Bachelor of Science in Data Science and Mathematics

4 Years On Campus Bachelors Program

Concordia University Wisconsin

Program Overview

The Bachelor of Science in Data Science and Mathematics at Concordia University Wisconsin is a 120-credit, on-campus STEM program designed for students who enjoy mathematics, statistics, analytical problem-solving, and working with large volumes of data. Students develop skills in data analysis, visualization, statistics, coding, algorithms, and mathematical reasoning while applying these skills to areas such as public health, sports and human performance, environmental science, technology, finance, and research.

Curriculum Structure:

Year 1: Students establish their quantitative foundation through MATH 2010 – Calculus I, MATH 2050 – Statistics I, and DSAA 2000 – Introduction to Data Science and Analysis. They begin learning how data is collected, integrated, analyzed, and modeled while building the mathematical skills needed for later data science and statistics coursework.

Year 2: Students move deeper into computational data analysis with courses such as DSAA 3000 – Data Mining and Predictive Analysis, DSAA 3100 – Visual Analytics, and CSC 2050 – Coding II – Algorithms. This stage develops their ability to identify patterns, make predictions, apply algorithms, and communicate analytical insights through visualization.

Year 3: Students strengthen their technical capabilities through CSC 4400 – Coding III – Data Structures, CSC 3030 – Database Fundamentals, and MATH 3050 – Statistics II in the Data Science concentration. These subjects develop skills in organizing and managing data, statistical analysis, and computational approaches to increasingly complex datasets.

Year 4: Students complete advanced study and bring their knowledge together through DSAA 4000 – Senior Research Seminar, where they undertake a capstone research project and apply data-science tools to interpret, analyze, and integrate data. Students following the Mathematics concentration instead progress through subjects including MATH 3250 – Linear Algebra and Differential Equations and MATH 4410 – Real Analysis, developing deeper mathematical reasoning alongside their data-science foundation.

Focus Areas: Data science, mathematics, statistics, coding, algorithms, data mining, predictive analysis, visual analytics, database fundamentals, mathematical problem-solving, public health, sports and human performance, environmental science, technology, finance, research, and business analytics.

Learning Outcomes: Analyze and interpret data ethically; apply mathematical and data-science problem-solving strategies to real-world applications; select and apply appropriate statistical analysis; identify suitable algorithms and software tools; prove and disprove mathematical conjectures; and communicate solutions through oral, written, and symbolic formats.

Professional Alignment (Accreditation): STEM designated: Concordia University Wisconsin identifies the Data Science and Mathematics bachelor's degree as a STEM-designated program. Program-specific professional accreditation: Not stated on the official CUW program or academic-catalog pages reviewed.

Reputation (Employability Rankings): Official program-specific employability ranking: Not stated on the official CUW program pages reviewed. CUW does state that graduates can enter fields including technology, science, research, finance, insurance, manufacturing, and education, with career options including data scientist, financial analyst, actuary, and research-related roles.

Experiential Learning (Research, Projects, Internships etc.)

Students in the BS in Data Science and Mathematics at Concordia University Wisconsin develop practical skills by working with real data, statistical analysis, algorithms, predictive modelling and visual analytics. The curriculum combines programming and mathematics with applied data-science work, while the DSAA 4000 Senior Research Seminar gives students the opportunity to complete and present a capstone research project; students can also gain workplace experience through the program’s dedicated internship course. These opportunities are supported by faculty expertise in areas such as data science, machine learning, computational science and graph theory:

