North Carolina A&T State University

Ph.D. in Computational Data Science and Engineering

We admit into our programs students with backgrounds from a great variety of disciplines ranging from the basic sciences (like physics and biology) to engineering, mathematics, and business and economics. Bring your passion for the computing-enabled pursuit of knowledge and acquire state-of-the-art skills that are necessary in the big-data, big-computation, and analytics era.

The Ph.D. program generally takes 4 years to complete. The duration may take longer depending on thesis/dissertation topic, writing, and research.

Degree Requirements

Post-BS Option

Total credit hours: 62

• Pass 12 credit hours as core courses: CSE 702, 703, 801, 804
• Pass 27 credit hours of elective courses from engineering, computer science, mathematics, physics, chemistry, biology, economics, business, agricultural science, or other courses approved by the CDSE Department, with approval of Advisor
• Pass the Doctoral Seminar (CSE 992: 1 credit hour) twice for a total of 2 credit hours.
• Take 15 credits of Dissertation-CSE 997
• Pass 6 additional credit hours to complete the 62 credit hour requirement. These credit hours can be from Dissertation-CSE 997, Continuation of Dissertation-CSE 999, Supervised Teaching-CSE 993, Supervised Research-CSE 994, or approved graduate-level courses, with approval of Advisor
• At least 26 credit hours should be 800-999
• Pass Qualifying Exam, Preliminary Exam, and Dissertation Defense
• Maintain appropriate GPA

Post-MS Option

Total credit hours: 44

• Pass 24 credit hours of elective courses from engineering, computer science, mathematics, physics, chemistry, biology, economics, business, agricultural science, or other courses approved by the CDSE Department, with approval of Advisor
• Pass the Doctoral Seminar (CSE 992: 1 credit hour) twice for a total of 2 credit hours.
• Take 15 credits of Dissertation-CSE 997
• Pass 3 additional credit hours to complete the 44 credit hour requirement. These credit hours can be from Dissertation-CSE 997, Continuation of Dissertation-CSE 999, Supervised Teaching-CSE 993, Supervised Research-CSE 994, or approved graduate-level courses, with approval of Advisor
• At least 26 credit hours should be 800-999
• Pass Qualifying Exam, Preliminary Exam, and Dissertation Defense
• Maintain appropriate GPA

Qualifying Written Examination Requirements

The successful Ph.D. candidate must pass a Qualifying Examination administered by the Department in the four core courses CSE 702, CSE 703, CSE 801, CSE 804.

Research and Dissertation Requirements

Major Advisor: Initially the Director of the Ph.D. program will serve as an Academic Advisor for all new students entering the Program. Each student in the Ph. D. Program is expected to select a Major Advisor by the beginning of the second year with the approval of the Department Chair. The Major Advisor must hold a tenure or tenure-track full-time faculty position at the university, and shall subsequently act as the Academic Advisor as well.

Composition of Ph.D. Committee: A Ph.D. Advisory Committee will consist of a minimum of five (5) graduate faculty with the Major Advisor as its chairperson. The Ph.D. Advisory Committee will be recommended by the Major Advisor, with input from the student, to the Chair of Computational Data Science and Engineering, for approval by the Dean of Graduate Studies. The Committee shall supervise the student’s Program, administer dissertation review and approval, and finally recommend the awarding of the degree.

Plan of Study: Upon the student’s selection of a research area, the Ph.D. Advisory Committee shall review the student’s prior transcripts, evaluate and recommend any transfer credits, and provide advice to the student. The student shall subsequently prepare a Plan of Study for approval by the Ph.D. Advisory Committee, the Chair of the CDSE Department, and the Dean of the School of Graduate Studies.

Oral Defense of Dissertation Proposal (Preliminary Examination): The dissertation proposal is submitted to the student’s Major Advisor and the Ph.D. Advisory Committee for review. The committee will make recommendations as needed. The proposal must be orally defended by the candidate before the Advisory Committee, and it must be approved by the Committee, and the student can proceed further with his/her research.

Candidacy for Ph.D. in Computational Data Science and Engineering: Admission to candidacy for Ph.D. in Computational Data Science and Engineering shall require compliance with all existing Graduate School policies, and shall occur after the student has successfully passed the Qualifying Examination and the Preliminary Examination.

Final Oral Examination: The final oral examination is scheduled after the dissertation is complete except for such revisions as may be necessary as a result of the examination, but not earlier than the semester or its equivalent after admission to candidacy, and not before all required courses work has been completed or is in progress.

Dissertation: The doctoral dissertation presents the results of the student’s original investigation in the field of major interest. It must be a contribution to knowledge, be adequately supported by data, and be written in a manner consistent with the highest standards of scholarship. Publication is expected.

Other Requirements

• Grade Point Average: The student must successfully complete the approved Plan of Study with a minimum cumulative GPA of 3.0 or better.
• Residency Requirements: For the Doctor of Philosophy (Ph.D.) degree, the student is expected to be registered for graduate work for at least four semesters beyond the Master of Science. At least two residence credits must be secured in continuous residence (registration in consecutive semesters) as a graduate student at the university.

The mission of the Department of Computational Data Science and Engineering is to graduate professionals who (a) have expertise in developing novel computational methodologies and products, and/or (b) have extended their expertise in specific disciplines (in science, technology, engineering, and socioeconomics) with computational tools.

• Graduates shall demonstrate expertise, critical thinking, and the ability to conduct research and development in scalable computing, computational methods, artificial and computational intelligence, complex system modeling and simulation, and data science and engineering.
• Graduates shall have mastery of communicating, planning, and implementing solutions and research and development products in computational approaches in various applications in science, technology, engineering, and mathematics, including the use of advanced visualization and analytics techniques.
• Graduates shall develop skills and abilities to be effective educators in computational and data science and engineering disciplines at the university level.
• Graduates shall demonstrate the ability to conduct novel and independent research and scholarly activity.