Program Overview
School of Computing- Location: Clemson, SC
- Length: 2 Years
- Tuition: $31,615
- Website: https://www.clemson.edu/cecas/departments/computing/academics/graduates/degrees/mac.html
The Master of Applied Computing (MAC) at Clemson University is specifically designed for students without a computer science background who want to gain comprehensive computing expertise combined with specialized knowledge in their chosen field.
This unique program requires 42 total credit hours for completion and combines 12 credits of foundational computer science coursework through the MSCS Ready program with 30 credits of advanced graduate-level studies.
Students must choose from six specialized tracks, with the Artificial Intelligence and Machine Learning track being particularly robust for those seeking ML expertise.
Program Duration and Credit Requirements
Full-time students typically complete the MAC program in two academic years plus one intervening summer, while part-time students have up to six calendar years to finish their degree. The program is designed for fall semester entry only, ensuring cohort-based learning and proper course sequencing.
Total Requirement: 42 credit hours
- Foundational Modules: 12 credits (MSCS Ready)
- Track Courses: 15 credits specific to your chosen track
- Additional Coursework: 15 credits of graduate electives
Advanced-Level Courses: At least 21 credits must be 8000-level
School of Computing Courses: At least 27 credits must be CPSC or HCC offerings within the School of Computing
Machine Learning Specialization
The Artificial Intelligence and Machine Learning track offers comprehensive coverage of modern ML techniques through specialized courses including:
- Applied Data Science
- Artificial Intelligence
- Machine Learning Implementation and Evaluation
- Advanced Machine Learning
- Deep Learning
- Data Mining
Curriculum
Students must complete at least three core ML courses from this track, with options for up to two additional courses from an approved list maintained in the Graduate Student Handbook.
The curriculum emphasizes hands-on implementation, requiring students to code algorithms from basic principles without relying on existing machine learning libraries. Advanced courses cover decision trees, Bayesian learning, genetic algorithms, reinforcement learning, and theoretical concepts like VC dimension and PAC learnability.
The machine learning core includes these classes:
CSIS 638 Implementation of Database Management Systems (3) – This advanced database course covers query processing algorithms, optimization techniques, physical database design, and transaction management. Students learn concurrency control, backup and recovery methods, distributed database systems, and multidimensional data handling using Datalog for recursive queries. Additional topics include multimedia databases, object-relational systems, data warehousing, and data mining techniques.
DATA 510 Data Cleaning, Organization, and Visualization (3) – Students master essential data preparation skills including cleaning, wrangling, organizing, and querying large datasets and streaming data. The course emphasizes practical strategies for handling big data challenges and creating effective visualizations to communicate insights from complex data sources.
DATA 531 Database Concepts (3) – This foundational course introduces core database principles with emphasis on the relational model. Students explore data models, query languages, relational design using normalization, database programming, and security considerations. Hands-on experience includes working with relational database management systems and SQL programming.
DATA 534 Machine Learning, Data Mining, and Analytics (3) – Students implement and apply cutting-edge machine learning algorithms for knowledge discovery from data. The course covers fundamental concepts and advanced methods in machine learning, analytics, and data mining, providing practical experience with state-of-the-art algorithms used in industry and research.
MATH 550 Linear Models (3) – This theory-focused course provides comprehensive coverage of linear models for data analysis using vector space concepts and projections. Topics include analysis of variance, regression models, Bayesian estimation, hypothesis testing, experimental design, and advanced concepts like variance component estimation and balanced incomplete block designs.
DATA 507 Scientific Computing in Data Science (3) – Students learn to apply scientific methodology to data science challenges including incomplete data handling, temporal analysis, bias correction, and outlier detection. The course covers experimental design, signal processing, time-dependent modeling, ensemble methods, statistical image analysis, and scientific numerical techniques for rigorous data analysis.
MATH 540 Statistical Learning I (3) – This advanced statistics course introduces modern statistical learning approaches including empirical processes, classification algorithms, clustering techniques, and nonparametric methods. Students explore density estimation, regression analysis, model selection procedures, adaptive algorithms, bootstrapping, and cross-validation for robust statistical inference.
You can find more info about the coursework options here: https://catalog.clemson.edu/preview_program.php?catoid=42&poid=11158
Admission Requirements
Who Can Apply
- Open to candidates holding a bachelor’s degree in any discipline
- Perfect for career changers and professionals moving into computing
AI & Machine Learning Track
- Requires prior statistics coursework
- Or completion of STAT 8010 in your first Clemson semester
Application Materials
- Official GRE scores (mandatory, non-waivable)
- Unofficial transcripts
- Two letters of recommendation
- Statement of purpose
- Current résumé or CV
Target Audience
The program particularly suits working professionals, career changers, and individuals from non-technical backgrounds who want to develop expertise in computing and machine learning without starting from an undergraduate computer science degree.
Tuition
Based on the Clemson tuition calculator, the estimated cost of the program is $31,615.
Cost Breakdown:
- Cost per semester: $6,323
- Program duration: 5 semesters (2 academic years + 1 summer)
- Calculation: $6,323 × 5 semesters = $31,615
Important Notes:
- This assumes full-time enrollment following the standard 2-year + summer timeline
- Summer semester costs may differ from regular semester costs (not specified)
- Additional fees beyond those listed may apply
Below is a screenshot from the calculator output.
Career Outcomes
Graduates develop strong programming skills, understanding of computer systems, algorithm selection abilities, and problem-solving capabilities that prepare them for careers in data science, machine learning engineering, software development, and technology consulting. The program’s coursework-only format focuses on practical application and industry-relevant skills, making graduates immediately valuable to employers seeking professionals who can bridge technical computing knowledge with domain expertise from their previous academic and professional backgrounds.