May 29, 2024  
2023-2024 Academic Bulletin 
2023-2024 Academic Bulletin [ARCHIVED CATALOG]

Data Science BS

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Data Scientists provide insight and predictions from data. They work in research labs, startups, corporations, universities, governments, and nonprofits. A degree in data science provides students with skills for acquiring, managing, visualizing, mining, and modeling data. Students in this program learn tools and techniques for working with Big Data and using machine learning for making predictions. They consider implications of decisions made with data for human rights and privacy from an appropriate ethical, legal, and Christian framework. The program consists of a core set of data science, computer science, mathematics, and statistics courses and the choice of a minor or certificate where students learn discipline-specific techniques and issues.

The Data Science BS program is shared between the Department of Mathematics in the College of Arts and Sciences and the Department of Computing in the College of Professions.

Total Credits - 120

Application Area - 12+

Completion of a minor (or second major) in any area or an Innovation & Entrepreneurship Certificate. See for recommended courses in common areas such as Accounting, Behavioral Sciences, Biology, Finance, Marketing, Physics, and Public Health.

Additional Requirements

No grade lower than C- may be counted toward major, cognate, or application area requirements.

General Education (Andrews Core Experience)

Students must fulfill all Bachelor’s Degree requirements listed in the Andrews Core Experience .

Student Learning Outcomes

Graduates of this program will:

  • Know how to collect, clean, anonymize, and manage data from different sources in a convenient system for analysis.
  • Understand issues and solutions for storing, managing, and analyzing large-scale data sets.
  • Apply techniques to understand, visualize, identify, and communicate trends in data.
  • Identify appropriate tools, algorithms, mathematical techniques and models to perform desired analysis; understanding limitations and pitfalls.

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