Master of Science and Technology in Computational Science

The Professional Science Master's in Computational and Data Science at the University of Utah prepares students to transform complex data into meaningful insights through advanced computational techniques, statistical analysis, and machine learning. Designed for students with a strong background in mathematics, computer science, or related quantitative fields, the program combines technical specialization with business and professional development, making it suitable for careers in data science, computational research, analytics, and technology-driven industries.

Curriculum Structure:

First Year – Computational Foundations and Professional Skills:

Students begin by developing strong foundations in statistical analysis, probability, algorithms, and professional communication. Key courses include Applied Statistical Techniques (MST 6600), which introduces exploratory data analysis and the R programming language, Introduction to Probability (MATH 5010), which explores probability distributions and statistical concepts, and Graduate Algorithms (CS 6150), which develops advanced algorithm design and analysis skills. Students also complete professional development subjects such as Professional Development for Scientists & Engineers (MST 6200) and Scientific Reasoning & Inquiry (MST 6500) to strengthen their communication, analytical reasoning, and decision-making abilities.

Second Year – Advanced Data Science and Computational Specialization:

Students expand their technical expertise through graduate-level courses in machine learning, database systems, data mining, and numerical analysis. Important subjects include Machine Learning (CS 6350), which explores supervised and unsupervised learning algorithms, Data Mining (CS 6140), which focuses on discovering patterns in large datasets, and Advanced Database Systems (CS 6530), which covers database architecture, query optimization, and large-scale data management. Students can also select electives in computational science and mathematics based on their professional interests.

Final Stage – Professional Experience Project:

Students complete the required Professional Experience Project (MST 6975), which provides an opportunity to apply their technical knowledge in a real workplace environment involving business and scientific applications. Before beginning the project, students undertake Professional Experience Project Planning (MST 6974) to develop project objectives, define scope, identify risks, allocate resources, and establish timelines. They also complete Business Development for Scientists & Engineers (MST 6110), gaining knowledge of business planning, financial analysis, marketing, and management.

Focus Areas:

Computational Science, Data Science, Machine Learning, Statistical Analysis, Applied Mathematics, Numerical Analysis, Data Mining, Graduate Algorithms, Advanced Database Systems, Data Visualization, Probability, Statistical Inference, Computational Modeling, Scientific Computing, Business Development, Project Management, Professional Communication.

Learning Outcomes:

Graduates develop the ability to analyze complex datasets, apply statistical and computational methods, design efficient algorithms, develop machine learning models, manage large-scale databases, and communicate analytical findings effectively. They also gain practical experience in scientific reasoning, business development, project planning, professional collaboration, and applying computational solutions to real-world industry challenges.

Professional Alignment (Accreditation):

The program emphasizes professional readiness by combining advanced STEM education with business and management coursework. Students complete 12 credits of professional skills courses and a required three-credit Professional Experience Project, which connects academic knowledge with practical workplace applications. The curriculum develops leadership, communication, business planning, project management, and technical problem-solving skills. No separate program-specific professional accreditation is identified on the official program page.

Reputation (Employability Rankings):

The University of Utah provides an interdisciplinary academic environment through its Graduate School and Professional Science Master's program, connecting computational science with mathematics, computer science, statistics, and business. Students benefit from graduate-level coursework across different academic departments and opportunities to apply their knowledge through professional projects in scientific and business environments. The program prepares graduates for careers in data science, machine learning, computational analysis, database management, scientific research, and technology consulting.