Data Science
Data science is an interdisciplinary field that combines mathematics, statistics, and computer science to address complex challenges through the analysis and interpretation of data. As data continues to shape decision-making across nearly every profession, advanced training in data science prepares graduates to lead data-driven initiatives in research, industry, and government.
Students pursuing a master of science degree in data science deepen their knowledge of statistical modeling, computational methods, machine learning, data management, and data visualization while developing expertise in the responsible application of modern analytical techniques. Coursework emphasizes both theoretical foundations and practical problem solving using contemporary data science tools.
Through your graduate studies at Missouri S&T, you will develop the advanced analytical and computational skills needed to tackle complex data-intensive problems and communicate meaningful results to diverse audiences. Graduates are prepared for leadership roles in data science and analytics or for doctoral study in data science and related disciplines.
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 |