Data Science
Master of Science
Data Science
The non-thesis M.S. in Data Science curriculum is designed to give students an interdisciplinary educational experience that provides a solid foundation in computer science, mathematics, and statistics. The curriculum prepares students to solve challenging data-driven problems across a variety of application areas. Students pursuing the non-thesis M.S. in Data Science must select a primary focus area, namely Computational Learning or Statistical Learning. Courses within the focus area provide more advanced discipline-specific content. Students must take a minimum of 12 total credit hours of COMP SCI courses and 12 total hours of STAT or MATH courses.
In accordance with campus requirements, this M.S. non-thesis degree requires a minimum of thirty hours of graduate credit. The plan of study must include a minimum of twenty-four credit hours of 4000-, 5000-, and 6000- level lecture courses (1000/2000-level courses cannot be included). A minimum of nine credit hours of the required coursework must come from the group of 6000-level lecture courses. Additionally, no credit hours of graduate research may be applied toward the plan of study.
Required courses:
| COMP SCI 5206 | Probability and Its Applications in Computing | 3 |
| or STAT 5643 | Probability And Statistics | |
| COMP SCI 5420 | Introduction to Machine Learning | 3 |
| or MATH 5680 | Mathematics of Machine Learning | |
| COMP SCI 5400 | Introduction To Artificial Intelligence | 3 |
| COMP SCI 5480 | Deep Learning | 3 |
| STAT 5346 | Regression Analysis | 3 |
| or COMP SCI 5204 | Regression Analysis | |
| STAT 5364 | Causal Data Science | 3 |
| Primary focus area electives | 6 | |
| Secondary focus area elective | 3 | |
| Additional elective 1 | 3 | |
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The additional elective can be chosen from the primary focus area, the secondary focus area, or the additional course options
Computational Learning Focus Area Electives
| COMP SCI 5402 | Introduction to Data Mining | 3 |
| COMP SCI 5409 | Applied Social Network Analysis | 3 |
| COMP SCI 5411 | Natural Language Processing | 3 |
| COMP SCI 5480 | Deep Learning | 3 |
| COMP SCI 5700 | Bioinformatics | 3 |
| COMP SCI 6202 | Markov Decision Processes | 3 |
| COMP SCI 6400 | Advanced Topics In Artificial Intelligence | 3 |
| COMP SCI 6401 | Advanced Evolutionary Computing | 3 |
| COMP SCI 6402 | Advanced Topics in Data Mining | 3 |
| COMP SCI 6406 | Machine Learning in Computer Vision | 3 |
| COMP SCI 6407 | Internet of Things with Data Science | 3 |
Statistical Learning Focus Area Electives
| STAT 5210 | Statistical Data Analysis Using R | 3 |
| STAT 5270 | Foundations of Statistical Learning | 3 |
| STAT 5290 | Computational Bayesian Methods using Python | 3 |
| STAT 5353 | Statistical Data Analysis | 3 |
| STAT 5814 | Applied Time Series Analysis | 3 |
| STAT 6239 | Clustering Algorithms | 3 |
| STAT 6342 | Categorical Data Analysis | 3 |
| STAT 6343 | Nonparametric Statistical Methods | 3 |
| STAT 6344 | Design And Analysis Of Experiments | 3 |
| STAT 6545 | Multivariate Statistical Methods | 3 |
Additional Course Options
| COMP ENG 5310 | Computational Intelligence | |
| COMP SCI 5200 | Analysis Of Algorithms | 3 |
| COMP SCI 5201 | Object-Oriented Numerical Modeling I | 3 |
| COMP SCI 5408 | Game Theory for Computing | 3 |
| COMP SCI 5802 | Introduction to Parallel Programming and Algorithms | 3 |
| COMP SCI 6204 | Applied Graph Theory for Computer Science | 3 |
| COMP SCI 6304 | Cloud Computing and Big Data Management | 3 |
| COMP SCI 6601 | Privacy Preserving Data Integration and Analysis | 3 |
| ECON 5360 | Data Driven Strategic Insights | 3 |
| ECON 5380 | Data Intelligence using Case Studies | 3 |
| ENG MGT 5414 | Introduction To Operations Research | 3 |
| ENG MGT 6412 | Mathematical Programming | 3 |
| ENG MGT 6415 | Optimization under Uncertainty | 3 |
| IS&T 5420 | Business Analytics and Data Science | 3 |
| MATH 5601 | Introduction to Numerical Analysis | 3 |
| MATH 5670 | Scientific Programming with Python | 3 |
| MATH 5762 | Marketing Revolution with Machine Learning | 3 |
| MATH 6490 | Nonlinear Optimization in Machine Learning | 3 |
| STAT 5644 | Mathematical Statistics | 3 |
| STAT 6553 | Linear Statistical Models I | 3 |
| STAT 6841 | Stochastic Processes | 3 |
| SYS ENG 5212 | Introduction to Neural Networks and Applications | 3 |
| SYS ENG 6102 | Information Based Design | 3 |
| SYS ENG 6213 | Deep Learning | 3 |
| SYS ENG 6215 | Adaptive Dynamic Programming | 3 |