Data Science Major
The major in data science provides students with the skills and the theory necessary to analyze and derive insights from large sets of structured and unstructured data. The program aims to provide students with the computing and programming skills needed for data science. It also aims to provide them with a broad and deep understanding of the concepts in statistics, machine learning, neural networks, artificial intelligence, natural language processing, data visualization, data mining, and the mathematics that is foundational to these concepts.
Goal 1: Students will be prepared for a variety of professions in data science and be able to adapt to complex technological and analytical environments in the workplace.
Outcome 1.1: Students will be able to perform standard data science tasks such as data wrangling, data visualization, statistical modeling, and the application of machine learning models.
Outcome 1.2: Students will be able to communicate, orally and in writing, the results of technical data analysis to both specialists and non-specialists.
Goal 2: Students will know the fundamental mathematical, statistical and computing skills needed for data science.
Outcome 2.1: Students will be able to perform basic computations in areas of math and statistics that are needed for understanding the methodologies and algorithms in data science.
Outcome 2.2: Students will be able to write computer programs to solve a problem or perform a task needed for data science.
The traditional undergraduate programs includes a minimum of 120 credits distributed across three components: A General Education component divided into Signature Courses, Variable Courses, and an Integrative Learning requirement; a Major and Divisional component; and Free Electives. In addition to course requirements as specified in each area, students must complete one certified course in each of the following overlay areas1:
- Diversity, Globalization or Non-western Area Studies,
- Ethics Intensive
- Writing Intensive, and
- Diversity
- 1
Overlay requirements are part of the 120 credit requirements
General Education Signature Courses
See this page about Signature courses.
General Education Variable Courses
See this page about Variable courses. Six to Nine courses
| Code | Title | Hours |
|---|---|---|
| Required Math Beauty | ||
| MAT 161 | Calculus I | 4 |
| Total Hours | 4 | |
General Education Overlays
General Education Integrative Learning Component
See this page about Integrative Learning Component. Three courses:
| Code | Title | Hours |
|---|---|---|
| CSC 115 | Intro to Computer Science | 3 |
| or CSC 133 | Python Programming for All | |
| MAT 162 | Calculus II | 4 |
| MAT 213 | Calculus III | 4 |
Major Requirements
| Code | Title | Hours |
|---|---|---|
| DSC 223 | Intro Math of Data Science | 3 |
| DSC 325 | Essentials of Data Science | 3 |
| or CSC 346 | Introduction to Data Science | |
| DSC 326 | Advanced Data Science | 3 |
| or CSC 347 | Advanced Data Science | |
| MAT 226 | Introduction to Linear Algebra | 3 |
| MAT 321 | Probability | 3 |
| MAT 322 | Mathematical Statistics | 3 |
| CSC 120 | Computer Science I | 4 |
| CSC 351 | Database Management Systems | 3 |
| CSC 362 | Artificial Intelligence | 3 |
| DSS 415 | Data Wrangling & Visualization | 3 |
| or DSS 416 | Data Wrangling: Ethics Int. | |
| DSS 445 | Statistical Programming Lang | 3 |
| Electives: | 9 | |
| Data Science for Sports | ||
| Regression and Time Series | ||
| Machine Learning/Data Science | ||
| Design of Experiments | ||
| Applied Statistical Methods | ||
| Numerical Analysis | ||
| Mathematical Optimization | ||
| Operations Research | ||
| Combinatorics & Graph Theory | ||
| Convex Analysis & Optimization | ||
| Generative AI | ||
| Image Data Science | ||
| Advanced Machine Learning | ||
| Intro to Security | ||
| Big Data and Web Intlgce | ||
| Internet Application Develpmnt | ||
| Introduction to Data Mining | ||
| Advanced Business Analytics | ||
| Machine Learning for Bus I | ||
| Machine Learning for Bus II | ||
| Econometrics | ||
| Economic Forecasting | ||
| Research Methods | ||
| Intro. to Network Science | ||
| Bioinformatics | ||
| Bioinformatics Lab | ||
| Special Topics | ||
| Total Hours | 43 | |
Double Major in Data Science
A double major in Data Science and Mathematics and a double major in Data Science and Computer Science are possible and can be completed within four years. A double major in Data Science and some other discipline might also be possible. For students who are interested in a double major in Data Science, please contact the program director.