Ph.D. in Data Science - Computing Track:
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 Ph.D. director) from a college or university accredited in the United States, or its equivalent, with a minimum overall GPA of 3.5 out of 4.0.
- GRE scores are required.
- International student applicants shall demonstrate proficiency in English, if English is not their first language, following the NJIT admission standard. Applicants should refer to NJIT’s published international graduate admissions information for current English proficiency requirements and exemption criteria.
- Prepared students shall have a good background in programming and data structures (e.g., NJIT CS 280 Programming Language Concepts and CS 435 Advanced Data Structures and Algorithm Design), advanced Calculus (e.g., NJIT MATH 211 Calculus III A), and Probability and Statistics (e.g., MATH 333 Probability and Statistics/MATH 341 Statistical Methods II). Admitted students lacking competencies in one or more of these areas shall consult with the Ph.D. program director to take relevant preparatory courses. Students might be required to enroll in a relevant Certificate Program at NJIT and will only be admitted with a GPA of 3.0 or higher in the Certificate Program.
II.1 Course Requirements
The courses include core courses, elective courses, and courses for conducting research. The definition of “core courses” in this document is that they are offered by the Department of Data Science or the Department of Mathematical Sciences and are considered especially relevant to Data Science and are recommended to students as such. In addition to the listed elective courses, a student may take other special topic courses, at most two of which can be counted as electives, subject to the approval of the Ph.D. program director.
Course descriptions for the core courses and elective courses are listed below. Courses listed are offered by the Ying Wu College of Computing (YWCC), the Jordan Hu College of Science and Liberal Arts (JHCSLA), and the Newark College of Engineering. These colleges are happy to collaborate with DS/MATH, providing regular course offerings and accommodating the Ph.D. students in the Data Science program.
Courses for conducting research include: DS 790A Doctoral Dissertation & Research; DS 791 Graduate Seminar; DS 792B Pre-Doctoral Research, described below:
DS 790A Doctoral Dissertation & Research
PhD students who successfully defend the dissertation proposal must then register for the one-credit dissertation course each semester until they complete all the degree requirements.
DS 791 Graduate Seminar
Ph.D. students are required to register each semester for a zero-credit Graduate Seminar. Attendance and participation in the Seminar are required of all students.
DS 792B Pre-Doctoral Research
Ph.D. students who pass the Qualifying Exam must then register for 3 credits of pre-doctoral research per semester until they successfully defend the dissertation proposal.
Students may take courses simultaneously with the DS 791 Graduate Seminar or DS 792B Pre-Doctoral Research course as per Ph.D. program director and dissertation advisor/committee recommendation.
Computing Track:
- Students who start the program with a recognized Master’s degree in Data Science or a related area, e.g., Computer Science, Information Science and Technology, etc., are required to take two 3-credit courses (6 credits) at the 600 level and four 3-credit courses (12 credits) at the 700 level.
- Students who start the program with a recognized Baccalaureate degree are required to take eight 3-credit courses (24 credits) at either the 600 level or 700 level, as well as four additional 700-level 3-credit courses (12 credits), for a total of twelve 3-credit courses (36 credits).
- All students must choose 18 credits of the required courses from sections designated as doctoral sections. A doctoral section is a section of a graduate-level course designated as such by the PhD committee. 700-level courses are always automatically considered doctoral sections.
- At most 6 credits can be taken as Independent Study in Data Science (DS 725 Independent Study I and/or DS 726 Independent Study II). If a student takes both Independent Study courses, then they should be taken with two different professors. At least 6 credits must be in lecture-based courses at the 700 level. Under rare circumstances, the student’s research advisor and dissertation committee might ask the student to take additional courses, usually before the Qualifying Exam, but possibly also after the Qualifying Exam.
- Master’s project (course DS 700B Master's Project), Master’s thesis (course DS 701B Master's Thesis), or more than two independent study courses (courses DS 725 Independent Study I and DS 726 Independent Study II ) cannot be used to satisfy these coursework requirements.
- Students will be required to take DS 675 Machine Learning or DS 644 Introduction to Big Data, and also MATH 644 Regression Analysis Methods.
- All required courses can be substituted by courses of equal difficulty, if the Ph.D. advisor and the Ph.D. directors in both tracks agree to them in writing. For example, if a student has already taken an equivalent course to a required course, then a substitute will be determined.
