Data Science, MS
The Master of Science in Data Science program is designed for students who have completed their undergraduate degree in Computer Science or a related field, and who seek to enhance their theoretical, analytical, and practical skills through a solid foundation in data science, statistical computing, object-oriented programming, database systems, data mining, artificial intelligence, natural languages, machine learning, computer networks, cloud computing, big data and analytics. Achieving an MS degree in Data Science provides career opportunities in the following industries: technology, banking/financial, healthcare, media/telecom, defense, entertainment, retail, real estate, education, government, and non-profit industries/entities.
Students who complete the degree program will be prepared for various industry recognized certifications such as Certified Analytics Professional (CAP), Data Science Council of America (DASCA), IBM Certified Data Architect, Microsoft MCSE-Data Management and Analytics, Microsoft Certified Azure Data Scientist Associate, SAS Certified Advanced Analytics Professional, SAS Certified Big Data Professional, SAS Certified Data Scientist and/or other certifications.
Primary program objectives of the MS in Data Science degree program are:
- Upon completion of the program, graduates will have acquired strong theoretical and practical skills in coding, data modeling, statistical computing, data visualization, forecasting, and technical analytic techniques - all needed in modern business settings
- Upon completion of the program, graduates will have developed competencies in the areas of database systems, data mining, big data, data science, computer networks, cloud computing, artificial intelligence, machine learning, and statistical programming as they prepare for advanced careers in data science
- Upon completion of the program, graduates will be able to utilize leading edge resources such as Oracle, Hadoop, AWS, SAS, Tensorflow, and Tableau, and languages such as SQL, Python and R. These skills will strengthen the graduate’s expertise in assessing and analyzing systems in a variety of industry sectors.
Requirements
| Code | Title | Credits |
|---|---|---|
| Required Core Courses | ||
| CS-617 | Statistical Computing | 3 |
| CS-628 | Data Science | 3 |
| CS-630 | Database Systems | 3 |
| CS-633 | Data Mining | 3 |
| CS-650 | Artificial Intelligence | 3 |
| CS-655 | Machine Learning | 3 |
| CS-675 | Big Data: Management & Analytics | 3 |
| CS-703 | Applied Data Science Project | 3 |
| KG-604 | Graduate Research & Critical Analysis | 3 |
| Required Core Courses Subtotal | 27 | |
| Elective Courses | ||
| Select three of the following: | 9 | |
| Computer Networks | ||
| Computer Security & Privacy | ||
| Analytic Techniques | ||
| Cloud Computing | ||
| Research Topics in Data Science | ||
| Subtotal | 9 | |
| Total Credits | 36 | |
| Semester 1 | Credits | |
|---|---|---|
| CS-628 | Data Science | 3 |
| CS-630 | Database Systems | 3 |
| KG-604 | Graduate Research & Critical Analysis | 3 |
| Credits | 9 | |
| Semester 2 | ||
| CS-617 | Statistical Computing | 3 |
| CS-633 | Data Mining | 3 |
| CS-650 | Artificial Intelligence | 3 |
| Credits | 9 | |
| Semester 3 | ||
| CS-655 | Machine Learning | 3 |
| CS-ELE | Computer Science Elective | 3 |
| CS-ELE | Computer Science Elective | 3 |
| Credits | 9 | |
| Semester 4 | ||
| CS-675 | Big Data: Management & Analytics | 3 |
| CS-703 | Applied Data Science Project | 3 |
| CS-ELE | Computer Science Elective | 3 |
| Credits | 9 | |
| Total Credits | 36 | |