M.S. in Data Science

NA On Campus Masters Program

Mississippi State University

Program Overview

The Master of Science in Data Science at Mississippi State University is an interdisciplinary graduate program designed to help students turn complex, large-scale datasets into meaningful insights that support better decision-making across industries, government organizations, and non-governmental organizations. Combining statistical modelling, optimization, programming, database management, and computational techniques, the program is suitable for graduates from different academic backgrounds who have strong quantitative and computational skills and want to pursue careers in data science or continue to advanced studies.

Curriculum Structure

The programme is a 30-credit-hour, courses-only master's degree. Rather than following a fixed year-by-year structure, students complete core courses, concentration-specific subjects, graduate electives, and a final project-based capstone. The following describes the learning progression rather than claiming an official two-year study plan.

Core Studies and Technical Foundations: Students begin by developing essential data science knowledge through courses such as Data Science Concepts and Practice, Statistical Thinking: Probability Models and Theory of Statistics, and Statistical Modeling. They also study Database Management Systems, gaining skills in managing, organizing, processing, and interpreting complex datasets.

Advanced Studies and Concentration Options: Students then build specialist knowledge through graduate electives or one of the available concentrations: General Data Science, Manufacturing Analytics, Geospatial Science, or Agricultural Autonomy. Depending on their chosen pathway, they may explore Machine Learning with Industrial Engineering Applications, Advanced GIS, Remote Sensing of the Physical Environment, Artificial Intelligence, or Robotics for Biological Systems.

Capstone and Applied Learning: In the final stage, students complete a three-credit-hour, committee-approved capstone project, such as a Directed Project through the Computer Science and Engineering department. This experience allows them to apply statistical, computational, and analytical techniques to a practical data science problem and present their findings through written and oral reports.

Focus Areas

Statistical Modelling, Data Management, Machine Learning, Artificial Intelligence, Database Management, Data Visualization, Optimization, Manufacturing Analytics, Geospatial Science, Geographic Information Systems, Remote Sensing, Agricultural Autonomy, Robotics, Statistical Computing.

Learning Outcomes

Graduates develop the ability to apply statistical modelling and computational techniques, manage and process large and diverse datasets, extract meaningful insights from complex information, conduct data-driven research, solve analytical problems, and communicate technical findings effectively to audiences with different levels of expertise.

Professional Alignment and Accreditation

The programme provides interdisciplinary preparation through the Department of Mathematics and Statistics and the Department of Computer Science and Engineering. Its curriculum combines statistical, computational, and applied analytical skills relevant to professional data science roles. The official programme page does not identify a separate professional accreditation specifically for this degree.

Reputation and Employability

Mississippi State University's programme is designed to prepare graduates for professional opportunities in industry, government, and NGOs. Its interdisciplinary structure and project-based capstone provide opportunities to develop skills that can be applied across sectors, including manufacturing, agriculture, technology, and geospatial analysis. The official programme page does not publish a specific graduate employment rate or programme-level salary statistic.

Experiential Learning (Research, Projects, Internships etc.)

Mississippi State University provides students with opportunities to develop practical data science skills through applied analytics projects, research activities, and access to advanced computing resources. The programme's project-based approach allows students to work with statistical models, databases, machine learning techniques, and complex datasets. Students can also explore interdisciplinary research environments where data science is applied to manufacturing, agriculture, cybersecurity, engineering, and social sciences.

The following facilities and practical learning opportunities are particularly relevant to the programme:

Project-Based Capstone: Students complete a three-credit-hour capstone project, such as a CSE 8080 Directed Project, applying their data science knowledge to a practical problem. The project includes written and oral presentations evaluated by a faculty committee.
Advanced Computing Resources: Students learn to configure personal laptops for data analysis and access cloud, high-performance, and quantum computing resources. The university also identifies GPU-based high-performance computing infrastructure for student and faculty use.
McKay Predictive Analytics and Technology Integration (PATENT) Laboratory: Provides a research environment focused on data management, predictive modelling, artificial intelligence, machine learning, cybersecurity, and cyber-physical systems.
Data Science for the Social Sciences Laboratory (DS3): Supports the integration of data science techniques with social science research, using digital and user-generated datasets.
Center for Advanced Vehicular Systems (CAVS): Offers exposure to interdisciplinary research involving autonomous vehicles, high-performance computing, advanced controls, materials science, and human-machine interaction.
Agricultural Autonomy Institute (AAI): Supports research and workforce development in agricultural autonomy, including opportunities connected to robotics, data analytics, and intelligent agricultural technologies.
Sensing and Automation in Agri-System (SAAS) Lab: Conducts research in agricultural robotics, computer vision, artificial intelligence, machine learning, remote sensing, and smart agriculture.
University Libraries: Mississippi State Libraries provide electronic and print research resources, specialist data science expertise, physical laboratory space, and maker labs. The university library system includes six libraries, including Mitchell Memorial Library.
Cooperative Education and Internship Opportunities: MSU's Career Center operates a Co-op and Internship Program that connects students with employers for structured work experiences. Students can explore these opportunities alongside their academic studies, subject to eligibility and employer requirements.

Progression & Future Opportunities

Graduates of the M.S. in Data Science at Mississippi State University can pursue careers that involve transforming complex datasets into actionable insights, developing predictive models, and supporting data-driven business and research decisions. The interdisciplinary curriculum prepares students for opportunities in technology, manufacturing, agriculture, government, research, and other data-intensive sectors.

Potential Career Roles: Data Scientist, Data Analyst, Machine Learning Engineer, Business Intelligence Analyst.

Career Development and Employment Opportunities

Career Services: MSU's Career Center provides individual career counselling, résumé and cover letter assistance, employer networking, career fairs, internship support, and the Bulldog Mentor Program, which connects students with alumni mentors.
Employment Statistics: According to MSU's 2024–2025 First Destination Survey, 88% of responding graduates reported that their degree aligned with their current job or industry. The survey collected responses from 4,136 graduates. This is a university-wide statistic, not a programme-specific employment rate.
Employer Connections: During 2024–2025, MSU students engaged with 703 employers through 71 career fairs, interviewing events, and campus visits. The university also identifies employers such as Ergon Refining, Ingalls Shipbuilding, and International Paper among organisations hiring its graduates.
Industry and Research Connections: The university's Center for Advanced Vehicular Systems, Center for Cyber Innovation, and National Strategic Planning & Analysis Research Center provide research and technology-development environments connected to engineering, cybersecurity, workforce analytics, and economic development. These offer relevant interdisciplinary exposure, although specific recruitment partnerships for this master's programme are not published.
Internship Support: The university's Cooperative Education and Internship Program helps students identify structured work experiences with employers. It offers both internship and cooperative education pathways.
Salary and Employment Outlook: Data science graduates can explore opportunities in analytics, artificial intelligence, machine learning, and statistical modelling. The university does not publish a specific average salary or employment rate for graduates of this particular master's programme, so a programme-specific salary figure cannot be confirmed.
Professional Recognition: The programme develops transferable technical competencies in statistical modelling, database management, computational analysis, and machine learning. No separate professional accreditation specifically for this degree is identified on the official programme page.

Further Academic Progression:

Students who wish to continue their academic journey can pursue doctoral-level studies in Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, or related interdisciplinary research areas. The master's programme provides preparation for advanced academic work through its statistical and computational curriculum, research-oriented projects, and final capstone experience. Graduates can also explore research opportunities involving artificial intelligence, machine learning, geospatial analytics, agricultural technology, and predictive modelling.