- A student with an interest in Machine Learning or related areas may choose elective courses such as CS 732 Advanced Machine Learning, DS 789 Trustworthy Artificial Intelligence, etc. Potential research topics may include, but are not limited to, algorithm development for clustering, dimensionality reduction, reinforcement learning, and machine learning in Natural Language Processing.
- A student with an interest in Statistics may choose MATH 787 Non-Parametric Statistics, MATH 786 Large Sample Theory and Inference. Potential research topics may include, but are not limited to, machine learning, uncertainty quantification, statistical learning, and data mining.
- A student with an interest in Data Visualization may choose DS 650 Data Visualization and Interpretation. Potential research topics may include, but are not limited to, visualization techniques for explainable AI, visual analytics for human-machine trust, and communicative visualization design.
- A student with an interest in High Performance Computing may choose DS 642 Applications of Parallel Computing, CS 668 Parallel Algorithms, CS 750 High Performance Computing, etc. Potential research topics may include, but are not limited to, Real-world algorithms, Numerical computing, Scalable Systems, High Performance Data Analytics, Modeling & Simulation
II.2 Other Requirements
Students are expected to have their research findings published in high-quality peer-reviewed academic conference proceedings and journals at a volume that is considered the established standard in their subfield of Data Science.
Full-time students are also required to attend and participate in Data Science research seminars every semester and are encouraged to attend other research seminars across campus. Full-time PhD students in the Computing Track are required to attend 2/3 of all the in-person or online, and ½ of all weekly departmental Wednesday seminars in person. Seminar attendance will be monitored and recorded by the Ph.D. program director. Students in the Computing Track should attend research seminars in YWCC. Students in the Statistics Track should attend research seminars in the Department of Mathematical Sciences. Students in both concentrations should attend all Data Science-related seminars.
To continue in the PhD program, a student must fulfill the following requirements/milestones. Failure to satisfy these requirements may result in probation or dismissal from the program:
Computing Track
- Students must maintain a Cumulative GPA of at least 3.5 at all times after completing nine (9) credits, i.e., three courses of three credit hours each.
- To qualify as a PhD candidate, a student’s research potential is assessed through a Qualifying Exam, which must be completed within two years (24 months) from the time the student starts the Ph.D. program. The Qualifying Exam should be taken at the end of the third semester. A student failing the qualifying exam may retake it one time at the end of the fourth semester.
The Qualifying Exam evaluates the student's ability to conduct research supervised by their advisor, including literature review, problem formulation, solution development, and evaluation, demonstrating technical ability and oral and written communication skills, consisting of two components: 1) Written Research Report, 2) Oral Research Presentation. The presentation must be based on the written research report.
The faculty research advisor will propose a Qualifying Exam Committee (QEC) of three tenure/tenure-track NJIT faculty members, at least two of whom have their primary appointment in Data Science. The QEC members should have research experience or should be developing research interests related to the student’s research topic. The faculty research advisor(s) cannot be a member of the QEC.
The Oral Research Presentation will have a public portion of no more than 45 minutes (excluding questions) followed by a closed session with only the student and the QEC (the research advisor(s) cannot be present during the closed session). Each QEC member will evaluate the oral presentation and the written report. This evaluation includes the technical background knowledge about any topic that the student should have mastered after three semesters of Data Science courses (although such questions may or may not be asked during the presentation). Each QEC member will assign one of the following grades: Pass, Conditional Pass, or Fail.
● At least two Passes and no Fails are required for passing the Qual Exam.
● One or more Fails result in failing the Qualifying Exam.
● If the student did not Pass or Fail as described above, the student is considered to have passed the Qualifying Exam conditionally. The QEC must provide a written list of change requests as part of the Qualifying Exam result. The student will have at most four weeks from the time that the student receives the written change requests to submit a revised report and a written summary of all the changes made.
The QEC evaluates the revised report accompanied by the written summary and the QEC will report the final Pass or Fail decision after at most one week. The student will pass after successfully addressing the change requests. Failure to address the change request list on time results in failing the Qualifying Exam.
The student will be allowed at most two chances to take the Qualifying Exam. Normally, it is expected that the QEC composition remains the same for a retake. However, with the research advisor’s justification, a new committee can be appointed for a retake, which must have at least one member in common with the previous committee. When scheduling a retake, the research advisor must provide the previous decision letter to the new QEC.
