Ph.D in Business Data Science
Ph.D. in Business Data Science
Degree Requirements
Ph.D. students in Business Data Science (BDS) are expected to conduct innovative and independent research and have their research findings published in peer-reviewed scholarly journals and academic conference proceedings.
By the beginning of the first semester, upon the approval of the Ph.D. program director, student must have filed a Plan of Study (POS) that lists the courses to be taken and the timeline of study. Any modification to the POS must be approved by the Ph.D. program director and dissertation advisor (if chosen).
Coursework
Bridge Courses
Students who lack fundamental knowledge of certain subjects are required to complete assigned bridge courses by the end of year one, with a grade of at least a B in each assigned course. The assignment of bridge courses is based on recommendation and approval by the Ph.D. program director. Subjects and bridge course examples include:
- Programming and data structure (e.g. CS 280 or CS 505)
- Advanced Calculus (e.g. MATH 211)
- Probability and Statistics (e.g. MGMT 216 or MATH 333)
- Basic business knowledge (e.g. MGMT 492)
| Code | Title | Credits |
|---|---|---|
| Section I Core Courses | ||
| MGMT 682 | Business Research Methods I | 3 |
| MGMT 782 | Business Research Methods II | 3 |
| MGMT 635 | Data Mining and Analysis | 3 |
| or CS 634 | Data Mining | |
| CS 631 | Data Management System Design | 3 |
| or IS 631 | Enterprise Database Management | |
| Code | Title | Credits |
|---|---|---|
| Section II: Core Electives (At least two courses) | ||
| MGMT 735 | Deep Learning in Business | 3 |
| MRKT 766 | Seminar in Marketing Analytics | 3 |
| MGMT 740 | Innovation & Entrepreneurship | 3 |
| FIN 780 | Theory and Practice of Financial Research | 3 |
| Section III: Core Electives- MATH (At least one course) | ||
| MATH 660 | Introduction to statistical Computing with SAS and R | 3 |
| MATH 644 | Regression Analysis Methods | 3 |
| MATH 662 | Probability Distributions | 3 |
| MATH 678 | Statistical Methods in Data Science | 3 |
| MATH 680 | Advanced Statistical Learning | 3 |
| MATH 691 | Stochastic Processes with Applications | 3 |
| MATH 699 | Design and Analysis of Experiments | 3 |
| Section IV: Electives | ||
| BDS 725 | Independent Study I | 3 |
| BDS 726 | Independent Study II | 3 |
| ACCT 615 | Management Accounting | 3 |
| ECON 610 | Managerial Economics | 3 |
| HRM 601 | Managing Organizational Behavior in Technology-Based Organizations | 3 |
| HRM 630 | Managing Technological and Organizational Change | 3 |
| MGMT 620 | Strategic Management of Technological Innovation | 3 |
| MGMT 630 | Decision Analysis with Quantitative Modeling | 3 |
| MGMT 640 | New Venture Management | 3 |
| MGMT 641 | Global Project Management | 3 |
| MGMT 650 | Knowledge Management | 3 |
| MGMT 660 | Managing Supply and Value Chains | 3 |
| MGMT 670 | International Business | 3 |
| MGMT 680 | Entrepreneurial Strategy | 3 |
| MGMT 686 | Corporate Governance | 3 |
| MGMT 691 | Legal and Ethical Issues in a Digital World | 3 |
| MGMT 692 | Strategic Management | 3 |
| MIS 625 | Management Strategies for E-Commerce | 3 |
| MIS 645 | Information Technology and Competitive Advantage | 3 |
| MIS 648 | Decision Support Systems for Managers | 3 |
| MIS 680 | Management Science | 3 |
| MRKT 620 | Global Marketing Management | 3 |
| MRKT 631 | Marketing Research | 3 |
| MRKT 636 | Design and Development of High Technology Products | 3 |
| MRKT 638 | Sales Management for Technical Professionals | 3 |
| MRKT 645 | Digital Marketing Strategy | 3 |
| FIN 600 | Corporate Finance I | 3 |
| FIN 610 | Global Macro Economics | 3 |
| FIN 611 | Intro to Topics in Fin Tech | 3 |
| FIN 616 | Data Driven Financial Modeling | 3 |
| FIN 620 | Adv Financial Data Analytics | 3 |
| FIN 624 | Corporate Finance II | 3 |
| FIN 626 | Financial Investment Institutions | 3 |
| FIN 627 | International Finance | 3 |
| FIN 634 | Mergers, Acquisitions, and Restructuring | 3 |
| FIN 641 | Derivatives Markets | 3 |
| FIN 650 | Investment Analysis and Portfolio Theory | 3 |
| CS 610 | Data Structures and Algorithms | 3 |
| CS 644 | Introduction to Big Data | 3 |
| DS 675 | Machine Learning | 3 |
| DS 677 | Deep Learning | 3 |
| CS 732 | Advanced Machine Learning | 3 |
| CS 782 | Pattern Recognition and Applications | 3 |
