MS Data Science

2 Years On Campus Masters Program

University of Delaware

Program Overview

The Master of Science in Data Science (MSDS) at the University of Delaware (UD) is an interdisciplinary graduate program designed to develop advanced expertise in data analytics, statistics, mathematics, computer science, machine learning, and computational methods. Offered through the Graduate College, the program combines technical foundations with practical applications, preparing students to analyze large and complex datasets, develop predictive models, extract meaningful insights, and solve real-world problems across business, government, healthcare, science, and research.

The program requires 30 graduate credits and offers both thesis and non-thesis options. Students can complete the degree through full-time or part-time study, with the opportunity to customize their curriculum through electives across multiple academic departments. The program is STEM-designated and can generally be completed in 3–4 semesters, depending on the student's course load and study plan. It also offers selected 4+1 bachelor's-to-master's pathways and dual-degree opportunities with PhD programs in mathematics and applied mathematics.

Curriculum Structure

Year 1

Students begin by developing a strong foundation in probability, statistics, database management, data mining, machine learning, mathematical methods, and computational techniques. The core curriculum introduces the essential principles required to understand data-driven technologies and build reliable analytical models.

Example coursework includes:

Regression Analysis (STAT 611)
Applied Multivariate Methods (STAT 613)
Database Systems (CISC 637)
Introduction to Data Mining (CISC 683)
Introduction to Machine Learning (CISC 684)
Algorithm Design and Analysis (CISC 621)
Probability Theory and Applications (MATH 630)
Ethics in Data Science and Artificial Intelligence (PHIL 655)

Students develop practical knowledge of statistical modeling, database technologies, data processing, algorithm design, predictive analytics, and ethical data practices.

Year 2

During the second year, students focus on advanced technical electives and specialized applications of data science. They can select courses from different academic departments to develop expertise aligned with their professional interests, research objectives, and preferred industry sectors.

Example coursework includes:

Computational Methods for Equation Solving and Function Minimization (MATH 612)
Statistical Computing and Optimization (STAT 603)
Educational Data Mining (EDUC 875)
Search and Data Mining (CPEG 657)
Machine Learning for Public Policy (SPPA 722)
Analytics I – Statistical Learning (ELEG 815)
Applied Multivariate Data Analysis (EDUC 874)
Data Science for Physical Scientists (PHYS 661)

Students following the thesis option undertake independent research under faculty supervision, while those selecting the non-thesis option complete additional coursework and may undertake approved research, special problem projects, or industry-related internships. These experiences help students strengthen their analytical, technical, and professional capabilities.

Data Science; Data Analytics; Artificial Intelligence; Machine Learning; Statistical Modeling; Probability and Statistics; Database Management; Data Mining; Predictive Analytics; Computational Mathematics; Algorithm Design; Data Engineering; Statistical Computing; Optimization; Business Analytics; Data Ethics; Scientific Computing; Quantitative Research.

Learning Outcomes

Develop expertise in data collection and processing, statistical analysis, probability theory, machine learning, database management, data mining, algorithm design, predictive modeling, mathematical computation, statistical optimization, data visualization, ethical data practices, interdisciplinary research, and communicating analytical findings to technical and non-technical audiences.

Professional Alignment (Accreditation)

The program prepares students for careers in data science, data analytics, data engineering, business intelligence, statistical analysis, machine learning, and research. Its interdisciplinary curriculum combines computer science, mathematics, statistics, and application-based learning, enabling graduates to work with complex datasets and develop analytical solutions across different industries.

Students benefit from:

Technical Training: Advanced coursework in statistics, machine learning, database systems, data mining, mathematical modeling, and computational methods.
Research Opportunities: Access to more than 85 affiliated faculty members representing over 20 academic departments.
Practical Experience: Opportunities to participate in on-campus projects, research activities, and external internships.
Industry Applications: Opportunities to apply data science techniques to business, government, healthcare, education, and scientific research.
Faculty Mentorship: Individual academic guidance and support in developing personalized study plans.
Professional Development: Training in technical collaboration, workflow management, research communication, and professional accountability.

