Master of Data Science
Collaborative program with the Department of Applied Mathematics
This professional master’s degree program consists of 33 credit hours of coursework, including a practicum, in data science. The program is designed primarily for those with previous degrees or experience in computer science, statistics, mathematics, natural sciences, or business, who are interested in preparing for a career as a data science professional in business and industry. Full-time students may complete the program in one year, including one summer term.
Admission Requirements
Applicants should have a bachelor’s degree from an accredited university with a minimum cumulative GPA of 3.0/4.0. A combined verbal and quantitative GRE examination score of at least 304 and an analytic writing score of at least 3.0 (for the post-October 2002 test) are required. The GRE requirement is waived for students with a bachelor’s degree from an accredited college or university in the United States with a cumulative GPA of at least 3.0/4.0.
Prerequisites include knowledge of a high level programming language at the level of CS 201 (Java or C/C++programming is required), a data structures course at the level of CS 331, experience with database programming at the level of CS 425, linear algebra at the level of MATH 332, and probability and statistics at the level of MATH 474. Information on these courses is available in this catalog .
Students with an insufficient background in computer science and/or mathematics will be required to take the relevant prerequisite courses and earn at least a B grade in each. These prerequisite courses do not count toward the 33 credit hour requirement.
Curriculum
Coursework includes 18 credit hours of required core courses and 6 credit hours of CSP 572/MATH 572 Data Science Practicum. At least 9 credit hours must be taken of 500-level CS or CSP courses and 9 credit hours of 500-level MATH courses, not including the CSP 572/MATH 572 Data Science Practicum.
Up to 6 credit hours of 400-level undergraduate coursework may be used toward degree requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Core Courses | (18) | |
| CS 525 | Advanced Database Organization | 3 |
| or CS 554 | Data-Intensive Computing | |
| MATH 563 | Mathematical Statistics | 3 |
| or MATH 564 | Applied Statistics | |
| SCI 511 | Project Management | 3 |
| SCI 522 | Public Engagement for Scientists | 3 |
| CS 584 | Machine Learning | 3 |
| or MATH 569 | Statistical Learning | |
| CSP 571 | Data Preparation and Analysis | 3 |
| or MATH 571 | Data Preparation and Analysis | |
| Data Science Capstone | (6) | |
| CSP/MATH 572 | Data Science Practicum | 6 |
| Data Science Electives | (9) | |
| Select 9 credit hours of Data Science Electives | 9 | |
| Total Credit Hours | 33 | |
Data Science Electives
| Code | Title | Credit Hours |
|---|---|---|
| Computational Fundamentals | (27) | |
| CS 425 | Database Organization | 3 |
| CS 430 | Introduction to Algorithms | 3 |
| CS 450 | Operating Systems | 3 |
| CS 525 | Advanced Database Organization | 3 |
| CS 535 | Design and Analysis of Algorithms | 3 |
| CS 546 | Parallel and Distributed Processing | 3 |
| CS 553 | Cloud Computing | 3 |
| CS 554 | Data-Intensive Computing | 3 |
| CS 589 | Software Testing and Analysis | 3 |
| Computer Science Applications | (33) | |
| CS 422 | Data Mining | 3 |
| CS 512 | Computer Vision | 3 |
| CS 513 | Geospatial Vision and Visualization | 3 |
| CS 522 | Advanced Data Mining | 3 |
| CS 529 | Information Retrieval | 3 |
| CS 556 | Cyber-Physical Systems: Languages and Systems | 3 |
| CS 557 | Cyber-Physical Systems: Networking and Algorithms | 3 |
| CS 579 | Online Social Network Analysis | 3 |
| CS 583 | Probabilistic Graphical Models | 3 |
| CS 584 | Machine Learning | 3 |
| CS 585 | Natural Language Processing | 3 |
| Mathematics, Probability, and Statistics | (33) | |
| MATH 454 | Graph Theory and Applications | 3 |
| MATH 486 | Mathematical Modeling I | 3 |
| MATH 532 | Linear Algebra | 3 |
| MATH 540 | Probability | 3 |
| MATH 542 | Stochastic Processes | 3 |
| MATH 553 | Discrete Applied Mathematics I | 3 |
| MATH 554 | Discrete Applied Mathematics II | 3 |
| MATH 565 | Monte Carlo Methods in Finance | 3 |
| MATH 567 | Advanced Design of Experiments | 3 |
| MATH 569 | Statistical Learning | 3 |
| MATH 574 | Bayesian Computational Statistics | 3 |
| Mathematical and Scientific Computing | (15) | |
| BIOL 550 | Bioinformatics | 3 |
| MATH 577 | Computational Mathematics I | 3 |
| MATH 578 | Computational Mathematics II | 3 |
| MATH 590 | Meshfree Methods | 3 |
| PHYS 440 | Computational Physics | 3 |
