Professional Masters in Computational & Data Science

2 Years On Campus Masters Program

University of Utah

Program Overview

The Professional Master of Science and Technology in Computational and Data Science at the University of Utah is an interdisciplinary graduate programme designed for students with strong backgrounds in mathematics, computer science and other quantitative disciplines. It combines advanced STEM coursework in computational methods, statistical analysis, machine learning and data science with professional training in business development, project management and communication. The programme prepares graduates to apply scientific and computational expertise to practical challenges across industry, research and government organisations.

Curriculum Structure

The programme requires 36 credit hours, divided into at least 18 credits of computational and data science coursework, 12 credits of professional skills courses and a three-credit professional experience project. Full-time students can typically complete the degree in two years, while part-time students generally take between 2.5 and four years.

Core Computational and Data Science Curriculum: Students develop foundations in probability, statistical inference, numerical analysis and computational methods. Depending on their selected focus, they can study courses such as Introduction to Probability, Statistical Inference I, Graduate Algorithms, Advanced Database Systems, Data Mining and Machine Learning.

Advanced Learning and Specialisation: Students select at least six credits of focus-area electives according to their academic and professional interests. Options include Visualization for Data Science, Bayesian Computational Statistics, Deep Learning, Cloud Computing and advanced courses in mathematics, computer science and statistics. Students may also select coursework that qualifies them for the Graduate Certificate in Deep Learning.

Professional Skills Development: Students complete professional courses covering scientific communication, business development, project management and scientific reasoning. Key courses include Professional Development for Scientists and Engineers, Operations and Project Management for Scientists and Engineers, Scientific Reasoning and Inquiry, and Business Development for Scientists and Engineers.

Professional Experience Project: Students complete a three-credit supervised project in a real workplace or professional setting. The project allows them to apply their computational and scientific knowledge to an industry or research problem while developing project planning, communication, collaboration and management skills.

Focus Areas

Computational and Data Science
Statistical Analysis and Inference
Machine Learning and Deep Learning
Data Mining and Knowledge Discovery
Algorithm Development
Advanced Database Systems
Numerical Analysis and Scientific Computing
Data Visualisation
Probability and Mathematical Modelling
Big Data and Computational Methods
Business Development and Project Management
Applied Research and Industry Projects

Learning Outcomes

Graduates develop advanced computational, mathematical and analytical skills to address complex scientific and data-driven problems. They learn to apply statistical inference, machine learning, algorithms and numerical methods to real-world datasets and computational challenges. The programme also strengthens professional communication, business development, project management and workplace collaboration, enabling graduates to plan and deliver projects that combine scientific expertise with organisational objectives.

Professional Alignment (Accreditation)

The programme is part of the University of Utah's Professional Science Master's framework, which combines graduate-level STEM education with professional and business skills. Its required professional experience project provides practical workplace preparation. The university does not specify a separate programme-specific professional accreditation.

Reputation (Employability Rankings)

The University of Utah emphasises the programme's career-oriented structure, interdisciplinary coursework and practical professional experience. Students gain exposure to industry-relevant areas such as machine learning, data analysis, computational modelling and business development. The university does not publish a specific programme-level QS or Guardian ranking or a verified employment-rate statistic for this degree.

Experiential Learning (Research, Projects, Internships etc.)

At the University of Utah, the Master’s in Computational & Data Science program is designed to provide students with hands-on experience that bridges the gap between theoretical knowledge and real-world application. Students engage in practical projects, utilize cutting-edge technology, and collaborate with peers and industry professionals. The program emphasizes experiential learning through various facilities and tools that enhance your educational journey.

Here’s what you can expect in terms of experiential learning opportunities:

- Research Labs: Access to state-of-the-art labs such as the Scientific Computing and Imaging Institute (SCI) and the Center for High-Performance Computing, where you can work on innovative projects.

- Software Tools: Proficiency in industry-standard software such as Python, R, MATLAB, and various machine learning frameworks, ensuring you are well-equipped for the job market.

- Group Projects: Collaborative projects that simulate real-world challenges, allowing you to work closely with fellow students and faculty to solve complex problems.

- Internships: Opportunities for internships with local tech companies and research institutions, providing invaluable experience and networking opportunities.

- Field Trips: Organized visits to tech companies and research facilities, giving you insight into the industry and potential career paths.

- Libraries and Resources: Access to extensive digital libraries and resources, including the J. Willard Marriott Library, which offers a wealth of research materials and study spaces.

- Industry Partnerships: Collaborations with local and national organizations that enhance learning through guest lectures, workshops, and networking events.

These experiential learning components are designed to ensure that you not only gain theoretical knowledge but also practical skills that are highly valued in the workforce. By immersing yourself in these experiences, you will be well-prepared to tackle the challenges of the data science field.

Progression & Future Opportunities

The Master’s in Computational & Data Science at the University of Utah equips graduates with the skills and knowledge to excel in a rapidly evolving job market. Alumni often find themselves in high-demand roles such as Data Scientist, Machine Learning Engineer, and Computational Analyst. With a strong foundation in both theoretical and practical aspects of data science, graduates are well-prepared to tackle complex challenges across various industries.

Here’s what you can expect in terms of progression and future opportunities:

- University Services: The University of Utah offers robust career services, including personalized career counseling, resume workshops, and networking events that connect students with industry professionals.
- Employment Stats and Salary Figures: Graduates from the program have a high employment rate, with many securing positions within six months of graduation. The average starting salary for data science roles in Utah is around $85,000, with potential for growth as experience increases.
- University–Industry Partnerships: The university has established partnerships with leading tech companies such as Adobe, Microsoft, and Amazon, providing students with internship opportunities and real-world projects that enhance their learning experience.
- Long-term Accreditation Value: The program is accredited by the Higher Learning Commission, ensuring that the education you receive meets high standards and is recognized by employers globally.
- Graduation Outcomes: Over 90% of graduates report satisfaction with their education and career outcomes, often moving into leadership roles within a few years of completing their degree.

Further Academic Progression: After completing your Master’s in Computational & Data Science, you may choose to pursue a Ph.D. in Data Science or a related field, which can open doors to advanced research positions or academic roles. Additionally, you could explore specialized certifications in areas like artificial intelligence or big data analytics to further enhance your expertise and career prospects.

The University of Utah is committed to supporting your journey, ensuring you have the tools and connections needed to thrive in this exciting field.