a culturally and ethnically diverse community of faculty and students;
• competitive scholarship, grant, fellowship, and assistantship opportunities;
• extensive research library resources on campus and online. 

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Handbook for Doctoral Students

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College of Information, Data and Society

Department of Applied Data Science

The Department of Applied Data Science offers interdisciplinary programs, collaborates with Silicon Valley professionals and specializes in data analytics, data engineering and cutting-edge generative AI models to meet growing demands for data science expertise for solving real-world data challenges.

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AURA Technologies

Graduate fellow in applied data science [usa – remote].

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AURA TECHNOLOGIES, LLC (AURA) is an advanced research and development (R&D) and technology company creating game-changing innovations for the US Department of Defense (DoD) in Artificial Intelligence (AI)/Machine Learning (ML), Quantum Computing, and other system-level implementations of cutting-edge technology. We are creating advanced sensing systems for the US Navy; cyber-physical solutions for the US Army and a range of AI/ML platforms for DoD implementation. AURA partners with some of the best companies in the world, such as Boeing, Northrop Grumman, and Lockheed Martin. We also collaborate with the best and brightest at our nation’s universities, including Georgia Tech, Clemson, and NC State University – and others. 

If you are a smart, capable and talented individual who possesses high integrity, thrives in a fast-paced environment, wants to chart your own course based on your capabilities, and is willing to be accountable for failures and successes, then continue reading because you may be the ideal candidate to join our rapidly-growing R&D business. 

AURA has an immediate opening for a part-time, remote AURA Technologies Graduate Fellow in Applied Data Science [USA – Remote]. 

Flexible Work Schedule: Specific work hours are not prescribed, and the work schedule is flexible. AURA work can occur around other commitments such as university course work or research so long as the AURA work is completed within deadlines and expected quality. 

Summary: This is an individual-contributor position. In this role, you will work with technical and non-technical colleagues to write, refine, and edit technical and scientific applied approaches in white papers, proposals, presentations, and other documents comprising technical and non-technical content in the area of Data Science, and as needed, other areas such as AI/ML. Working on proposals and whitepapers are a particularly important part of the job, including responses to US Government and DoD solicitations; The successful candidate may also have the opportunity to work on product documentation, technical reports, presentations, and contribute to technical marketing materials. It is not a requirement for the AURA Technologies Graduate Fellow in Applied Data Science to have subject-matter expertise or experience in any specific technology. The role requires the ability to assimilate information and new technologies quickly and have sufficient literacy in subject matters to integrate with technical content provided by other AURA scientists and engineers. The goal is to be able to produce consistent, coherent, well-written work-products in English.  

Citizenship:  Only US Citizens will be considered for this position.  The work to be conducted includes work on contracts with and funded by the US Government or Department of Defense, which contracts, together with Federal laws and regulations, restrict such work to US Citizens.  

Location: This is a remote role. Candidates must be physically located in the United States and AURA verifies the physical location, in person, of all hires. 

Essential duties: 

•  Writing, refining, and editing technical and scientific applied approaches in white papers, proposals, presentations, and other documents in collaboration with technical staff in a collaborative manner. 
• Ensuring that all documents, especially those being submitted to potential sponsors, conform to submission requirements, guidelines, etc. 

Minimum Requirements: 

• Demonstrable outstanding proficiency in technical writing in English; if possible, please provide or be prepared to provide a portfolio or examples of your work.  You will be required to produce a real-time writing sample as part of the interview process without the aid of a computer. 
• Excellent understanding of Engineering and related principles, concepts, and practice as applied to AI/ML and Data Science, and demonstrable ability to clearly communicate the same to technical audiences 
• Demonstrable ability to rapidly dive into and establish sufficient fluency in unfamiliar technical subjects to integrate, synthesize, and narrate content 
• Eager to learn and dogged in asking and getting answers to questions 
• Detail-oriented with respect to both form (grammar, punctuation, etc.) and content (coherent narrative, clear exposition, flow, voice, etc.). 
• Effectively communicate with/to specific audiences or a diversity of audiences, question assumptions about the reader, and, for product documentation, be mindful of the end-user’s perspective 
• Expert proficiency in Microsoft Word 

Desirable skills or experience (not required): 

• In collaboration with technical staff, translate technical content into forms accessible to non-technical readers 
• Able to provide one or more examples of your work where you are the sole author 
• Expertise in embedded systems, real-time systems, operating systems, control theory/control systems, cybersecurity, cryptography, networking, data science, artificial intelligence, machine learning, cloud computing, signal processing, image processing, graphics, computer vision, augmented or virtual reality, human perception, Quantum Computing algorithms, translation systems, or formal languages  
• Previous work with US Government, DoD, NSF, NIH, DARPA, or other public-sector proposals, RFP/RFQ/RFI/SBIR/STTR responses, or similar written products; previous work for or exposure to the DoD. 
• Ability and desire to create or enhance technical illustrations, such as system architecture or block diagrams, graphs or charts, etc.  
• Candidates are encouraged to submit or provide links to portfolios or examples of their technical writing 
• Expertise in any of the following: electrical or mechanical engineering, physics and/or quantum physics, embedded systems, real-time systems, operating systems, control theory/control systems, cybersecurity, cryptography, networking, data science, artificial intelligence, machine learning, cloud computing, signal processing, image processing, graphics, computer vision, augmented or virtual reality, human perception, quantum computing algorithms, translation systems, or formal languages  

