Data Science Major
The Major in Data Science exposes students the theory and skills necessary to analyze and derive insights from large sets of structured and unstructured data. The program aims to provide students with deep understanding of concepts related to statistics, machine learning, neural networks, natural language processing, data mining, data visualization and the mathematics that is foundational to these concepts.
Goal 1: Students will be prepared for a variety of professions in Data Science and/or Master’s degree programs in Data Science and be able to adapt to complex technological and analytical environments in the workplace.
Objective 1.1: Students will be proficient programming in Python and in R.
Objective 1.2: Students will be able to analyze data sets using a variety of software and analytical approaches.
Objective 1.3: Students will be able to communicate the results of technical data analysis in a manner that is understandable to non-specialists.
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 |
| MAT 162 | Calculus II | 4 |
| MAT 213 | Calculus III | 4 |
Major Requirements:
| Code | Title | Hours |
|---|---|---|
| MAT 162 | Calculus II | 4 |
| MAT 213 | Calculus III | 4 |
| DSC 223 | Intro Math of Data Science | 3 |
| MAT 226 | Introduction to Linear Algebra | 3 |
| MAT 321 | Probability | 3 |
| MAT 322 | Mathematical Statistics | 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 | |
| CSC 115 | Intro to Computer Science | 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 | |
| Design of Experiments | ||
| Applied Statistical Methods | ||
| Regression and Time Series | ||
| Machine Learning/Data Science | ||
| Numerical Analysis | ||
| Mathematical Optimization | ||
| Operations Research | ||
| Convex Analysis | ||
| Combinatorics & Graph Theory | ||
| 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 | ||
| Bioinformatics | ||
| Bioinformatics Lab | ||
| Special Topics | ||
| Total Hours | 54 | |