Feb 07, 2023  
2020-2021 Academic Bulletin 
2020-2021 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. The program consists of a core set of data science, computer science, mathematics, and statistics courses and the choice of one track 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 - 124

Cognates - 12-16

The cognate requirements include a course addressing ethics, a course about human behavior/thinking, and a course with the scientific method.

Note: Three of the four cognate course requirements also count for general education requirements.

Application Area - 12+

At least 12 credits of courses in an area of application of data science. These courses complete a minor, a concentration, a certificate, or the educational goal of pre-medicine. Course plans are available for the following areas: Accounting, Behavioral Sciences, Biology, Data-driven Development, Finance, Innovation & Entrepreneurship, Marketing, Physics, Pre-medicine, and Public Health.

Choose one area (pre-medicine students are not required to complete a concentration):

Behavioral Sciences - 12

This application area requires 12 credits beyond the cognate plus 3 elective credits from Behavioral Sciences.

Biology - 15

This application area requires 15 credits beyond cognate courses.

Public Health - 15

The application area requires 15 credits beyond cognate and core courses.

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.
  • Know what questions can be asked within an appropriate ethical, legal, and Christian framework.
  • Respect individual rights and privacy when collecting and handling human data.
  • Consider the implications of decisions made using data.

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