Desired experience: While not required, author on at least two (2) published, peer-reviewed papers, articles, book chapters, or equivalent (include PDFs or links in your cover letter); served as primary or sole author of a technical report published by a university– with such publications in the area relevant to this Fellowship– would be taken by AURA as a very positive indication of capabilities. 

Minimum education: Must have received a PhD relevant to the Fellowship (ME, AE, EE, CS, Physics, etc) or be currently enrolled in a relevant PhD program. 

ADDITIONAL REQUIREMENTS:  

• Candidates must not now or in the future require sponsorship for employment visa status  
• SECURITY CLEARANCE: The successful candidate must be able to obtain a US Government Secret-level clearance once employed by AURA, or have and existing clearance. The successful candidate must maintain the clearance during the duration of their employment, which is a requirement of this position. The US Government generally only grants clearances to US citizens who are free from major criminal convictions. Individuals not meeting these two criteria will most likely not be granted a US Government security clearance. 
• Flexible schedule
• *MetLife Voluntary Benefits (e.g. term life insurance, critical illness care, hospital indemnity insurance, & legal services)     * Subject to eligibility based on average weekly hours  

TO APPLY FOR THIS POSITION: 

Submit your resume/CV in PDF format via instructions at the following link: http://aura.company/careers/

No phone calls after submission. We will let candidates know via automated reply that we have received their resumes and will contact them if there is a good fit after the closing date for this job.

AURA Technologies, LLC is an Equal Opportunity Employer and affirmative action employer of veterans protected under the Vietnam Era Veterans’ Readjustment Assistant Act (VEVRAA).  We are a Drug Free Workplace and thus, all job offers are contingent on successful criminal background check and drug screen.  As a US Federal Contractor, AURA uses the Department of Homeland Security e-Verify system to determine eligibility to legally work in the United States. 

Write a carefully crafted, well-written cover letter that elaborates on your interest in this position and why you think you are the best candidate for the job. Submit your cover letter, CV and three professional references (one of which must be from a current or former supervisor) in PDF format ONLY via BambooHR.

Any attachments must be in PDF format or will not be opened due to virus concerns. No phone calls after submission. We will let candidates know via automated reply that we have received their resumes and will contact them if there is a good fit after the closing date for this job.

From Possibility to Public Policy

Tyler Simko uses data science to make voting and education more fair

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As President of the Board of Education in his hometown of South Amboy, New Jersey, Tyler Simko negotiated the complexities of educational policy and budget allocations while engaging with stakeholders in his community. Elected to the board while still in his twenties, he discovered early on the power that local officials have to effect change in the lives of citizens.

“Serving on a school board has a much more direct impact than most academic work,” Simko says. “Academics often study things in the abstract and try to make generalizable arguments. That’s important work but it’s different from putting those insights into practice.”

Simko, who graduated last May with a PhD in government, combined research with action in his years at Harvard’s Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). Now, as then, his goal is to bridge the gap between possibility and public policy, making innovative use of computer algorithms and machine learning to address electoral issues like partisan gerrymandering as well as larger concerns like de facto racial segregation in public schools.

Sounding the ALARM on Gerrymandering

As a founder of the Algorithm-Assisted Redistricting Methodology (ALARM) Project, Simko collaborates with Professor of Government and of Statistics Kosuke Imai, PhD ’03—as well as students from Harvard’s graduate schools, Harvard College, and local high schools—to dissect gerrymandering, the partisan manipulation of electoral boundaries, through the use of powerful computational tools (see “Partisan Gerrymandering in Congressional Districts” model below). Some of the software the group has developed—including a package of tools called redist created in collaboration with Imai, Harvard Griffin GSAS student Christopher Kenny, alumnus Cory McCartan, PhD ’23, and research scientist Ben Fifield— is being used for research, litigation, and policy across the United States and in countries like Japan.

“We use the redist software to create alternative districts that follow state and federal requirements, like Idaho’s rule that congressional districts with multiple counties should connect based on the interstate highway system,” Simko says. “We then evaluate outliers by comparing the real, enacted plans to a distribution of simulated plans the state could have used.”

I use algorithmic tools to transparently evaluate many possible [redistricting] alternatives and characterize when and where policies can be effective.

These sampled plans can serve as a baseline for nonpartisan redistricting, says Professor Imai. “If the enacted plan favors one party, it serves as empirical evidence for partisan bias,” he explains. “We can use these algorithms and empirical evidence to help policymakers figure out the best policy.”

