Doctor of Philosophy with a major in Industrial Engineering Statistics and Data Science ** Track
| Key |
|---|
| * Deleted |
| ** New |
| *** Moved to another section |
| Note: Any course with the a line through it is either moved from a section or deleted. |
Important Note: This program will not be officially listed in the Georgia Tech catalog until Summer 2026. Students may choose to follow the catalog requirements from a later year than their matriculation year. However, they also have the right to remain under the catalog requirements from the year they first enrolled.
Domain Core Proposed Changes ( 4* 3** courses)
| Course | Title | Hours |
|---|---|---|
| ISYE 6412 | Theoretical Statistics | 3 |
| ISYE 6413*** | Design and Analysis of Experiments | 3 |
| ISyE 6416 | Computational Statistics | 3 |
| ISyE 7401 | Advanced Statistical Modeling | 3 |
Statistics Electives Proposed Changes ( 4* 5** courses)
| Course | Title | Hours |
|---|---|---|
| ISYE 6402 | Time Series Analysis | 3 |
| ISYE 6404 | Nonparametric Data Analysis | 3 |
| ISYE 6405** | Statistical Methods-Manufacturing and Design | 3 |
| ISYE 6413*** | Design and Analysis of Experiments | 3 |
| ISYE 6420 | Bayesian Statistics | 3 |
| ISYE 6421 | Biostatistics | 3 |
| ISYE 6740 | Computational Data Analysis: Learning, Mining, and Computation | 3 |
| ISYE 6781* | Reliability Theory | 3 |
| ISYE 6783 | Statistical Techniques of Financial Data Analysis | 3 |
| ISYE 6805 | Reliability Engineering | 3 |
| ISYE 7400 | Advanced Design of Experiments | 3 |
| ISYE 7405 | Multivariate Data Analysis | 3 |
| ISYE 7406 | Data Mining and Statistical Learning | 3 |
| MATH 6262** | Statistical Estimation | 3 |
| MATH 6263** | Testing Statistical Hypothesis | 3 |
| MATH 7252** | High dimensional statistics | 3 |
| ECE 8803 HOS** | High dimensional statistics, signal processing and optimization | 3 |
Statistics and Data Science Technical Electives (5 courses)
| Course | Title | Hours |
|---|---|---|
| ISYE 6661 | Linear Optimization | 3 |
| ISYE 6662 | Discrete Optimization | 3 |
| ISYE 6663 | Nonlinear Optimization | 3 |
| ISYE 6664 | Stochastic Optimization | 3 |
| ISYE 6761 | Stochastic Processes I | 3 |
| ISYE 6762 | Stochastic Processes II | 3 |
| ISYE 6832 | Simulation Theory and Methods | 3 |
| ISYE 6810 | Systems Monitoring and Prognostics | 3 |
| ISYE 7204 | Informatics in Production & Service Systems | 3 |
| ISYE 7750** | Mathematical Foundations of Machine Learning | 3 |
| MATH 6014 | Graph Theory and Combinatorial Structures | 3 |
| MATH 6241 | Probability I | 3 |
| MATH 6242 | Probability II | 3 |
| MATH 6643 | Numerical Linear Algebra | 3 |
| MATH 7251** | High Dimensional Probability | 3 |
| CS 6550 | Design and Analysis of Algorithms | 3 |
| CS 7520 | Approximation Algorithms | 3 |
| CS 7530 | Randomized Algorithms | 3 |
| CS 7545 | Theoretical Foundations of Machine Learning | 3 |
| ECE 6254 | Statistical Machine Learning | 3 |
All ten courses satisfying the aforementioned requirements in the Program of Study must be completed to obtain doctoral candidacy.
Comprehensive Exam Overview
1. In-class Exam (2 hours)
Coverage
The exam will have 1-2 questions from theoretical statistics and 1-2 questions from data science. The course ISyE 6412 (Theoretical Statistics) will provide good preparation for answering questions on theoretical statistics. The courses: ISyE 6416 (Computational Statistics) and ISyE 7401 (Advanced statistical modeling) will be helpful for the data science-related questions.
Format
The in-class exam is closed-book, with only two sheets of handwritten notes allowed (8.5”x11”, both sides can be used). The notes used should be submitted with the exam.
2. Oral Research Presentation (10-minute presentation and 20 minutes Q&A)
Coverage
The student’s individual research conducted under the supervision of one or more faculty members. The purpose of this requirement is to assess the student’s aptitude for research. Students must demonstrate adequate research capability during the presentation.
Format
The Oral Research Presentation will last a maximum of 30 minutes, with 10 minutes allocated for the student’s presentation and 20 minutes for follow-up questions from the program committee. The presentation should include a clear statement of the research problem, an overview of the methodology used, key findings, and the significance of the results. Students will be evaluated based on their understanding of the research topic, the clarity of their presentation, and their ability to answer questions and defend their work.
3. Letter from Advisor(s)
The student’s research advisor(s) should provide an evaluation of student’s research capability and an overall assessment of the student’s ability to successfully complete the PhD study.
Waiver of Statistics Comprehensive Exam
Conditions
A student may request a waiver of the statistics comprehensive exam and will be treated as successfully passing the exam if both of the following conditions are satisfied:
- The student has passed an equivalent comprehensive exam in a program that is equivalent to the Statistics and Data Science Program in ISyE (e.g., passing a qualifying exam in a solid statistics department in the US);
- The student has made satisfactory progress between passing that exam and the time the student requests for a waiver in ISyE. The student must provide sufficient information to convince the examination committee that the exam is as rigorous and comprehensive as the exam in ISyE.
The above may not exclude other conditions.
Procedure
When the exam committee reaches the conclusion that the student can receive a waiver, they will send a petition to the Graduate Committee in ISyE as well as to the Associate Chair of Graduate Studies. The Graduate Committee and Associate Chair need to approve the committee’s petition.