Contact
Hongming Wang, Ph.D.
Associate Director, School of Computer Science and Data Analytics
hwang7@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.
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 Computer Science and Data Analytics. A minimum of nineteen (19) hours of approved course credit is required.
Students wishing to declare a Statistics minor should complete a course plan in consultation with a Computer Science and Data Analytics faculty member.
Students may earn a Minor in Statistics by completing the following:
| Program Required Courses | Credits |
|---|---|
| MAT 150 – Statistics for Life Sciences | 3 |
| MAT 190 – Calculus I | 4 |
| MAT 220 – Linear Algebra | 3 |
| STS 220 – Probability | 3 |
| STS 250 – Statistical Method I: Linear Models | 3 |
| Total Credits | 16 |
| Select One (1) of the Following Courses: | Credits |
|---|---|
| DSC 344 – Machine Learning | 3 |
| DSC 360 – Deep Learning | 3 |
| DSC 410 – Data Mining | 3 |
| DSC 490 – Data Science Topics | 3 |
| STS 210 – Principles of Study Design | 3 |
| STS 280 – Statistical Computing | 3 |
| STS 360 – Time Series Analysis | 3 |
| STS 400 – Bayesian Methods | 3 |
| Total Credits | 3 |
| Minimum Total Required Credits | 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.
Learning Outcomes
- Build, deploy, and evaluate a variety of effective statistical models and inference procedures
- Effectively manage, process, and organize data and workflows
- Judge the soundness of statistical approaches and analyses
- Effectively use statistical software
Transfer Credit
Courses completed at another accredited college can be transferred to this degree program. Transferred courses must be reasonably close in scope and content to the required courses offered at É«ÏãÊÓƵ in order to count as exact equivalents. Otherwise, they may transfer as general electives. All courses completed must be no older than five (5) years.
Other restrictions apply. See Undergraduate Admissions for more information.
Notice and Responsibilities Regarding this Catalog
This catalog outlines the academic programs, degree criteria, policies, and events of the É«ÏãÊÓƵfor the 2025–2026 academic year and serves as the official guide for academic and program requirements for students enrolling at the University during the Summer of 2025, Fall 2025, and Spring 2026 semesters.
The information provided is accurate as of its publication date on April 30, 2025.
The É«ÏãÊÓƵreserves the right to modify its programs, calendar, or academic schedule as deemed necessary or beneficial. This includes alterations to course content, class rescheduling, cancellations, or any other academic adjustments. Changes will be communicated as promptly as possible.
While students may receive guidance from academic advisors or program directors, they remain responsible for fulfilling the requirements outlined in the catalog relevant to their enrollment year and for staying informed about any updates to policies, provisions, or requirements.