- A student who does not pass the Qual Exam before the end of the second year (24 months from the time the student starts the Ph.D. program) will be referred to the PhD committee to be considered for dismissal from the program.
- Students must contact their Ph.D. Program Director to get details regarding the Qual Exam.
- To prepare for the Qualifying Exam, students are recommended to choose a research advisor as soon as possible, but no later than the beginning of the second semester. In most cases, the research advisor will become the dissertation advisor.
- If a Ph.D. student wishes to switch research supervisor or dissertation advisor at any time after the first semester, the student has to identify a new research supervisor or dissertation advisor before the end of the same semester, in which the student left the previous research supervisor or dissertation advisor.
- Students are recommended to choose a dissertation advisor as soon as possible, but no later than 3 months after passing the Qualifying Exam.
- Any change to the program of study must be approved by the Ph.D. program director and the dissertation advisor (if chosen).
- End of year three: the student must have a dissertation committee established and the dissertation proposal must be successfully defended.
- The dissertation should be presented in writing and should be orally defended by the end of the fourth or fifth year, and must be defended at the latest by the end of the sixth year in the PhD program. Students who cannot defend their dissertation by the end of the sixth year will be dismissed from the program.
PhD Student Semi-annual Evaluation
- The student’s progress on program requirements and research is assessed by the departmental PhD Committee each semester.
Student Standing and Dismissal
- If a student fails to satisfy any of the program’s requirements, then they may be dismissed from the program. All decisions related to a student’s standing in the program are made by the PhD committee in consultation with the student’s research advisor, and are communicated to the student.
Ph.D in Data Science- Computing (Students with MS Degree)
| Code | Title | Credits |
|---|---|---|
| Must complete two 3-credit courses from the following: | ||
| DS 675 | Machine Learning | 3 |
| or DS 644 | Introduction to Big Data | |
| MATH 644 | Regression Analysis Methods * | 3 |
| Complete four 3-credit courses at the 700 level from the following: | ||
At least 6 credits must be at the 700 level in lecture-based courses from the list below: | ||
| DS 732 | Theoretical Foundation of Machine Learning | 3 |
| DS 733 | Deep Unsupervised Learning (Formerly DS 633) | 3 |
| DS 752 | Human-AI Collaborative Sensemaking | 3 |
| DS 786 | Selected Topics in Data Science | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| CS 703 | Computational Optimization | 3 |
| CS 704 | Sequencing and Scheduling | 3 |
| CS 708 | Advanced Data Security and Privacy | 3 |
| CS 731 | Applications of Database Systems | 3 |
| CS 732 | Advanced Machine Learning | 3 |
| CS 735 | Advanced Programming Languages | 3 |
| CS 750 | High Performance Computing | 3 |
| CS 756 | Mobile and Ubiquitous Computing | 3 |
| MATH 707 | Advanced Applied Mathematics IV: Special Topics | 3 |
| MATH 717 | Inverse Problems and Global Optimization | 3 |
| MATH 761 | Statistical Reliability Theory and Applications | 3 |
| MATH 763 | Generalized Linear Models | 3 |
| MATH 768 | Probability Theory | 3 |
| MATH 786 | Large Sample Theory and Inference | 3 |
| MATH 787 | Non-Parametric Statistics | 3 |
| CHEM 714 | Pharmaceutical Analysis | 3 |
| At most 6 credits from | ||
| DS 725 | Independent Study I | 3 |
| DS 726 | Independent Study II | 3 |
| Term Credits | 18 | |
*Students with sufficient prerequisite knowledge covering the topics in MATH 661 Applied Statistics should apply to the Math department for a permit here to register for MATH 644 Regression Analysis Methods.
