Ph.D. in Data Science - Computing Track

The Ph.D. in Data Science - Computing Track is a rigorous program designed to equip students with advanced knowledge and skills in data science, with a focus on computing. The program is tailored to meet the needs of students who wish to pursue a career in research and development in the field of data science.

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 Ph.D. program are:

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

Course Requirements

The courses include 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 and are recommended to students as such.

• Core courses include:
  • 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
  • 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 include:
  • 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

Computing Track Requirements

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 at the 600 level and four 3-credit courses at the 700 level. Students who start the program with a recognized Baccalaureate degree are required to take eight 3-credit courses at either the 600 level or 700 level, as well as four additional 700-level 3-credit courses, for a total of twelve 3-credit courses.

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 exam consists of two components: 1) Written Research Report, 2) Oral Research Presentation.

Dissertation Requirements

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 Ph.D. program. Students who cannot defend their dissertation by the end of the sixth year will be dismissed from the program.

Research Areas

Potential dissertation areas and possible elective courses include:

• Machine Learning or related areas: CS 732 Advanced Machine Learning, DS 789 Trustworthy AI
• Statistics: MATH 787 Non-parametric statistics, MATH 786 Large Sample Theory and Inference
• Data Visualization: DS 650 Data Visualization
• High Performance Computing: DS 642 Applications of Parallel Computing, CS 668 Parallel Algorithms, CS 750 High Performance Computing

Student Evaluation

The student's progress on program requirements and research is assessed by the departmental PhD Committee each semester. Students are required to attend and participate in Data Science research seminars every semester and are encouraged to attend other research seminars across campus.

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.

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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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Student Life and Campus Experience:

The provided context highlights the positive experiences of NJIT students. Students have access to various resources and opportunities, including research experiences, internships, and career fairs. The university emphasizes the importance of student involvement and provides a supportive environment for students to pursue their academic and professional goals.

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Key Reasons to Study There:

• Strong focus on STEM fields
• High return on investment
• Excellent research opportunities
• Supportive faculty and staff
• Vibrant campus community

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Academic Programs:

The context mentions various academic programs, including: - Computing - Architecture & Design - Engineering - Humanities & Liberal Arts - Math & Science - Business