Data Science Software: DSAA 3000 – Data Mining and Predictive Analysis requires students to use data-mining software to identify patterns and make predictions, giving them direct experience with computational approaches to data analysis.
Visual Analytics Tools: DSAA 3100 – Visual Analytics develops practical experience with methods and tools that combine visual capabilities, computational devices and algorithms to support analytical reasoning.
Programming and Databases: The Data Science concentration includes CSC 2000 – Coding I: Fundamentals, CSC 2050 – Coding II: Algorithms, CSC 3030 – Database Fundamentals, and CSC 4400 – Coding III: Data Structures, giving students hands-on preparation in coding, algorithms, databases and data structures.
Real-World Data Applications: Students apply data-science and mathematical problem-solving strategies to applications including public health, sports and human performance, and environmental science, connecting quantitative techniques with practical domains.
Senior Research Project: DSAA 4000 – Senior Research Seminar provides a capstone research experience in which students study research methods and apply disciplinary tools to interpret, analyze and integrate data before presenting their research project.
Internship Experience: DSAA 4990 – Internship allows students to apply classroom theories, methods and tools at an off-site organization; students complete a minimum of 120 hours on site and produce a paper connecting their coursework with their workplace learning.
Faculty Research: Students benefit from faculty expertise in data science and machine learning, including research in application of linear algebra and graph theory, creating opportunities to engage with current computational and mathematical topics.
Small-Class Mentorship: CUW’s Department of Engineering and Mathematics emphasizes small classes and close faculty mentorship, while its Mathematics and Data Science programs focus on developing real-world problem solvers who use mathematical and computational tools and communicate analytical results to varied audiences.
Collaborative, Applied Learning: The department describes its engineering and mathematics education as including real-world projects, internships and industry partnerships, with students encouraged to apply what they learn to real problems before graduation.

Progression & Future Opportunities

Graduates of the Bachelor of Science in Data Science and Mathematics at Concordia University Wisconsin develop strong quantitative, analytical, statistical, and computational skills for careers in technology, science, research, finance, insurance, manufacturing, healthcare, and education. The program specifically prepares students to extract, visualize, interpret, and communicate information from large datasets while solving real-world problems with mathematical and computational tools.

Typical Job Roles: Data Analyst, Data Scientist, Financial Analyst, Actuary.

Career Development & Industry Connections: CUW combines career guidance, employer-facing resources, and alumni connections to help students translate their analytical skills into professional opportunities:

Career Services: CUW Career Services provides access to Handshake for job listings, internships, and career events, while students can also receive career guidance and use professional-development resources.
Career Advising: The Center for Academic Advising and Career Engagement (CAACE) supports students with career planning, resume and cover-letter reviews, mock interviews, employer spotlights, career assessments, and personalized advising appointments.
Alumni Networking: CUWConnect enables students to connect with alumni in their field for professional mentoring and career connections, extending career support beyond graduation.
Employment & Salary: Not stated on the official CUW Data Science and Mathematics program pages reviewed. CUW identifies career areas and roles for graduates but does not publish a program-specific employment rate or salary figure there.
University–Industry Partnerships: Not specifically stated for the Data Science and Mathematics bachelor's program on the official CUW sources reviewed. CUW does, however, identify technology, science, research, finance, insurance, manufacturing, and education as fields in which DSM graduates work.
Long-Term Accreditation Value: CUW is institutionally accredited by the Higher Learning Commission (HLC), a regional accreditor recognized by the U.S. Department of Education. No separate professional accreditation for the Data Science and Mathematics bachelor's program is stated by CUW.
Graduation Outcomes: CUW's published educational objectives expect Data Science and Mathematics graduates to become real-world problem solvers who use mathematical and computational tools, inferential reasoning, and effectively communicate analytical results to varied audiences. The university also states that graduates may continue into graduate or doctoral study and pursue careers in areas such as research, technology, finance, and analytics.

Further Academic Progression: After completing the bachelor's degree, students can pursue graduate study in areas such as business analytics, computer science, artificial intelligence, mathematics, engineering, law, or medicine, depending on their career goals. At CUW, relevant options include the Master of Science in Business Analytics, which focuses on applied analytics and data-driven decision-making, and the Master of Science in Artificial Intelligence, which prepares graduates for advanced work in AI, machine learning, data science, and applied AI.