Mathematics and Computational Intelligence, M.S.

Program Overview

Bridging Mathematical Rigor with Computational Power for High-Demand Careers.

The MS in Mathematics and Computational Intelligence is a 30-credit hour graduate program designed to prepare students for careers in high-demand, data-intensive fields such as finance, technology, banking, insurance, quantum computing, and education. The program combines advanced training in mathematical thinking with hands-on experience in computational and statistical methods.

To support diverse professional and academic goals, the program offers two pathways to completion: a Coursework-Based MS and a Research-Based MS. The research pathway is especially well-suited for technical professionals seeking career transitions, individuals pursuing a second master’s degree, or those preparing for doctoral study. Both pathways provide scheduling flexibility and opportunities for industry-relevant or research-intensive projects aligned with fields such as AI, fintech, and risk analysis.

The curriculum includes flexible elective choices that allow students to develop expertise in areas such as -but not limited to-algebra, numerical methods, statistics, machine learning, cryptography, and risk analysis. Stackable micro-credentials embedded within the program offer additional certifications in specialized domains, enhancing career advancement and professional recognition.

Key Highlights:

Built-in micro-credentials in AI, cryptography, and risk analytics
Project-based learning with industry applications in tech, finance, and security
Designed for career advancement, whether in tech, academia, or financial sectors
Flexible pathways allowing specialization in AI, applied statistics, or applied analysis, and education, Shape the Future of Mathematics and Computing. Gain the skills employers demand and prepare for leadership roles in cutting-edge industries.

The Program Advisor will assist you in selecting courses that align with your career goals and, if applicable, coordinate with the Graduate School to help establish your thesis committee. You are encouraged to keep your advisor informed of your academic progress and evolving interests throughout the program.

MS Degree in Mathematics and Computational Intelligence (30 Credit Hours)

Master’s by Coursework Pathway

Program Structure

This entails Required Core Courses, electives clustered around the core, and stackable mini credentials which you can acquire as you progress in the pathway. The required core course areas are in

A. Applied algebra,

B. Statistics and Learning,

C. Analysis and Computing,

D. Research, Design Thinking, and Investigations.

Required MATH Courses: 12-15 credit hours

MATH 5310 - Modern Applied Algebra Credit Hours: 3
MATH 5630 - Bayesian Statistics Credit Hours: 3
MATH 5550 - Advanced Numerical Methods Credit Hours: 3
MATH 5950 - Masters’ Project Credit Hours: 3
OR
MATH 5980 - Master’s Thesis Credit Hours: 3-6

MATH or CPTR Electives (15 - 18 Credit Hours)

Electives must be chosen from approved 5000 or 6000-level courses in Mathematics or Computer Science. However, with the explicit approval of the Mathematics Graduate Advisor or the Chair, a maximum of six credit hours of 4000-level courses in Mathematics, Computer Science, or another STEM area can be used as electives, provided a grade of A or B is earned in each of the 4000-level courses.

The following are suggested areas for electives. These are not exhaustive; your advisor will help you to choose electives that best fit your career goals.

Data Science, Applied Statistics & Risk Analytics
Commutative algebra, computational algebra, cryptography.
Applied & Computational Analysis
Management & Computational Decision Science
Artificial Intelligence & Machine Learning
Secondary Math Education (content expertise for High School Teachers)
Quantum computing

Mini-Credentials

The following mini-credentials are structured as stackable micro-credentials within the MS in Mathematics and Computational Intelligence program. Each is designed to enhance employability in specialized domains by formally recognizing students’ competencies in targeted skill areas. Students may chose to be mini-credentialed without earning a full degree.

1. AI & Machine Learning Certification

This credential focuses on core competencies in artificial intelligence, deep learning, and Natural Language Processing and is aligned with the demand for AI specialists across tech, finance, and business intelligence.

Requirements:

A student earns this certification by successfully completing the following three courses and a Research Project.

MATH 5825 - Machine Learning Implementation Credit Hours: 3
MATH 5830 - Deep Learning Implementation Credit Hours: 3
CPTR 4360 -Natural Language Processing
Additional Requirements:

Completion of a research project in AI demonstrating proficiency in applying ML/DL/NLP techniques. This Project could be your MS project.

2. Cryptography & Cybersecurity Badge

This badge certifies expertise in modern cryptographic techniques and cybersecurity principles, ensuring students are prepared for careers in cybersecurity, blockchain, and secure financial transactions.

Requirements:

To earn the Cryptography & Cybersecurity Badge, students must complete the following courses and a Research Project.

MATH 5310 - Modern Applied Algebra Credit Hours: 3
(Covers algebraic structures used in cryptography)
CPTR 5770 - Cryptography I Credit Hours: 3
(Focuses on classical and modern cryptographic techniques, including RSA, elliptic curves, and post-quantum cryptography)
CPTR 5760 - Advanced Network Security and Privacy Credit Hours: 3
Additional Requirements:
- Students must complete a practical security project or cybersecurity case study, such as implementing a cryptographic protocol, or analyzing security vulnerabilities, or blockchain integration. This listing in not exhaustive. Their MS Project can fulfill the Project requirement provided it addresses the above mentioned topics.

MS Degree in Mathematics and Computational Intelligence (30 Credit Hours)

Masters’ by Research Pathway

This path is designed for students seeking a research‑intensive experience and preparation for doctoral studies or applied scholarly work. The pathway adopts the UK and European model of awarding degrees primarily on a substantial thesis while blending this focused research approach with the flexibility of American graduate education.

This pathway is particularly suitable for:

• Industry/government professionals seeking career transitions into teaching or research roles.

• Industry or military personnel who want to convert their experience and problem‑solving expertise into academic credentialing.

• Those who already hold a master’s degree in a complementary field such as computer science and are seeking a second master’s degree.

• As a stepping-stone for future PhD candidates needing more focused research experience.

Program Structure

• 3 courses (9 credits) approved by the thesis committee. (This may include a course on Research Methodology, Design Thinking, and Intellectual Property Rights.)

• 21 credits of thesis research under faculty supervision.

Completion Requirements

• Successful viva voce examination.

• Submission of thesis to the Graduate School.

• Submission of the thesis, in part or in whole, to an approved peer‑reviewed journal for publication.

• Public presentation to faculty and peers.