Statistics Minor
Contact
Ryan Hedstrom
Assistant Academic Director, School of Mathematics and Data Science
rhedstrom@une.edu
Mission
The Minor in Statistics is to equip students with a comprehensive understanding of statistical principles and methodologies, fostering the ability to analyze and interpret data effectively across diverse disciplines.
Program Description
The Minor in Statistics will provide students with a solid foundation in statistical inference and data interpretation. The minor complements a wide range of disciplines, such as biology, health, social sciences and business, by equipping students with the tools necessary to analyze and make informed decisions based on data.
Program Goals
The minor in Statistics will:
- Train students in a range of foundational and modern statistical methods.
- Develop the ability to critically analyze data and make evidence-based decisions.
- Prepare students to use statistical software in any discipline and in a range of careers
Transfer Credit
See Undergraduate Admissions for more information.
Admissions
See Undergraduate Admissions for more information.
Financial Information
Tuition and fees for subsequent years may vary. Other expenses include books and housing. For more tuition and fee information, please consult this catalog’s Financial Information section.
Curricular Requirements
A student with a major in another program may minor in Statistics with the approval of the Associate Director of the School of Mathematics and Data Science. A minimum of 19 hours of approved course credit is required.
Students wishing to declare a Statistics minor should complete a course plan in consultation with a Mathematics and Data Science faculty member.
Students may earn a Minor in Statistics by completing the following:
| Code | Title | Hours |
|---|---|---|
| Program Required Courses | ||
| MAT 150 | Statistics for Life Sciences | 3 |
| MAT 190 | Calculus I | 4 |
| MAT 220 | Linear Algebra | 3 |
| STS 220 | Probability | 3 |
| STS 250 | Statistical Methods I: Linear Models | 3 |
| Select one of the following: | 3 | |
| Machine Learning | ||
| Deep Learning | ||
| Data Mining | ||
| Topics in Data Science | ||
| Principle of Study Design | ||
| Statistical Computing | ||
| Time Series Analysis | ||
| Bayesian Methods | ||
| Total Hours | 19 | |
Please note: While some courses can fulfill both core and program requirements, the credits earned do not count twice towards the minimum total required credits for the degree.