Degree
Bachelor of Science with a major in Data ScienceMission
The Bachelor of Science with a major in Data Science program inspires students to become innovators who make impactful contributions through data analysis, modeling, computation, and simulation. The program fosters flexible and creative approaches for problem solving and the ability to gain insights about complex relationships and interdependencies, and to describe and communicate these insights for prediction and decision making.
Major Description
In recent years the explosion of data in a wide range of fields has created a wealth of opportunities for data science professionals, and the demand for people with the right skills continues to grow. The B.S. with a major in Data Science program at 51С»Æ³µgives students the opportunity to apply their passion for mathematical modeling and computing to problems involving the analysis of data and the design of models for extracting information, making predictions, and decision-making.
Beginning with foundational mathematics, statistics, and computing, students will develop techniques in visualization, machine learning, and data mining.
Industry partnerships with local employers provide opportunities for students to apply these techniques and refine their expertise through project-based learning experiences throughout the curriculum as well as in a senior practicum.
Curricular Requirements
| CAS Core Requirements | Credits |
|---|---|
| Total Credits | 42 |
| Program Required Courses | Credits |
|---|---|
| DSC 110 – Survey of Software Tools | 1 |
| DSC 130 – Exploring Data | 3 |
| DSC 225 – Programming I or MAT 225 – Computer Programming w/MATLAB | 3 |
| DSC 260 – Data Visualization | 3 |
| DSC 301 – Introduction to Database Design/SQL | 3 |
| DSC 344 – Machine Learning | 3 |
| DSC 410 – Data Mining or DSC 420 – Predictive Modeling or DSC 490 – Topics in Data Science | 3 |
| DSC 480 – Data Science Practicum | 3 |
| MAT 120 – Statistics or MAT 150 – Statistics for Life Sciences | Credits Fulfilled by Core Requirements |
| MAT 190 – Calculus I | 3 |
| MAT 220 – Linear Algebra | 3 |
| Total Credits | 28 |
| Select Four (4) of the Following: | Credits |
|---|---|
| DSC 205 – Introduction to Data Analysis and Modeling | 3 |
| DSC 270 – Data Structures and Algorithms | 3 |
| DSC 325 – Programming II | 3 |
| GIS 364 – Spatial Data Analysis | 3 |
| MAT 195 – Calculus II | 3 |
| MAT 212 – Discrete Mathematics | 3 |
| MAT 323 – Applied Regression Analysis | 3 |
| MAT 340 – Graph Theory with Applications | 3 |
| MAT 405 – Introduction to Numerical Analysis | 3 |
| Total Credits | 12 |
| Open elective credits (as needed to reach 120 credits) | Variable |
| Minimum Total Required Credits | 120 |
|---|
Learning Outcomes
Students successfully completing the B.S. with a major in Data Science will:
- Develop, test, and deploy mathematical and statistical models for data analysis, prediction, and decision making
- Use current field-standard digital tools for data management, manipulation, organization, analysis, and visualization
- Effectively communicate quantitative information to technical and non-technical audiences orally, in writing, and through visual formats
Minors
A student with a major in another program may minor in Data Science with the approval of the Academic Director.
Students wishing to declare a Data Science minor should complete a course plan in consultation with a Mathematical Sciences faculty member.
Students may earn a Minor in Data Science by completing the following:
| Program Required Courses | Credits |
|---|---|
| DSC 130 – Exploring Data | 3 |
| DSC 225 – Programming I or MAT 225 – Computer Programming w/MATLAB | 3 |
| DSC 260 – Data Visualization | 3 |
| DSC 344 – Machine Learning | 3 |
| MAT 120 – Statistics or MAT 150 – Statistics for Life Sciences | 3 |
| One (1) Program Specific Elective | 3–4 |
| Minimum Total Required Credits | 18 |
|---|
| Program Specific Electives | Credits |
|---|---|
| DSC 205 – Introduction to Data Analysis and Modeling | 3 |
| DSC 301 – Introduction to Database Design/SQL | 3 |
| DSC 410 – Data Mining | 3 |
| DSC 420 – Predictive Modeling | 3 |
| DSC 490 – Topics in Data Science | 3 |
| GIS 364 – Spatial Data Analysis | 3 |
Honors Program
At this time, Data Science does not offer Honors Program.
Transfer Credit
Courses previously completed at another accredited college can be transferred to this degree program beginning in Fall 2020. Transferred mathematics courses must be reasonably close in scope and content to the mathematics courses offered at 51С»Æ³µin order to count as exact equivalents. Otherwise, they will transfer as general electives.
All Science/Math courses previously completed must be no older than five (5) years.
See Undergraduate Admissions for more information.
Admissions
See Undergraduate Admissions for more information.
Financial Information
Tuition and Fees
Tuition and fees for subsequent years may vary. Other expenses include books and housing. For more information regarding tuition and fees, please consult the Financial Information section of this catalog.
Notice and Responsibilities Regarding this Catalog
This catalog outlines the academic programs, degree criteria, policies, and events of the University of 51С»Æ³µ for the 2024–2025 academic year and serves as the official guide for academic and program requirements for students enrolling at the University during the Summer of 2024, Fall 2024, and Spring 2025 semesters.
The information provided is accurate as of its publication date on April 26, 2024.
The University of 51С»Æ³µ reserves the right to modify its programs, calendar, or academic schedule as deemed necessary or beneficial. This includes alterations to course content, class rescheduling, cancellations, or any other academic adjustments. Changes will be communicated as promptly as possible.
While students may receive guidance from academic advisors or program directors, they remain responsible for fulfilling the requirements outlined in the catalog relevant to their enrollment year and for staying informed about any updates to policies, provisions, or requirements.