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Ph.D. in Data Science

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Program Overview

The Rowan Experience
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The Ph.D. in Data Science program will provide the essential skills required to analyze big and complex data sets and equip students with a broad understanding of data challenges and opportunities, along with the research and inquiry skills necessary to independently conduct research and answer questions within their area of concentration.  

To meet this goal, courses in the Ph.D. in Data Science Program curriculum are organized around interdisciplinary focal areas in computer science, engineering, mathematics, and statistics. Courses offered within this framework include traditional lecture-style, e-learning, and special topics courses that introduce students to the latest theories, methods, and emerging issues; seminar series; and experiential learning through thesis research, (directed independent study and internship programs). Through this framework, students will gain proficiency in the application of scientific principles such as, critical thinking, experimental design, data preprocessing and wrangling, data visualization, advanced statistical learning/data mining and machine learning, as well as a sense of professional and technical writing, and reporting, responsibility, and integrity.

Curriculum
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Students possessing a bachelor's degree will be required to complete a minimum of 72 semester hours of graduate-level work. Students possessing a master’s degree in a related field will be required to complete a minimum of 42 semester hours of graduate-level work beyond their master's degree in addition to meeting other Ph.D. requirements. Up to 30 of the credits earned in pursuit of your master's degree may be transferable to the Ph.D. program as either core courses or elective courses.

To maintain Minimum Satisfactory Academic Progress in and to successfully graduate from this program, students must:

  • Earn no more than two total C grades of any combination of “C+” or “C.” (C- grades are not acceptable.)
  • Earn no grades lower than a “C”
  • Earn an official cumulative GPA (according to matriculation level) of at least 3.000 on Rowan’s 4.000 scale
  • Successful completion of Qualifying Exam & Successful defense of research thesis dissertation.

 

The following courses make up the Ph.D. in Data Science.

 

Course Number Title S.H. (Credits)
Required Courses: 21 S.H.
CS 02516 Big Data Tools and Techniques 3
MATH 01505 Probability and Mathematical Statistics I 3
STAT 02515 Applied Multivariate Data Analysis 3
  Take one of the following:  
CS 07556 Machine Learning I 3
ECE 09555 Advanced Topics in Pattern Recognition 3
  Take one of the following:  
MATH 03511 Operations Research I 3
ENGR 01511 Engineering Optimization 3
  General Coursework (both required)  
XEED 01601 Effective Teaching in Academic, Corporate and Gov’t Settings 3
ECE 09702 Strategic Technical Writing and Winning Grant Proposals 3
Elective Courses: 21-30 S.H.
CS 02505 Data Mining I 3
CS 02530 Advanced Database Systems: Theory and Programming 3
CS 02605 Data Mining II 3
CS 02620 Data Warehousing 3
CS 02625 Data Quality and Web/Text Mining 3
CS 02630 Advanced Topics in Database Systems 3
CS 07540 Advanced Design and Analysis of Algorithms 3
DS 02510 Visual Analytics 3
DS 02695 Advanced Topics in Data Science 3
ECE 09558 Reinforcement Learning 3
ECE 09560 Artificial Neural Networks 3
ECE 09566 Advanced Topics in Systems, Devices, and Algorithms in  Bioinformatics 3
ECE 09568 Discrete Event Systems 3
ECE 09585 Advanced Engineering Cybersecurity 3
ECE 09586 Advanced Portable Platform Development 3
ECE 09595 Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining 3
ECE 09655 Advanced Computational Intelligence and Machine Learning 3
MATH 01506 Probability and Mathematical Statistics II 3
STAT 02510 Introduction to Statistical Data Analysis 3
STAT 02511 Statistical Computing 3
STAT 02514 Decision Analysis 3
STAT 02525 Design and Analysis of Experiments 3
STAT 02530 Applied Survival Analysis 3
STAT 02585 Introduction to Bayesian Statistical Methods 3
Thesis Coursework: 21-30 S.H.
DS 02799 Doctoral Research and Dissertation  3

 

Note: The courses listed above are not official and are subject to change. For an official list of available courses please visit the Rowan Global section tally.

Transfer Credit Evaluation Policy

Students seeking a Ph.D. in Data Science should have a Bachelor's degree in Mathematics, Statistics, Computer Science, or related field from an accredited institution of higher learning with a minimum undergraduate cumulative GPA of 3.0 (on a 4.0 scale). Depending on the undergraduate area of study, completion of additional foundation courses may be required. Students who do not meet the entrance requirements for the Ph.D. program may be admitted to the masters program. Upon successful completion of the master’s degree, they can transfer into the Ph.D. program and apply master’s courses to the Ph.D. program. All master's coursework will be eligible to transfer. Often with graduate programs, students who are in a Master’s Thesis track can convert to a Ph.D. track. The checkpoint we will create will occur at the end of the spring semester of their first Master’s year. If a student has a cumulative MS GPA of 3.3 with no grade lower than a B, they will be allowed to transfer to the PhD. If not, they may successfully complete the MS and then be allowed to transfer into the PhD program, completing the remaining coursework and graduation requirements.

Admission Requirements
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The following is a list of items required to begin the application process for the program. There may be additional action or materials required for admission to the program. Upon receipt of the materials below, a representative from the Rowan Global Admissions Processing Office will contact you with confirmation or will indicate any missing items.

 

  • Completed Application Form
  • $65 (U.S.) non-refundable application fee
  • Bachelor’s degree (or its equivalent) in Mathematics, Statistics, Computer Science, Computational Science, Data Science or related field from an accredited institution of higher learning.  Applicant should have taken courses in Probability & Statistics, Data Structures, Multivariate Calculus, and Linear Algebra and should have proficiency in programming languages commonly used in data science, such as Python, R, and/or SQL.
  • Official transcripts from all colleges attended (regardless of number of credits earned)
  • Current professional resume. Applicants should include a statement on the professional resume that verifies evidence of applied skills including research proficiency.
  • Typewritten statement of professional objectives and research interests
  • Three letters of recommendation. Applicants with Master's degrees completed within the past 5 years should include as one of their recommenders an instructor (or MS thesis advisor) from their Master's program.
  • Students recommended by a Rowan Thesis advisor who is willing to do research with the student may waive one or more of the requirements above.
  • Students who do not meet the entrance requirements for the Ph.D. program may be admitted into the Master’s program. Upon completion of the Master’s degree, exceptional candidates can apply to transfer into the Ph.D. program. Students successfully transferred into Ph.D. program will be able to count all their Master’s courses towards satisfying the Ph.D. program requirements. 
  • Minimum undergraduate cumulative GPA of 3.5 (on a 4.0 scale)
  • Graduate committee may conduct interviews with shortlisted candidates to further assess their suitability for the Ph.D. program
We invite you to join us for a

Virtual Information Session or Event

Rowan University hosts a series of on campus and virtual events throughout the year to help you get to know us. From general information sessions, to program specific meetings with advisors, to webinars on timely and thought-provoking topics, these gatherings provide an excellent opportunity to ask questions, network with academics and future students, and learn from some of the most innovative and informed faculty in higher education today.
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