Ph.D in Data Science- Computing (Students with Baccalaureate Degree BA/BS)
| Code | Title | Credits |
|---|---|---|
| Must complete three 3-credit courses from the following: | ||
| DS 675 | Machine Learning | 3 |
| DS 644 | Introduction to Big Data | 3 |
| MATH 644 | Regression Analysis Methods * | 3 |
| Complete five additional 3-credit courses at either the 600 or 700 level from the following: | 24 | |
| DS 636 | Data Analytics with R Program | 3 |
| DS 642 | Applications of Parallel Computing | 3 |
| DS 650 | Data Visualization and Interpretation | 3 |
| DS 677 | Deep Learning | 3 |
| DS 680 | Natural Language Processing | 3 |
| CS 602 | Java Programming | 3 |
| CS 608 | Cryptography and Security | 3 |
| CS 610 | Data Structures and Algorithms | 3 |
| CS 630 | Operating System Design | 3 |
| CS 631 | Data Management System Design | 3 |
| CS 634 | Data Mining | 3 |
| CS 643 | Cloud Computing | 3 |
| CS 645 | Security and Privacy in Computer Systems | 3 |
| CS 647 | Counter Hacking Techniques | 3 |
| CS 648 | Cyber Sec Investigations & Law | 3 |
| CS 656 | Internet and Higher-Layer Protocols | 3 |
| CS 670 | Artificial Intelligence | 3 |
| MATH 611 | Numerical Methods for Computation | 3 |
| MATH 631 | Linear Algebra | 3 |
| MATH 659 | Survival Analysis | 3 |
| MATH 660 | Introduction to Statistical Computing | 3 |
| MATH 662 | Probability Distributions | 3 |
| MATH 665 | Statistical Inference | 3 |
| MATH 678 | Statistical Methods in Data Science | 3 |
| MATH 680 | Advanced Statistical Learning | 3 |
| MATH 683 | High Dimensional Stat Inferenc | 3 |
| MATH 691 | Stochastic Processes with Applications | 3 |
| MATH 699 | Design and Analysis of Experiments | 3 |
| ECE 601 | Linear Systems | 3 |
| ECE 673 | Random Signal and Data Analysis | 3 |
| IE 650 | Advanced Topics in Operations Research | 3 |
| IE 687 | Healthcare Enterprise Systems | 3 |
| IE 688 | Healthcare Sys Perfor Modeling | 3 |
| IT 696 | Network Management and Security | 3 |
| IS 634 | Information Retrieval | 3 |
| IS 665 | Data Analytics for Info System | 3 |
| IS 682 | Forensic Auditing for Computing Security | 3 |
| IS 684 | Business Process Innovation | 3 |
| IS 688 | Web Mining | 3 |
| PHYS 611 | Adv Classical Mechanics | 3 |
| PHYS 621 | Classical Electrodynamic | 3 |
| PHYS 641 | Statistical Mechanics | 3 |
| CHEM 658 | Advanced Physical Chemistry | 3 |
| ME 616 | Matrix Methods in Mechanical Engineering | 3 |
| ME 625 | Introduction to Robotics | 3 |
| CE 611 | Project Planning and Control | 3 |
| Complete four 3-credit courses at the 700 level from the following: | 12 | |
At least 6 credits must be at the 700 level in lecture-based courses from the list below: | ||
| DS 732 | Theoretical Foundation of Machine Learning | 3 |
| DS 733 | Deep Unsupervised Learning (Formerly DS 633) | 3 |
| DS 752 | Human-AI Collaborative Sensemaking | 3 |
| DS 786 | Selected Topics in Data Science | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| CS 703 | Computational Optimization | 3 |
| CS 704 | Sequencing and Scheduling | 3 |
| CS 708 | Advanced Data Security and Privacy | 3 |
| CS 731 | Applications of Database Systems | 3 |
| CS 732 | Advanced Machine Learning | 3 |
| CS 735 | Advanced Programming Languages | 3 |
| CS 750 | High Performance Computing | 3 |
| CS 756 | Mobile and Ubiquitous Computing | 3 |
| MATH 707 | Advanced Applied Mathematics IV: Special Topics | 3 |
| MATH 717 | Inverse Problems and Global Optimization | 3 |
| MATH 761 | Statistical Reliability Theory and Applications | 3 |
| MATH 763 | Generalized Linear Models | 3 |
| MATH 768 | Probability Theory | 3 |
| MATH 786 | Large Sample Theory and Inference | 3 |
| MATH 787 | Non-Parametric Statistics | 3 |
| CHEM 714 | Pharmaceutical Analysis | 3 |
| At most 6 credits from | ||
| DS 725 | Independent Study I | 3 |
| DS 726 | Independent Study II | 3 |
| Total Credits | 36 | |
*Students with sufficient prerequisite knowledge covering the topics in MATH 661 Applied Statistics should apply to the Math department for a permit here to register for MATH 644 Regression Analysis Methods.
