The data science concentration is available for CS/CIS majors. Data science and analytics contribute to a wide range of scholarly disciplines and commercial endeavors. The courses provide principles and techniques of Descriptive, Predictive, and Prescriptive Analytics.
| Course | Title | Credits |
|---|---|---|
| Prescriptive Courses | ||
| CS 230 | Fundamentals of Computing | 3 |
| CS 231 | Computer Programming I | 3 |
| CS 232 | Computer Programming II | 3 |
| CS 234 | Discrete Computational Structures | 3 |
| CS 304 | Technical Writing for Computer Science (WI) | 3 |
| CS 310 | Software Engineering I | 3 |
| CS 331 | Data Structures and Algorithms | 3 |
| CS 333 | Computer Organization and Architecture | 3 |
| CS 350 | Fundamentals of Computer Operating Systems | 3 |
| CS 450 | Computer Networking | 3 |
| CS 462 | Ethics and Legal Issues (WI) | 3 |
| CS 488 | Database Systems | 3 |
| CS 491 | Software Engineering II | 3 |
| Data Science | ||
| CS 306 | Introduction to Data Science | 3 |
| CS 445 | Predictive Analysis | 3 |
| CS 480 | Special Topics in Data Science | 3 |
| CS 489 | Business Intelligence | 3 |
| MS 444 | Applied Statistical Methods | 3 |
| Courses in the major may not be taken until all prerequisites are completed with a grade of "C" or better. | ||
| In addition to the major courses, support courses required are: | ||
| MS 125 | Calculus I | 4 |
| MS 126 | Calculus II | 4 |
| MS 302 | Applied Probability and Statistics | 3 |
| MS 352 | Linear Algebra | 3 |
In addition to courses noted below, candidates for graduation must successfully complete all JSU Academic Regulations. More information about general education requirements can be found in the Summary of Degrees/Requirements section of the catalog.
| Freshman | ||
|---|---|---|
| Fall | Hours | |
| EH Composition sequence | 3 | |
| Natural Science sequence | 4 | |
| CS 201 | Introduction to Information Technology | 3 |
| CS 230 | Fundamentals of Computing | 3 |
| SSC 101 | First Year Experience | 0 |
| Hours | 13 | |
| Spring | ||
| EH Composition sequence | 3 | |
| EH 141 | Speech | 3 |
| Natural Science sequence | 4 | |
| CS 231 | Computer Programming I | 3 |
| CS 234 | Discrete Computational Structures | 3 |
| Hours | 16 | |
| Sophomore | ||
| Fall | ||
| Fine Arts | 3 | |
| MS 125 | Calculus I | 4 |
| History | 3 | |
| CS 232 | Computer Programming II | 3 |
| CS 304 | Technical Writing for Computer Science (WI) | 3 |
| Hours | 16 | |
| Spring | ||
| History, Social/Behavioral Science 1 | 3 | |
| MS 126 | Calculus II | 4 |
| CS 310 | Software Engineering I | 3 |
| CS 331 | Data Structures and Algorithms | 3 |
| CS 333 | Computer Organization and Architecture | 3 |
| Hours | 16 | |
| Junior | ||
| Fall | ||
| Literature | 3 | |
| Social/Behavioral Science | 3 | |
| CS 306 | Introduction to Data Science | 3 |
| CS 488 | Database Systems | 3 |
| MS 302 | Applied Probability and Statistics | 3 |
| Hours | 15 | |
| Spring | ||
| Humanities/Fine Arts 1 | 3 | |
| CS 350 | Fundamentals of Computer Operating Systems | 3 |
| CS 491 | Software Engineering II | 3 |
| MS 352 | Linear Algebra | 3 |
| MS 444 | Applied Statistical Methods | 3 |
| Hours | 15 | |
| Senior | ||
| Fall | ||
| Social/Behavioral Science | 3 | |
| CS 445 | Predictive Analysis | 3 |
| CS 450 | Computer Networking | 3 |
| Electives | 5 | |
| Hours | 14 | |
| Spring | ||
| CS 462 | Ethics and Legal Issues (WI) | 3 |
| CS 480 | Special Topics in Data Science | 3 |
| CS 489 | Business Intelligence | 3 |
| Electives | 6 | |
| Hours | 15 | |
| Total Hours | 120 | |
| 1 | Either a history sequence or a literature sequence is required. |