Data Science (B.S.)

Location(s): Main Campus

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Program Summary

The Bachelor of Science in Data Science integrates essential computing, mathematical, and statistical skills vital for modern data-centric tasks encompassing data processing, analysis, and presentation. This program empowers students to engage in data-driven decision making across diverse interdisciplinary contexts, utilizing computationally intensive approaches.

At its core, the curriculum emphasizes foundational computing and data analysis knowledge, covering data structures and algorithms. Graduates will adeptly construct tailored solutions for complex problems, mastering skills such as data acquisition, management, governance, and analysis. The program’s focus spans probability, statistics, modeling, machine learning, data analytics, and data visualization.

The goal is to equip graduates not only with technical expertise but also with adaptable problem-solving abilities, cultivating a cohort capable of innovation and meaningful contributions to the dynamic field of data science.

Program Information

All courses must be passed with a “C” (2.0) or better to be counted in the major.
Students seeking the Data Science major are strongly encouraged to pursue a minor in one of the Data Science domains.

General Education Requirement (30-35 Credits)

See General Education for course options.

Core Course Requirements (12-15 Credits)
Breadth Area Requirements (16-20 Credits)

Please note: All students must complete a minimum of 30 credits between the Core and Breadth Area requirements.

Bachelor of Science (B.S.) Degree Requirement (12 Credits*)

Bachelor of Science Degree - Math or Science minimum requirement 12 Credits

* Note: A portion of the B.S. requirements may be met by fulfilling the General Education Core and Breadth Areas. Because of this, these credits are typically not counted in the degree total at the bottom of page. Some students majoring in non-science fields may need additional math/science courses to reach the minimum 12 credits. Consult your Student Success Advisor for details.

Data Science Curriculum (75-77 Credits)

Data Science Core (51 Credits)

ANLY 2500 - Introduction to Business Analytics 3 Credit(s)
ANLY 4100 - Data Analytics I 3 Credit(s)
ANLY 4110 - Data Analytics II 3 Credit(s)
CS 1400 - Fundamentals of Programming 3 Credit(s)
CS 1410 - Object Oriented Programming 3 Credit(s)
CS 2420 - Introduction to Algorithms and Data Structures 3 Credit(s)
CS 4200 - Big Data Analytics 3 Credit(s)
CSCY 2400 - Technology and Ethics 3 Credit(s) (can be used to fulfill General Education Social & Behavioral Sciences requirement)
MATH 1210 - Calculus I 4 Credit(s) * (can be used to fulfill General Education Quantitative Literacy requirement)
MATH 1220 - Calculus II 4 Credit(s)
MATH 2170 - Applied Linear Algebra 3 Credit(s)
MATH 3150 - Applied Statistics 3 Credit(s)
MATH 3190 - Fundamentals of Data Science 3 Credit(s)
MATH 3700 - Probability and Statistics 4 Credit(s)
MATH 4810 - Data Science Capstone Project 3 Credit(s)

Note

* Students who are not prepared for this math course may need additional math credits/preparation. Please consult your Student Success Advisor for more information.

Support Courses (12-14 Credits)

Foundational Statistics Course

Complete one of the following:

MATH 1040 - Statistical Inference 4 Credit(s)
MATH 1190 - Introduction to Data Science 3 Credit(s)

Additional Mathematics Course

Complete one of the following:

CS 2300 - Discrete Structures 3 Credit(s)
MATH 2210 - Calculus III 4 Credit(s)
MATH 3770 - Mathematical Modeling 3 Credit(s)

Data Management Course

Complete one of the following:

ANLY 3250 - Data Management and Organization 3 Credit(s)
CSCY 3200 - Database Design and Management 3 Credit(s)

Machine Learning Course

Complete one of the following:

CS 4150 - Machine Learning 3 Credit(s)
ME 3600 - Introduction to AI and Machine Learning for Engineers 3 Credit(s)

Data Domain (12 Credits)

Complete at least 12 credits from one of the following areas. All credits must come from the same course prefix and at least 6 credits must be upper division (numbered 3000-4999):

ACCT (Accounting)
BIOL (Biology)
CE (Civil Engineering)
CS (Computer Science)
CHEM (Chemistry)
ECON (Economics)
EE (Electrical Engineering)
ENGR (Engineering)
FIN (Finance)
GEO (Geology)
MATH (Mathematics)
ME (Mechanical Engineering)
MKTG (Marketing)
PHYS (Physics)
PSY (Psychology)
Classes from prefixes/areas not listed may be accepted with departmental approval

Free Electives (8-24 Credits)

Total elective credit is determined by General Education courses taken, degree type selected, additional credits earned, and any additional math or other prerequisite courses needed. For a bachelor’s degree, students must earn a minimum of 40 upper-division credits (courses numbered 3000-4999). Students may need to select additional upper-division courses not included in their major requirements in order to reach this minimum. Please consult your Student Success Advisor for details.

Recommended Electives

Although not required, the Department of Mathematics recommends that Data Science majors complete the following electives:

CS 2450 - Software Engineering 3 Credit(s)
CSCY 2000 - Web Development 3 Credit(s)

Total Credits, B.S. Degree: 120

Program Learning Outcomes

Per the ABET CAC outcomes for programs named Data Science, graduates of the program will have an ability to:

Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
Communicate effectively in a variety of professional contexts.
Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
Apply theory, techniques, and tools throughout the data science lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.