Data Analytics A.B.
Chair Department of Mathematics and Statistics: Associate Professor Beuerle
Associate Chair: Professor K. Doehler
The Department of Mathematics and Statistics offers programs leading to the Bachelor of Arts or Bachelor of Science degree with a major in Applied Mathematics, Data Analytics, Mathematics or Statistics. With an A.B. in Data Analytics students will be exposed to methods and issues related to managing and analyzing data. This particular degree is meant to be interdisciplinary in nature with requirements in mathematics, statistics, computer science, and a requirement of a supporting additional major or minor.
Major Requirements
Foundations: 28 sh
| STS 2120 | Statistics in Application | 4 sh |
| STS 2300 | Introduction to Data Analytics | 4 sh |
| STS 2320 | Statistical Modeling | 4 sh |
| STS 3270 | Statistical Computing for Data Management | 4 sh |
| STS 3300 | Statistical Methods for Data Analytics | 4 sh |
| CSC 1100 | Data Science and Visualization | 4 sh |
| MTH 2300 | Mathematical Methods for Data Analytics | 4 sh |
Capstone Requirement: 4 sh
Students will be required to complete either
/sts-4980'>STS 4980 or
approved capstone focusing on data analytics from another major or minor.
Electives: 8 sh
Take 2 of the following courses; at least 4 sh must be taken at the 3000-level or higher.
For other courses in this category, seek approval from the Chair of the Department of Mathematics and Statistics.
Additional Requirement
Students must complete a full minor or a second major in another discipline. A major in statistics with a concentration in data analytics or minor in computer science, data science, or statistics does not count toward fulfillment of this requirement.
Total Credit Hours: 40
Program Outcomes
Data analytics promotes quantitative critical thinking skills that should serve the student in the rest of their studies at Elon and in their professional and educational careers after graduation. The data analytics major at Elon draws on strengths from other areas of the university. Upon graduation, students should be able to:
Apply data analytics concepts to solve real-world problems and interpret results.
Effectively use at least three statistical software or programming languages with an emphasis on computational reproducibility.
Reflect on ethical implications of data-based decisions and technologies.
Clearly and accurately communicate data analytics concepts, methods, and results to address specific audiences (technical and non-technical).