Selected Educational Outcomes
| 1. | Collect, clean, and preprocess data from various sources for accurate analysis. |
| 2. | Apply key mathematical concepts such as linear algebra, calculus, and probability theory to solve data science problems. |
| 3. | Develop proficiency in using programming languages appropriate to the ever-changing field of data science to manipulate and analyze data. |
| 4. | Design, build, train, and evaluate predictive models using fundamental machine learning algorithms and techniques. |
| 5. | Demonstrate skills in designing, querying, and managing databases to support data-driven decision-making. |
| 6. | Analyze ethical considerations and legal issues related to data collection, analysis, and sharing, ensuring adherence to data privacy regulations. |
| 7. | Communicate complex technical information and data insights to non-technical stakeholders effectively through written and oral presentations. |
| 8. | Complete a capstone project that involves real-world data analysis, demonstrating practical skills and knowledge in data science. |
Requirements for the Bachelor of Science Degree with a Major in Data Science
| Code | Title | Hours |
|---|---|---|
| Core Curriculum | 60 | |
| Core IMPACTS (see VSU Core Curriculum) | 42 | |
| Core Field of Study | 18 | |
| ACED 1100 | Introduction to Business | 3 |
| or BUSA 1105 | Introduction to Business | |
| DATA 2600 | Foundations of Data Science | 3 |
| MATH 2261 | Analytic Geometry and Calculus I | 3 |
| or MATH 1261 | Survey of Calculus I | |
| MATH 2262 | Analytic Geometry and Calculus II | 4 |
| or MATH 1262 | Survey of Calculus II | |
| CS 1301 | Principles of Programming I | 4 |
| MATH 2900 | Mathematics Sophomore Seminar – Discrete Mathematics | 2 |
| Senior College Curriculum | 60 | |
| MATH 3600 | Probability and Statistics | 3 |
| DATA 3700 | Statistical Computing | 3 |
| DATA 3801 | Programming for Data Science I | 3 |
| DATA 3502 | Data Architecture | 3 |
| DATA 3505 | Data Management | 3 |
| DATA 3508 | Data-Driven Decision Making | 3 |
| DATA 3355 | Data Mining | 3 |
| DATA 4610 | Statistical Machine Learning I | 3 |
| DATA 4905 | Topics in Data Science | 3 |
Select One: | ||
| MATH 3900 | Mathematical Theory of Interest | 3 |
| DATA 3701 | Time Series Forecasting Techniques | 3 |
| DATA 4750 | Data Visualization for Data Science | 3 |
| DATA 4990 | Special Topics in Data Science | 3 |
| Supporting Courses | 3 | |
| MATH 2150 | Introduction to Linear Algebra | 3 |
| Choose from among the following concentrations: | ||
| Computational Science Engineering | 19 | |
| Principles of Programming II | ||
| UNIX Programming | ||
| Introduction to Manufacturing Systems | ||
| Industrial Automation | ||
| Select two: | ||
| Computer Organization | ||
| Data Structures | ||
| Introduction to Big Data and Machine Learning | ||
| Artificial Intelligence | ||
| Industrial Cost Control | ||
| Project Management | ||
| Special Topics in Data Science | ||
| Supply Chain and Logistics | 18 | |
| Six Sigma and Lean Manufacturing | ||
| Simulation Modeling of Industrial Systems | ||
| Supply Chain and Logistics Concepts | ||
| Operations Research | ||
or MATH 4901 | Operations Research I | |
| Select two: | 6 | |
| Industrial Cost Control | ||
| Industrial Automation | ||
| Project Management | ||
| Time Series Forecasting Techniques | ||
| Special Topics in Data Science | ||
| Business Analytics | 18 | |
| Operations Research | ||
| Time Series Forecasting Techniques | ||
| Project Management | ||
| Principles of Accounting I | ||
| Mathematical Theory of Interest | ||
| Select one: | 3 | |
| Data Analytics in Accounting | ||
| Management Information Systems | ||
| Data Visualization for Data Science | ||
| Special Topics in Data Science | ||
| General Electives | 8-9 | |
| Total hours required for the Degree | 120 | |
Note:*If MATH 2261/2262 are taken instead of MATH 1261/1262, then the extra 2 hours may be applied in General Electives.
