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 | 1 |
| or MATH 1261 | Survey of Calculus I | |
| MATH 2262 | Analytic Geometry and Calculus II | 4 |
| or MATH 1262 | ||
| 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 Architecture | 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 |
| Supporting Courses | 9 | |
| 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 | |
| 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 | ||
or MATH 4901 | Operations Research I | |
| Time Series Forecasting Techniques | ||
| Project Management | ||
| Principles of Accounting I | ||
| Select two: | 6 | |
| Data Analytics in Accounting | ||
| Management Information Systems | ||
DATA 4750 | ||
| Special Topics in Data Science | ||
| General Electives | 8-9 | |
| Total hours required for the Degree | 120 | |
- 1
One (1) hour of MATH 2261 spilled from STEM Area
