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Academic Catalog

Data Science

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2025-2026 Edition

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Degree: B.S., Mathematics
Department of Mathematics

The concentration in Data Science teaches principles and builds skills in the science of how and why we use data. Decision-making across all levels is increasingly shifting away from subjective human judgment and expert opinion. It is being replaced by superior evidence-based approaches driven by data and analytical models. Data Science is the discipline concerned with gathering and manipulating large volumes of data, developing statistical models to gain critical insights from it, understanding the behavior of complex systems through simulation, and making non-trivial decisions optimally, often in response to quickly changing conditions. The track in Data Science is designed to provide students with the coursework to succeed in this area.

Student Learning Outcomes

1. Students will learn the central ideas and techniques of various areas of mathematics. 

2. Students will analyze, construct, and present mathematical and logical arguments.

3. Students will develop problem-solving abilities.

4. Students will discover mathematical patterns and formulate conjectures by exploration and experimentation.

5. Students will represent quantitative information by means of appropriate symbols, graphs, equations, or tables.

Major Requirements

Course List
Code Title Credits
DATA 101Introduction to Data Science3
DATA 219Foundations for Data Science3
STAT 180Introduction to Statistics3
STAT 280Statistical Methods3
STAT 320Applied Regression Analysis3
STAT 381Probability and Statistical Inference3
CPSC 225Software Development Tools1
DATA 350Applications of Databases4
MATH 121Calculus I4
MATH 122Calculus II4
MATH 201Introduction to Discrete Mathematics3-4
or CPSC 284 Applied Discrete Mathematics
MATH 300Linear Algebra4
Select one of the following:3
DATA 401
Applied Machine Learning
or DATA 419
Data Mining
Select one of the following:3
DATA 370A9
Information Storage and Retrieval Systems
or DATA 420
Modeling and Simulation
Select one of the following:3
DATA 402
Analytics Applications and Development
DATA 470D3
Natural Language Processing
DATA 470
Advanced Special Topics in Data Science (Course must be at least 3 credits.)
DATA 470D1
Deep Learning in Python
DATA 491
Indiv Study in Data Science (Course must be at least 3 credits.)
STAT 382
Probability and Statistical Inference
STAT 420
Applied Multivariate Statistics
STAT 461
Topics in Statistics
Total Credits47-48

General Education Requirements

The general education requirements for Bachelor of Arts/Bachelor of Science degrees apply to all students who are seeking to earn an undergraduate B.A., B.S. or B.S.Ed. degree.

Students seeking a Bachelor of Liberal Studies degree have a separate set of BLS general education requirements.

Electives

Elective courses are those that are not needed to fulfill a general education requirement or major program requirement but are chosen by the student to complete the 120 credits required for graduation with a B.A./B.S./B.S.Ed. degree or the BLS degree. These courses may be taken graded or pass/fail (or S/U in the case of physical education and 100-level dance). No student in a regular B.A./B.S./B.S.Ed. program may count more than 60 credits in a single discipline toward the 120 credits required for graduation.

Total Credits Required for the Degree: 120 credits

Plan of Study

This suggested plan of study should serve as a guide to assist students when planning their course selections. It is not a substitute for a student's Degree Evaluation or the Program Requirements listed for this major in the catalog. Academic planning is the student's responsibility, and course selections should be finalized only after speaking with an advisor. Students should familiarize themselves with the catalog in effect at the time they matriculated at the University of Mary Washington. Students should also familiarize themselves with general education requirements which can be fulfilled through general electives as well as major/minor course requirements. Course requirements and sequencing may vary with AP, IB, CLEP, Cambridge or previous coursework, transfer courses, or other conditions. To be considered full-time, an undergraduate student must be enrolled in 12 or more credits for the semester. 

Plan of Study Grid
Freshman
FallCredits
DATA 101 Introduction to Data Science 3
FSEM 100 First-Year Seminar 3
MATH 121 Calculus I 4
General Education Courses 6
 Credits16
Spring
MATH 122 Calculus II 4
MATH 201
or CPSC 284
Introduction to Discrete Mathematics
or Applied Discrete Mathematics
3
STAT 180 Introduction to Statistics 3
General Education Courses 6
 Credits16
Sophomore
Fall
DATA 219 Foundations for Data Science 3
STAT 280 Statistical Methods 3
General Education Courses 9
 Credits15
Spring
CPSC 220 Computer Programming and Problem Solving 4
MATH 300 Linear Algebra 4
General Education Courses 6
 Credits14
Junior
Fall
CPSC 225 Software Development Tools 1
DATA 350 Applications of Databases 4
STAT 381 Probability and Statistical Inference 3
General Education Courses or Electives 9
 Credits17
Spring
STAT 320 Applied Regression Analysis 3
DATA or STAT 300 or 400-Level Elective 3
General Electives 9
 Credits15
Senior
Fall
DATA 370
or DATA 420
Special Topics in Data Science
or Modeling and Simulation
3
DATA 401
or DATA 419
Applied Machine Learning
or Data Mining
3
General Electives 6
 Credits12
Spring
MATH 305 Mathematics as a Profession 1
General Electives 14
 Credits15
 Total Credits120

Data Science Program

Julius N. Esunge, Chair, Mathematics Department

Affiliated Faculty

Prashant Chandrasekar, Computer Science
Stephen Davies, Computer Science
Melody B. Denhere, Mathematics
Christopher J. Garcia, Business
Debra L. Hydorn, Mathematics
Jessica Zeitz, Computer Science


 

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