Data science provides opportunities for work and service in numerous fields. It enables critical thinking and problem solving, while leading to rewarding careers that address practical contemporary issues.
Bethel College offers both a major and a minor in data science.
What is data science?
Data science is the science of planning for, acquisition, management, analysis of, and inference from data. Data science is an interdisciplinary field based primarily upon the disciplines of mathematics, statistics, and computer science. In addition, data scientists must have an understanding of the field from which their data come. Students of data science need a knowledge of how to organize, visualize, and draw conclusions from data as well as hands on experience in applying that knowledge through computing with data in their field of interest.
What can you do with a major in data science from Bethel?
- Data Scientist: Collect, clean, and analyze large datasets; build predictive models and machine learning algorithms; communicate findings to guide business strategy and organizational decisions.
- Data Analyst: Examine data to identify trends, create reports and dashboards, and support evidence-based decision-making across departments.
- Data Engineer: Design and maintain the infrastructure and pipelines that enable data scientists and analysts to access reliable, high-quality data.
- Business Intelligence (BI) Analyst: Bridge business data and actionable strategy by building dashboards and reports that help organizations stay competitive.
- Machine Learning Engineer: Take predictive models further by deploying and maintaining scalable machine learning systems in real-world environments.
- Market Research Analyst: Analyze consumer behavior and market trends to shape marketing strategies and product decisions.
- Data Ethics Officer: Guide organizations in the responsible and ethical use of data, AI, and analytics—a role that aligns directly with Bethel’s emphasis on ethical reasoning and Christian social ethics.
Median Wage & Job Outlook
Source: Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Data Scientists. Retrieved 2026-09-17.
- Median Wage: $112,590 per year (May 2024)
- Job Outlook: 34% growth from 2024 to 2034 (much faster than average)
- Annual Openings: About 23,400 openings projected each year, on average, over the decade
Data Science Major
What You’ll Learn
Data Science Major Learning Goals:
- An understanding of the mathematical foundations of data science
- Proficiency in the use of programming languages that are widely employed in data science
- An understanding of data structures and data management methods as applied to data sets from a variety of fields
- Facility in the use of statistical analysis and modeling methods and their application to data from a variety of fields
- An ability to collaborate and communicate effectively with colleagues or clients with whom they work
- An understanding of the ethical issues that arise in the practice of data science
Coursework
Total credit hours: 50-51 (Upper level: 20-21 Lower level: 22 Interdisciplinary : 8)
Required courses for the Data Science Major:
Courses in mathematics:
- Calculus I (4 hours)
- Linear Algebra (3 hours)
- Discrete Mathematics (3 hours)
Courses in statistics:
- Applied Statistics (4 hours)
- Data Wrangling and Visualization (3 hours)
- Advanced Statistics (4 hours)
- Machine Learning Fundamentals (3 hours)
Courses in software development:
- Introduction to Computer Programming (4 hours)
- Intermediate Programming: Data Structures (4 hours)
- Introduction to Artificial Intelligence (3 hours)
- Database Management Systems (3 hours)
Seminar or internship:
- Data Science Seminar (1 hour)
– or –
Data Science Internship (2 hours)
Required supporting coursework:
- Christian Social Ethics (3 hours)
Data Science Minor
Students must complete 16 credit hours selected from courses listed in the Data Science major.
Coursework
At least one course in mathematics:
- Calculus I (4 hours)
- Linear Algebra (3 hours)
- Discrete Mathematics (3 hours)
At least two courses in statistics:
- Applied Statistics (4 hours)
- Data Wrangling and Visualization (3 hours)
- Advanced Statistics (4 hours)
- Machine Learning Fundamentals (3 hours)
At least one course in software development:
- Introduction to Computer Programming (4 hours)
- Intermediate Programming: Data Structures (4 hours)
- Introduction to Artificial Intelligence (3 hours)
- Database Management Systems (3 hours)
