Data Science at SVU
Southern Virginia University is developing a Bachelor of Science in Data Science, available on campus and fully online. This page describes the program as proposed by the Mathematics and Computer Science faculty.
The degree is a proposal that is moving through the university’s curriculum approval process. Requirements, course numbers, and dates may change before it appears in the catalog. If you are interested in the program, please email me.
Why data science
Data and AI literacy are becoming essential across a wide range of industries. As data-driven decision-making and artificial intelligence continue to shape the future of work, some students will want to make data science their profession. The Bureau of Labor Statistics projects employment of data scientists to grow 35 percent from 2025 to 2035, much faster than average, with a median wage of $120,230 (May 2025).
What makes this degree different
- Available entirely online, and personal. Large online programs are built for scale: standardized courses, accelerated terms, and many start dates a year. Ours is the opposite. Online students enroll in the same small, full-semester sections as students in Buena Vista, taught by the same professors, who know them by name. They are part of the class and the campus community.
- Rigorous. Calculus I and II, linear algebra, discrete mathematics, mathematical statistics, and statistical modeling give graduates a mathematical foundation that prepares them for graduate study as well as employment.
- Focused. The major requires six computer science courses: programming, data structures, databases, computational data science, and artificial intelligence. Students spend the rest of their time on statistics and applied analysis.
- Applied. Every student completes an applied analytics workshop on data from a field of their own choosing and a data science capstone.
- Connected to other fields. Students choose two electives from computer science, business, economics, political science, and psychology, so the major can point toward the field where a student wants to apply data science.
- A liberal arts setting. The degree is built almost entirely from mathematics and computer science courses SVU already teaches, alongside the SVU Core.
What graduates will be able to do
- Use Python, its data science libraries (such as pandas, matplotlib, and scikit-learn), regular expressions, and Jupyter notebooks to collect, clean, analyze, and visualize data.
- Apply the theory of data science, including regularized multiple linear regression, statistical and stochastic modeling, principal component analysis, and the fundamentals of machine learning, and explain the mathematics behind these methods.
- Apply data science methods to a problem in another field, through the Applied Data Analytics workshop and the senior capstone.
- Communicate the results of a data analysis, from question to conclusion, to technical and non-technical audiences.
Degree requirements (53 credits)
MAT 241 Calculus I carries 4 credits and DAT 498 carries 1; every other course carries 3.
Computing (18 credits)
| Course | Title | Credits | Offered |
|---|---|---|---|
| CSC 110 | Authoring for the Web | 3 | Every semester |
| CSC 120 | Programming Fundamentals | 3 | Every semester |
| CSC 220 | Data Structures | 3 | Fall |
| CSC 333 | Computational Data Science | 3 | Spring |
| CSC 340 | Artificial Intelligence | 3 | Fall |
| CSC 350 | Database Systems | 3 | Fall |
Mathematics and statistics (22 credits)
| Course | Title | Credits | Offered |
|---|---|---|---|
| MAT 221 | Statistics | 3 | Every semester and summer |
| MAT 241 | Calculus I | 4 | Fall |
| MAT 242 | Calculus II | 3 | Spring |
| MAT 321 | Mathematical Statistics I | 3 | Fall, from Fall 2028 |
| MAT 322 | Mathematical Statistics II (Statistical Modeling) | 3 | Spring, from Spring 2029 |
| MAT 332 | Discrete Mathematics | 3 | Fall |
| MAT 343 | Linear Algebra | 3 | Spring, from Spring 2028 |
Applied data science (7 credits)
| Course | Title | Credits | Offered |
|---|---|---|---|
| ART 362 | Data Visualization | 3 | Every other year |
| DAT 3XX | Applied Data Analytics (new course) | 3 | Fall, from Fall 2027 |
| DAT 498 | Senior Capstone (new course) | 1 | Spring, from Spring 2030 |
Data science electives (6 credits; choose two)
| Course | Title | Credits |
|---|---|---|
| CSC 210 | Advanced Authoring for the Web | 3 |
| CSC 375R | Special Topics in Computer Science | 3 |
| BUS 355 | Data Science (for Business) | 3 |
| ECN 210 | Principles of Microeconomics | 3 |
| ECN 490 | Econometrics | 3 |
| POL 233 | Comparative Politics (prerequisite: POL 203) | 3 |
| PSY 435 | Psychometrics: Tests & Measures | 3 |
Students not ready for Calculus I begin with MAT 115 College Algebra and MAT 116 Trigonometry, the Calculus I prerequisites. MAT 115 also satisfies the SVU Core mathematics requirement.
Electives taught outside Mathematics and Computer Science are available online only if their programs offer them online. Online students can always choose CSC 210 or CSC 375R.
New courses
DAT 3XX Applied Data Analytics (3 credits)
Prerequisites: MAT 221 and CSC 120. Offered: every fall. The course number has not yet been assigned.