The ALARM team’s algorithm has already helped courts in states like Ohio and Pennsylvania decide whether enacted redistricting plans have a significant partisan bias. ALARM’s tools have also been used at the US Supreme Court level in the Alabama racial gerrymandering case Allen v. Milligan, which reaffirmed that a core portion of the Voting Rights Act can be applied legally to redistricting. In collaboration with Harvard Griffin GSAS student Emma Ebowe and Harvard College student Michael Zhao, the group is now studying reforms that could lead to fairer congressional districts.

“The ALARM group at Harvard has been a perfect way to combine my passion for policy impact with research,” Simko says.

Taking the LocalView

While doing work that has a national impact, Simko is still focused on the local level. He points out that local governments often have the greatest influence on the dayto-day lives of citizens. City councils and planning boards across the country have extensive power over issues like land use, education, and public health. But because power is so decentralized—and local journalism is vanishing—few largescale data sources on local policymaking are easily available to researchers, academics, journalists, and the public.

To bridge the information gap, Simko and Soubhik Barari, PhD ’23, launched LocalView, which uses computational tools to collect hundreds of thousands of meeting videos from local governments across the United States. “LocalView enables researchers to use the text, audio, and video data from these meetings to answer all kinds of important public policy questions,” Simko says. “Others, like journalists and the public, can use the data to understand what conversations are happening in communities across the country.”

In collaboration with Professor Rebecca Johnson of Georgetown University’s McCourt School of Public Policy, LocalView recently expanded to collect over 100,000 videos of school board meetings around the US. “We aim to increase transparency, informed decision-making, and real-world impact,” Simko says.

Taking on Segregation in Schools

Because national politics and legislation dominate the headlines, people are often surprised, Simko says, by how much influence local officials can have over their lives. “School boards approve the curriculum, set the budget, choose the textbooks, and negotiate union contracts with teachers and other district staff. They often have a low profile but are very powerful.”

In New Jersey, as in many states, the lines drawn on maps have far-reaching consequences for educational equity. While de jure segregation was outlawed after the US Supreme Court’s landmark Brown v. Board of Education decision in 1954, de facto racial segregation persists across the US, fueled by zoning patterns and residential choices (see “K-12 Public School Segregation in New Jersey,” page 20).

“New Jersey is one of the most racially and ethnically diverse states in the country, but residential segregation is also strong,” Simko says. “Like several other states, New Jersey generally divides school districts by town, which are often highly segregated. These boundaries also often divide homes that are more expensive from those that are more affordable.”

Simko cites the example of the small, affluent community of Glen Ridge, New Jersey. “There’s no legal requirement that low-income students can’t attend school in Glen Ridge, but low-income families are effectively priced out of living in town,” he observes.

The situation has given rise to debates about how US public schools draw their district lines. It’s also sparked lawsuits like Latino Action Network v. New Jersey, with plaintiffs claiming that the state has failed to remedy segregation caused by school districts and seeking to break the boundary lines in the name of equity. The challenge for those bringing suit, however, is to find alternative ways to draw the boundaries. “It’s not clear how districts could be redesigned to be more racially or socioeconomically integrated without increasing other constraints like student enrollment and travel time,” Simko says.

While it is difficult to imagine state officials using the algorithms directly to redraw school district lines, Tyler’s results make it vividly clear that it is politics and not practical considerations that stand in the way.

To address the challenge, Simko uses computational algorithms to redraw school districts according to different guidelines and then compares how each would change outcomes like racial, ethnic, and socioeconomic segregation. “For example, states could keep school district lines the same and reassign students to different schools,” Simko explains. “Or states could redraw the district lines entirely. States could even draw school districts at the county or regional level like they do in much of the South.”

Simko says it’s hard to know in advance which approach might work best in a particular setting because of logistic constraints—say, the number of existing schools—and the geographical distribution of students. “That’s why I use algorithmic tools to transparently evaluate many possible alternatives and characterize when and where policies can be effective,” he says.

Henry Lee Shattuck Professor of Education Martin West, PhD ’06, says that Simko’s algorithms reveal just how much progress could be made toward desegregating schools simply by redrawing district boundaries. “In New Jersey, racial segregation could be cut nearly in half even without requiring students to travel farther to school or the construction of new facilities,” he says. “While it is difficult to imagine state officials using the algorithms directly to redraw school district lines, Tyler’s results make it vividly clear that it is politics and not practical considerations that stand in the way.”

Simko says he and his colleagues are not proposing to simply turn the school districting process over to algorithms, no matter how well-intentioned their designers may be. “These tools are not meant to be prescriptive,” he says. “Complex policies like school assignments should ultimately be decided in conversation with stakeholders like district officials, families, and staff. However, these tools allow us to make it very clear what the possibilities are under different policies compared to where we are today.”

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