Overview
This specialized track is designed to deepen your understanding of the mathematical foundations and techniques that underpin both theoretical and applied research in data science. It blends rigorous mathematics courses with core theoretical data science subjects—such as game theory, probability, statistics, machine learning, deep learning, and optimization—within the broader framework of the Data Science and Engineering degree.
By choosing this track, students gain a powerful combination of analytical skills and theoretical insight that enhances their engineering education. Participants also have the opportunity to engage in hands-on research, guided by experienced faculty, through dedicated research project courses.
Successful completion of the track is recognized with a formal certificate signed by the faculty dean.
Why Choose This Track?
- Deepen your foundation: Modern data science demands a strong grasp of mathematical concepts.
- Balance theory and practice: Combine advanced mathematical studies with applied data science—why choose one when you can do both?
- Get ahead academically: Ideal preparation for graduate studies, with a solid theoretical background and research experience.
- Connect with research: Participate in cutting-edge research during your undergraduate degree.
- No extra credits required: The track fits within your existing degree credit requirements.
Who Should Apply?
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Have strong mathematical aptitude and are also drawn to real-world applications.
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Want to understand the theory behind the technology.
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Are interested in research and/or planning to pursue a graduate degree.
Admissions
Admission to the track is either before the start of the degree or following the completion of the first academic year. New students will receive an invitation to apply upon admission to the degree. Returning students will receive an invitation at the end of the first year. Admission to the program is based on admission data (new students) or first-year grades (for continuing students), as well as a personal interview.
Program Requirements
- Completion of 26 academic credits, comprising mandatory and elective courses.
- Optional participation in 1–2 research project courses.
- No extra credit load beyond the degree’s standard requirements—however, students are expected to take most of their elective credits from the approved track course list.
Core Curriculum
| Course Number | Course Name | Credit Points |
|---|---|---|
| 01040285 | Ordinary Differential Equations | 3.5 |
| 01040142 | Introduction to Metric and Topological Spaces | 3.5 |
| 01040273 | Introduction to Functional Analysis and Fourier Analysi | 5 |
| 01040165 | Real-Valued Functions | 3.5 |
| 01040122 | Complex Analysis | 3.5 |
| Course Number | Course Name |
|---|---|
| 00940701 | Research Project 1 |
| 00940702 | Research Project 2 |
| 00960212 | Probabilistic Graphical Models |
| 00960226 | Computation, Game Theory, and Economics |
| 00960231 | Mathematical Models in Advanced Information Retrieval |
| 00960311 | Theory and Algorithms for Optimization |
| 00960335 | Optimization Under Uncertainty |
| 00960336 | Optimization Methods in Machine Learning |
| 00960351 | Polyhedral Methods for Integer Programming |
| 00960415 | Topics in Regression |
| 00960470 | Semi-Parametric Models |
| 00960576 | Learning and Complexity in Game Theory |
| 00970211 | Fault-Tolerant Network Protocols |
| 00970249 | Machine Learning in Sequential Decision Making |
| 00970280 | Algorithms in Uncertainty Scenarios |
| 00970317 | Cooperative Game Theory |
| 00970325 | Theory and Methods in Sparse Optimization |
| 00980413 | Stochastic Processe |
| 01060429 | Stochastic Processes |
| 00980414 | Statistical Theory 3 |
| 00980455 | Probability and Stochastic Processes 2 |
| 00980312 | Optimization 2 |
| 01040030 | Introduction to Partial Differential Equations |
| 01040158 | Introduction to Groups |