Program Overview
SICCS- Location: Flagstaff, AZ
- Length: 24 Months
- Tuition: $48,000
- Website: https://nau.edu/school-of-informatics-computing-and-cyber-systems/ms-computer-science/
The Master of Science in Computer Science at Northern Arizona University is a flexible, 30-credit program designed to support full-time students seeking careers in computer science or further doctoral studies.
The program can be completed in as few as two years and offers both thesis and non-thesis options, giving students a choice in how they want to tailor their studies. With a tuition structure that varies based on residency and a focus on core computer science areas, this degree is a good fit for those looking to expand their technical expertise and research capabilities.
Curriculum and Customization
The curriculum includes core computer science coursework alongside a wide range of elective options, ensuring students can customize their learning experience. Students may choose electives in areas like computer networking, cybersecurity, high-performance computing, and software engineering.
Notably, the program allows for significant customization with a strong focus on machine learning.
Courses such as CS 472 – Unsupervised Machine Learning, CS 573 – Interpretable Machine Learning, INF 504 – Data Mining And Machine Learning, and MRE 372 – Introduction To Probability And Machine Learning enable students to deeply explore ML concepts.
CS 472 – Unsupervised Machine Learning
CS 472 explores machine learning topics that do not rely on labeled data, extending from CS470. The course covers key areas of unsupervised learning, including clustering, Gaussian mixture models, change point detection, and dimensional reduction. Students gain insight into how to identify structure and patterns in data without explicit guidance.
CS 573 – Interpretable Machine Learning
CS 573 introduces students to interpretable machine learning algorithms that provide clear insights into how predictions are made. It covers sparse linear models, decision trees, nearest neighbors, and model-agnostic interpretability techniques. The course prepares students to balance accuracy with transparency in AI models.
INF 504 – Data Mining and Machine Learning
INF 504 delves into advanced machine learning principles and data mining practices. Key topics include uncertainty modeling, Bayesian inference, graphical models, and computational inference such as message passing and Markov Chain Monte Carlo. The course also addresses current research challenges in data mining and machine learning.
MRE 372 – Introduction to Probability and Machine Learning
MRE 372 covers fundamental concepts in statistics and probability and their application to deep machine learning. Students use PyTorch to build deep learning algorithms and learn how these concepts support solving engineering and scientific problems. The course combines theory with hands-on experience using Python tools.
Multi-State Physical Unclonable Functions with Machine Learning
This technology captures the physical behavior of physically unclonable functions (PUFs) using multi-states to assess challenge-response pair errors. It uses machine learning to analyze variations caused by aging, temperature, and other factors. The system flags statistical anomalies and calculates error rates to improve security and reliability.
This flexibility allows students to align their degree with their personal interests and professional goals, making it a strong choice for those wanting to specialize in emerging areas like machine learning.
Key Features and Learning Outcomes
Students in this program gain the ability to identify and synthesize key computer science concepts and apply them to real-world challenges. Graduates will be equipped to critically analyze scientific literature, communicate effectively in professional settings, and design technical solutions to complex problems.
Depending on the option they select, students may engage in thesis work or a substantial applied project, providing hands-on research experience and building a strong foundation for further studies or immediate entry into the workforce.
Admissions and Requirements
Applicants must have a bachelor’s degree in computer science or a related field, a GPA of 3.0 or higher, and must submit a personal statement and two recommendation letters.
Students must also pass an initial skills inventory exam or complete prerequisite courses CS 500 and CS 501 before enrolling in graduate-level CS courses. International students have additional language proficiency requirements.
Cost
For the 30-credit graduate program at Northern Arizona University:
- Arizona Resident: The estimated total cost is $47,600.
- Non-Resident: The estimated total cost is $111,780.
Cost Breakdown
Arizona Resident
- Tuition & Fees (30 credits): $14,280
- Books, Supplies, & Course Material: $900
- Food: $6,642
- Housing: $7,790
- Personal Expenses: $2,000
- Transportation: $2,500
- Total Estimated Cost: $34,112
Non-Resident
- Tuition & Fees (30 credits): $33,534
- Books, Supplies, & Course Material: $900
- Food: $6,642
- Housing: $7,790
- Personal Expenses: $2,000
- Transportation: $2,500
- Total Estimated Cost: $53,366
Graduate tuition information available here: https://nau.edu/paying-for-college/tuition-and-fees/graduate/
Program Outcomes and Fit
Graduates of the MS in Computer Science program are well-positioned for careers in software development, data science, cybersecurity, and academia. The program’s customizable curriculum makes it an excellent choice for those with strong programming backgrounds who wish to focus on advanced machine learning techniques.
Whether aiming for professional practice or further research in a doctoral program, this degree provides the theoretical and practical skills needed for success in today’s data-driven world.