Ph.D. in Data Science - Statistics Track

The Ph.D. in Data Science - Statistics Track is a comprehensive program designed to equip students with advanced knowledge and skills in data science, with a focus on statistical methods and techniques.

Admission Requirements

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 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.
• Prepared students shall have a good background in programming and data structures, advanced Calculus, and Probability and Statistics.

Course Requirements

The program includes core courses, elective courses, and courses for conducting research. All core courses are listed in Table DR-1. 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.

• 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 - Doctoral Seminar: Ph.D. students are required to register each semester for a zero-credit Graduate Seminar.
• 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.

Statistics Track

Ph.D. students with a recognized Baccalaureate degree are required to take ten 600-level or 700-level 3-credit courses (30 credits) of coursework beyond the Baccalaureate degree as well as four additional 700-level 3-credit courses (12 credits), for a total of fourteen 3-credit courses (42 credits).

• Students will be required to take DS 675 (Machine Learning), MATH 644 (Regression), and MATH 631 (Linear Algebra).
• 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.

Computing Track

Students who start the program with a recognized Master's degree in Data Science or a related area 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.

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.

Qualifying Exam

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.

• The Qualifying Exam consists of two components: 1) Written Research Report, 2) Oral Research Presentation.
• The faculty research advisor will propose a Qualifying Exam Committee (QEC) of three tenure/tenure-track faculty members, at least two of whom have their primary appointment in Data Science.

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.

Course List

The following courses are available:

Core Courses

• DS 675: Machine Learning
• DS 644: Introduction to Big Data
• DS 636: Data Analytics with R Programming
• DS 677: Deep Learning
• DS 642: Applications of Parallel Computing
• DS 650: Data Visualization
• DS 680: Natural Language Processing
• DS 725: Independent Study in Data Science I
• DS 726: Independent Study in Data Science II
• DS 790A Doctoral Dissertation & Research
• DS 791: Graduate Seminar
• DS 792: Pre-Doctoral Research
• DS 786: Special topics seminar in Data Science
• MATH 644: Regression Analysis Methods
• MATH 660: Introduction to Statistical Computing
• MATH 691: Stochastic Processes with Applications
• MATH 611: Numerical Methods for Computation
• MATH 678: Statistical Methods in Data Science
• MATH 699: Design and Analysis of Experiments
• MATH 665: Statistical Inference
• MATH 662: Probability Distributions
• MATH 631: Linear Algebra

Elective Courses

• CS 630: Operating System Design
• CS 631: Data Management System Design
• CS 634: Data Mining
• CS 656: Internet and Higher-Layer Protocols
• CS 670: Artificial Intelligence
• CS 610: Data Structures and Algorithms
• CS 732: Advanced Machine Learning
• CS 750: High Performance Computing
• CS 645: Security and Privacy in Computer Systems
• CS 602: Java Programming
• CS 608: Cryptography and Security
• CS 643: Cloud Computing
• CS 647: Counter Hacking Techniques
• CS 648: Cyber Sec Investigations & Law
• CS 708: Advanced Data Security and Privacy
• ECE 601: Linear Systems
• ECE 673: Random Signal Analysis
• IE 650: Advanced Topics in Operations Research
• IE 687: Healthcare Enterprise Systems
• IE 688: Healthcare Sys Perfor Modeling
• IT 696: Network Management and Security
• IS 634: Information Retrieval
• IS 665: Data Analytics for Info System
• IS 682: Forensic Auditing for Computing Security
• IS 684: Business Process Innovation
• IS 688: Web Mining
• MATH 787: Non-Parametric Statistics
• MATH 786: Large Sample Theory and Inference
• MATH 768: Probability Theory
• MATH 763: Generalized Linear Models
• MATH 707: Advanced Applied Mathematics IV: Special Topics
• MATH 717: Inverse Problems and Global Optimization
• MATH 761: Statistical Reliability Theory and Applications
• MATH 659: Survival Analysis
• MATH 680: Advanced Statistical Learning
• MATH 683: High Dimensional Stat Inferenc
• PHYS 621: Classical Electrodynamic
• PHYS 641: Statistical Mechanics
• PHYS 611: Adv Classical Mechanics
• CHEM 658: Advanced Physical Chemistry
• CHEM 714: Pharmaceutical Analysis
• ME 625: Introduction to Robotics
• ME 616: Matrix Methods in Mechanical Engineering
• CE 611: Project Planning and Control
• PTC 628: Analyzing Social Networks

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Overview:

New Jersey Institute of Technology (NJIT) is a public research university located in Newark, New Jersey. It is known for its strong focus on science, technology, engineering, and mathematics (STEM) fields. NJIT is consistently ranked among the top schools for return on investment.

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