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COMPUTER SCIENCE, MS
Computer Science
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Master of Science in Computer Science
Plan 1 - 30 hours with Thesis
| Requirements | Hours | |
|---|---|---|
| Select 24 credit hours of CS courses and approved non-CS electives at the 500+ level 1,2 | 24 | |
| Allowed Electives from other disciplines (up to 3 chrs) | ||
| Numerical Linear Algebra 3 | ||
| Numerical Analysis I 3 | ||
| or | ||
| Numerical Analysis II 3 | ||
| CS 699 | Master's Thesis Research | 6 |
| Total Hours | 30 | |
Plan II - 30 hours
| Requirements | Hours | |
|---|---|---|
| Select 30 credit hours of CS courses and approved non-CS electives at the 500+ level 1,2 | 30 | |
| Allowed Electives from other disciplines (up to 3 chrs) | ||
| Numerical Linear Algebra 3 | ||
| Numerical Analysis I 3 | ||
| or | ||
| Numerical Analysis II 3 | ||
| Total Hours | 30 | |
| 1 |
No more than three (9 credit hours of) 500 level courses can count towards the MS degree |
| 2 |
No more than one (3 credit hours of) special course (CS 697 or CS 598) can count towards the MS degree |
| 3 |
May substitute any other graduate level course approved by the graduate program director |
Master of Science in Data Science
Plan I
| Requirements | Hours | |
|---|---|---|
| Core | 12 | |
| CS 510 | Database Application Development | |
| or CS 610 | Database Systems | |
| CS 652 | Advanced Algorithms and Applications | |
| CS 667 | Machine Learning | |
| CS 685 | Foundations of Data Science | |
| or CS 680 | Matrix Algorithms for Data Science | |
| Electives 1 | 12 | |
| Data Analytics | ||
| Big Data Programming | ||
| Artificial Intelligence | ||
| Natural Language Processing | ||
| Data Mining | ||
| Deep Learning | ||
| Computer Vision and Convolutional Neural Networks | ||
| Data Visualization | ||
| Matrix Algorithms for Data Science | ||
| Complex Networks | ||
| Cyber Security | ||
| Computer Security | ||
| Network Security | ||
| Modern Cryptography | ||
| Cloud Security | ||
| High Performance Computing | ||
| Parallel Computing | ||
| Cloud Computing | ||
| Digital Forensics | ||
| Investigating Online Crimes | ||
| Digital Media Forensics | ||
| Cyber Risk Management | ||
| Non-Computer Science Electives 2 | ||
| Biostatistics | ||
| Intermediate Statistical Analysis I | ||
| Intermediate Statistical Analysis II | ||
| Statistical Methods I | ||
| Statistical Methods II | ||
| Bioinformatics | ||
| Introduction to Bioinformatics | ||
| Algorithms in Bioinformatics | ||
| Biological Data Management | ||
| Business Intelligence | ||
| Data Science for Business | ||
| Applied Marketing Research | ||
| Quantitative Analysis for Business Managers | ||
| Thesis Research | 6 | |
| Master's Thesis Research | ||
| Total Hours | 30 | |
Plan II
| Requirements | Hours | |
|---|---|---|
| Core | 12 | |
| CS 510 | Database Application Development | |
| or CS 610 | Database Systems | |
| CS 652 | Advanced Algorithms and Applications | |
| CS 667 | Machine Learning | |
| CS 685 | Foundations of Data Science | |
| or CS 680 | Matrix Algorithms for Data Science | |
| Electives 1 | 18 | |
| Data Analytics | ||
| Big Data Programming | ||
| Artificial Intelligence | ||
| Natural Language Processing | ||
| Data Mining | ||
| Deep Learning | ||
| Computer Vision and Convolutional Neural Networks | ||
| Data Visualization | ||
| Matrix Algorithms for Data Science | ||
| Complex Networks | ||
| Cyber Security | ||
| Computer Security | ||
| Network Security | ||
| Modern Cryptography | ||
| Cloud Security | ||
| High Performance Computing | ||
| Parallel Computing | ||
| Cloud Computing | ||
| Digital Forensics | ||
| Investigating Online Crimes | ||
| Digital Media Forensics | ||
| Cyber Risk Management | ||
| Non-Computer Science Electives 2 | ||
| Biostatistics | ||
| Intermediate Statistical Analysis I | ||
| Intermediate Statistical Analysis II | ||
| Statistical Methods I | ||
| Statistical Methods II | ||
| Bioinformatics | ||
| Introduction to Bioinformatics | ||
| Algorithms in Bioinformatics | ||
| Biological Data Management | ||
| Business Intelligence | ||
| Data Science for Business | ||
| Applied Marketing Research | ||
| Quantitative Analysis for Business Managers | ||
| Total Hours | 30 | |
| 1 |
At most three credit hours of special courses (CS 697: Directed Readings or CS 598: Practical Work Experience) can count towards the MSDS degree. No more than three (9 credit hours of) 500 level courses can count towards the MSDS degree. |
| 2 |
Students may take up to three (9 credit hours of) non-CS electives upon the approval of the graduate program director. |