| CS 786 | Seminar in Computer Science II | 3 |
| ECE 744 | Optimization for Data Engineering | 3 |
| ECE 788 | Selected Topics in Electrical and Computer Engineering | 3 |
| IS 650 | Data Visualization and Interpretation | 3 |
| IS 657 | Spatiotemporal Urban Analytics | 3 |
| IS 661 | User Experience Design | 3 |
| IS 665 | Data Analytics for Info System | 3 |
| IS 684 | Business Process Innovation | 3 |
| IS 688 | Web Mining | 3 |
| IS 698 | Special topics in Information Systems | 3 |
| IS 735 | Social Media | 3 |
| EM 602 | Management Science | 3 |
| EM 640 | Distribution Logistics | 3 |
| IE 621 | Systems Analysis and Simulation | 3 |
| IE 650 | Advanced Topics in Operations Research | 3 |
| IE 673 | Total Quality Management | 3 |
| IE 659 | Supply Chain Engineering | 3 |
A student entering the program with only a Bachelor’s degree in related areas shall take 36 credits of advanced courses (600-level and 700-level) beyond the Bachelor’s degree with the approval by the Ph.D. program director. The 36 credits shall include core and elective courses, in addition to the credits for dissertation research. Among the 36 credits, at least 12 credits must be 700-level courses.
A student entering the program with a Master’s degree or above in the related areas shall take 21 credits of advanced courses (600-level and 700-level) or equivalent with the approval by the Ph.D. program director. Students with strong credentials in business and/or data science and with a Master’s degree may be approved to take 18 credits of advanced courses, subject to the approval by the Ph.D. committee. At least 12 credits must be 700-level courses.
The required course credits listed above are those in addition to the credits for dissertation research (BDS 792B and BDS 790A).
GPA
Students must maintain a cumulative GPA of 3.0 or higher. As per current NJIT policy, students receiving financial support, as assistantship and fellowship, for the first time must have a cumulative GPA of 3.5 or higher. To continue receiving support, they must maintain a cumulative GPA of at least 3.0
Qualifying Exams
All Ph.D. students are required to take Core Course Qualifying Exams by the end of year one and must pass the Core Course Qualifying Exams by the end of year two. The Core Course Qualifying Exams covers subject matter drawn from the core courses.
All Ph.D. students are required to take Subject Qualifying Exam by the end of year two. Each Subject Qualifying Exam covers a subject area based on the student’s research interest.
Dissertation Requirements
Registration
In addition to the required course credits listed above, students must meet Ph.D. dissertation requirements. Students must register BDS 792B for dissertation proposal and BDS 790A for dissertation. The requirement of BDS 792B and BDS 790A credits are described at: http://www5.njit.edu/graduatestudies/content/new-phd-credit-requirements/ and https://catalog.njit.edu/graduate/academic-policies-procedures/.
Dissertation Advisor
Students are recommended to choose a dissertation advisor as soon as possible, but no later than 3 months after passing the Core Course Qualifying Exams.
Dissertation Proposal Defense
The dissertation proposal must be defended in a public forum successfully either by the end of the third year in the Ph.D. program or four semesters after registering for the first time in the 792 pre-doctoral research course, whichever occurs earlier.
Dissertation Defense
PhD students must defend the dissertation successfully by the end of the sixth year in the Ph.D. program.
Please refer to the following website for other institution-wide policies and procedures for Ph.D. programs: https://catalog.njit.edu/graduate/academic-policies-procedures/
Other Requirements
Ph.D. students are required to register each semester for a 0-credit course: BDS 791 Doctoral Seminar. Full-time students must attend BDS 791 seminars each semester unless justifiable reasons are approved by the program director in advance. Part-time students are expected to attend at least 50% of the BDS 791 seminars in their first year. They may be asked to perform alternative work assigned by the program director in lieu of attending seminars.
In their first year, Ph.D. students are required to take a 0-credit course: INTD 799 Responsible Conduct of Research and receive a Satisfactory grade.
