Information Technology Concentration
Ph.D. in Management
Offered by the Department of Management Science and Information Systems, this program is closely associated with the Center for Information Management, Integration and Connectivity.
Students who aspire to doctoral study in information technology but need to strengthen their background may wish to consider our Master of Information Technology and Analytics program, which admits both part-time and full-time students. Students in this program take many of the same courses as students in the doctoral program and may use these courses towards their doctoral degree if they are later admitted to the doctoral program.
Characteristics of students most likely to be admitted:
- Students are expected to have basic knowledge in calculus, probability, statistics, linear algebra, and computer science.
- Most students admitted in recent years come with a master’s degree in computer science, information technology, or industrial engineering.
Requirements
Seminar Series
Doctoral students in Information Technology who have not yet defended a dissertation proposal are expected to attend the seminar series of the Center for Information Management, Integration and Connectivity, and each semester they receive a grade on their transcript based on their attendance and participation. Applicants and potential applicants are also welcome at the seminar.
Additional enrollments may be required:
- Students are sometimes required to enroll in non-degree courses to improve their English or their writing. They may also need to enroll in the non-degree course Teacher Training Seminar as part of their preparation for teaching. These enrollments require payment of tuition, but they do not count towards the 72 credits required for the degree.
- Students must enroll in 26:198:689 every semester until they have defended a dissertation proposal. This registration requires their attendance in Management Science and Information Systems department's weekly seminar. A grade is given, but the enrollment is for zero credits and no tuition is charged.
During the first two years, students are expected to take at least three courses for degree credit each semester. They should then take the qualifying examination in May at the end of their second academic year. The last two years of the program should be devoted primarily to completing the dissertation, but students may be advised to take some additional courses. For more details concerning rules and requirements that apply to all RBS doctoral students, see Policies and Procedures.
Course Information
Foundation/methodology requirement (4 courses)
These courses should be selected, in consultation with the adviser, from master’s level courses in information technology or computer science (taught at NJIT or at Rutgers-New Brunswick). In some cases, courses already taken by the student in the course of his or her master’s study may be transferred for credit to meet part of this requirement. However, students who have not yet studied Probability at the level of 26:960:575 should take it as one of their three minor courses.
Major (5 courses)
Of the five major courses:
- 26:198:641 Advanced Database Systems
- 26:198:643 Information Systems Security
- Three additional courses approved by adviser, departmental coordinator, and doctoral director
Minor (3 courses)
- 26:960:577 Statistical Linear Models
- Two other courses.
It is strongly recommended that students include the following courses in their study plan:
- 26:198:644 Data Mining
- 26:198:645 Privacy, Security, and Data Analysis
First early research requirement (equivalent to one course): Students write a paper with a faculty member, to be presented to the department during the fall semester.
Second early research requirement (equivalent to one course): Write a paper (ideally a dissertation proposal) with a faculty member, to be presented to the department during the fall semester.
Other rules and requirements: For details of rules and requirements that apply to all doctoral students in RBS, see Policies and Procedures.
Course Descriptions
26:198:621 - Electronic Commerce
This course will cover the theoretical foundations, implementation problems and research issues of the emerging area of electronic commerce. It will discuss technological, conceptual, and methodological aspects of electronic commerce. The list of topics to be covered in this course includes: fundamentals of Internet technology, pricing of and accounting for Internet transport, security problems of the Internet, electronic payment systems, online financial reporting and auditing, intelligent agents, web measurements, electronic markets and value chain over the Internet. The coursework will include presentations of research articles, in-class discussions, and a final course project researching one of the problems of electronic commerce. Prerequisite: basic computer literacy, introductory courses in computer information systems and economics.
- Spring 2017 syllabus by Professor Alexander Kogan
26:198:622 - Machine Learning
Every spring.
- Spring 2018 syllabus by Professor Alexander Kogan
26:198:641 - Advanced Database Systems
Every fall.
Emphasizes the functions of database administrator. Includes survey of physical and logical organization of data and their methods of accessing, and the characteristics of different models of generalized database management systems.
Prerequisite: A master's-level course in databases such as 22:198:603 or NJIT CIS 631.
- Fall 2022 syllabus by Professor Joann J. Ordille
26:198:642 - Multimedia Information Systems
Fall 2009 and every second fall thereafter.
This course covers principal topics related to multimedia information systems. These include organizing multimedia content, physical storage and retrieval of multimedia data, content-based search and retrieval, creating and delivering networked and multimedia presentations, and current research directions in this area. Prerequisite: A master's-level course in databases such as 22:198:603 or NJIT CIS 631.
- Fall 2015 syllabus by Professor Vijay Atluri
26:198:643 Information Security
Fall 2008 and every second fall thereafter.
- Fall 2018 syllabus by Professor Vijay Atluri
26:198:644 - Data Mining
Every spring.
The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection.
- Fall 2018 syllabus by Meng Qu
26:198:645 - Data Privacy
Fall 2009 and every second fall thereafter.
- Spring 2018 syllabus by Jaideep Vaidya
26:198:685 - Special Topics in Information Systems
Data Structures and Algorithms
- Fall 2018 syllabus by Professor Farid Alizadeh
Big Data: Data-Intensive Analytics
- Spring 2014 syllabus by Professor Nabil Adam
Applications of Machine Learning to Big Data
- Spring 2017 syllabus by Professor Nabil Adam
- 26:198:686 First Early Research Seminar in Information Systems
- 26:198:687 Second Early Research Seminar in Information Systems
- 26:198:688 Independent Study in Information Systems
- 26:198:799 Dissertation Research in Information Systems
Please note: Links to recent syllabi are provided where possible. In some cases, the link goes to the web site for the individual faculty member, where the syllabus is maintained. In other cases, the link allows you to download the syllabus. Other syllabi are available in the Program Office.
These syllabi are provided as information to potential applicants. They should also help current students make their individual study plans. But they are subject to change. Students should not buy books or make other plans related to a course until they have confirmed with the instructor that they have an up-to-date syllabus for the semester in which they are taking the course.
Ph.D. Executive Committee, January 2019