Statistics courses teach methods on clean textbook data. Real data are messy, and most of an analyst’s time goes into getting them ready. This course teaches students to prepare real data and to use the core tools of applied modeling, then has each student apply them to a question from a field of their choosing.
| Part | Weeks | Content |
|---|---|---|
| Tools | 1–8 | Importing data from files, spreadsheets, and databases; tidy data, reshaping and joining tables; data cleaning (missing values, inconsistent coding, outliers, validation); exploratory summaries and graphics; multiple linear regression by ordinary least squares, including assumptions, diagnostics, categorical predictors, and interpretation; exploratory factor analysis; reproducible workflow. |
| Workshop | 9–15 | Each student brings a question and data set from their chosen field, cleans and analyzes the data, presents drafts for critique, and delivers a final written report and presentation for a non-specialist audience. |
Students who complete the course will be able to:
- Import, reshape, join, and clean real data sets, documenting each step so the analysis can be reproduced.
- Fit, check, and interpret multiple linear regression models.
- Carry out and interpret an exploratory factor analysis.
- Complete an analysis of data from their own discipline and communicate the results clearly and honestly.
DAT 498 Senior Capstone (1 credit)
The data science major’s own capstone, taken in the final spring. It requires CSC 333 and DAT 3XX.
When courses are offered
X = offered. Online students take these same sections. MAT 221 is also offered every summer. Each course keeps the same term every year, so the pattern continues past Fall 2030.
| Course | Sp 27 | Fa 27 | Sp 28 | Fa 28 | Sp 29 | Fa 29 | Sp 30 | Fa 30 |
|---|---|---|---|---|---|---|---|---|
| CSC 110 Authoring for the Web | X | X | X | X | X | X | X | X |
| CSC 120 Programming Fundamentals | X | X | X | X | X | X | X | X |
| CSC 220 Data Structures | X | X | X | X | ||||
| CSC 333 Computational Data Science | X | X | X | X | ||||
| CSC 340 Artificial Intelligence | X | X | X | X | ||||
| CSC 350 Database Systems | X | X | X | X | ||||
| MAT 221 Statistics | X | X | X | X | X | X | X | X |
| MAT 241 Calculus I | X | X | X | X | ||||
| MAT 242 Calculus II | X | X | X | X | ||||
| MAT 321 Mathematical Statistics I | X | X | X | |||||
| MAT 322 Mathematical Statistics II | X | X | ||||||
| MAT 332 Discrete Mathematics | X | X | X | X | ||||
| MAT 343 Linear Algebra | X | X | X | |||||
| ART 362 Data Visualization | X | X | ||||||
| DAT 3XX Applied Data Analytics | X | X | X | X | ||||
| DAT 498 Senior Capstone | X | |||||||
| CSC 210 Adv. Authoring for the Web (elective) | X | X | X | X |
ART 362 runs every other year and the Art program sets the term; it is shown here in Fall 2027 and Fall 2029.
Sample four-year plan
For an online student who starts in Spring 2027 at College Algebra. Only the major’s courses are shown; the remaining credits each term are SVU Core courses and electives.
| Term | Major courses |
|---|---|
| Spring 2027 | MAT 115 College Algebra · MAT 116 Trigonometry · CSC 110 · CSC 120 |
| Summer 2027 | MAT 221 Statistics |
| Fall 2027 | MAT 241 Calculus I · MAT 332 Discrete Mathematics |
| Spring 2028 | MAT 242 Calculus II · MAT 343 Linear Algebra |
| Fall 2028 | CSC 220 Data Structures · MAT 321 Mathematical Statistics I |
| Spring 2029 | MAT 322 Mathematical Statistics II · CSC 333 Computational Data Science |
| Fall 2029 | DAT 3XX Applied Data Analytics · ART 362 Data Visualization |
| Spring 2030 | DAT 498 Senior Capstone · two data science electives |
| Fall 2030 | CSC 340 Artificial Intelligence · CSC 350 Database Systems |
The student finishes in four years, with no more than two major courses in any semester apart from the 1-credit capstone. The order follows the prerequisites: Discrete Mathematics comes before Data Structures and Linear Algebra, Calculus I comes before Mathematical Statistics, and the capstone follows CSC 333 and DAT 3XX.
Planned timeline
- Spring 2027: soft launch. Students may declare the major, and online students begin in the entry courses: MAT 115, MAT 116, CSC 110, and CSC 120.
- Fall 2027: DAT 3XX Applied Data Analytics first offered.
- Fall 2028: MAT 321 first offered, with MAT 322 following in Spring 2029.
- Spring 2030: DAT 498 Senior Capstone first offered.
- Fall 2030: first four-year graduates, for students who start in Spring 2027 at the College Algebra level.
Questions
I am one of the faculty proposing this program, along with colleagues in Mathematics, Computer Science, and Design. I am glad to talk with prospective students about it: jared.mcbride@svu.edu.