The program is STEM-designated, making eligible international F-1 students potentially eligible for a 24-month STEM OPT extension, subject to applicable immigration requirements.

Reputation (Employability Rankings)

The University of Delaware offers the MS in Data Science through an interdisciplinary academic structure that brings together expertise from computer science, mathematics, statistics, engineering, and other disciplines. The program emphasizes practical analytical training, research collaboration, and industry-relevant skills to prepare graduates for the growing demand for data science professionals.

Students benefit from:

Interdisciplinary Faculty Network: More than 85 affiliated faculty members from over 20 academic departments.
Research Opportunities: Access to faculty-led research and interdisciplinary data science initiatives.
Industry Connections: Exposure to professional applications of data science through internships, projects, and collaborations.
Flexible Curriculum: Opportunities to customize coursework according to individual academic and career interests.
Professional Career Preparation: Development of technical, analytical, communication, and problem-solving skills.

Graduates can pursue careers as Data Scientists, Data Analysts, Data Engineers, Business Intelligence Analysts, Machine Learning Engineers, Statistical Analysts, Data Mining Specialists, Quantitative Analysts, Research Analysts, Business Analytics Consultants, Predictive Modeling Specialists, and Data Science Researchers. Alumni have secured opportunities with organizations such as JPMorgan Chase, Wells Fargo, Bank of America, PwC, Incyte, Paramount, and AAA.

Experiential Learning (Research, Projects, Internships etc.)

At the University of Delaware, the MS in Data Science program is designed to provide students with hands-on experience that is essential for success in the rapidly evolving field of data science. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach not only enhances theoretical knowledge but also equips students with the skills needed to tackle real-world challenges.

Here are some key aspects of the experiential learning opportunities available in the MS Data Science program:

- Collaborative Projects: Students often work in teams on data-driven projects, allowing them to apply their skills in a collaborative environment and learn from their peers.

- Internship Opportunities: The program encourages internships with local and national companies, providing students with valuable industry experience and networking opportunities.

- Access to Advanced Software: Students gain proficiency in industry-standard tools such as Python, R, SQL, and Tableau, which are essential for data analysis and visualization.

- Research Laboratories: The university boasts dedicated research labs equipped with the latest technology, where students can engage in cutting-edge research alongside faculty members.

- Data Science Institute: The University of Delaware's Data Science Institute fosters interdisciplinary collaboration and provides resources for students to work on innovative projects.

- Field Trips and Workshops: Students have the opportunity to participate in field trips to tech companies and workshops led by industry experts, enhancing their learning experience.

- Library Resources: The university libraries offer extensive resources, including access to databases and journals that are crucial for data science research.

Progression & Future Opportunities

The Master of Science in Data Science at the University of Delaware equips graduates with the skills and knowledge to excel in a rapidly growing field. Alumni have successfully secured positions such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Intelligence Analyst. With a strong foundation in data analysis, programming, and statistical methods, graduates are well-prepared to meet the demands of various industries.

Here’s how the University of Delaware supports your journey toward a successful career in data science:

- Career Services: The university offers dedicated career counseling, resume workshops, and interview preparation to help you stand out in the job market.
- Employment Statistics: Over 90% of graduates find employment within six months of graduation, with starting salaries averaging around $85,000 per year.
- University–Industry Partnerships: Collaborations with leading companies such as DuPont, JPMorgan Chase, and Bank of America provide students with internship opportunities and real-world projects that enhance learning.
- Long-term Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that your degree holds value in the job market for years to come.
- Graduation Outcomes: Graduates have gone on to work in diverse sectors, including finance, healthcare, and technology, showcasing the versatility of a data science degree.

Further Academic Progression: After completing the MS in 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. Alternatively, you could explore certifications in specialized areas such as machine learning or artificial intelligence to further enhance your expertise and career prospects. The University of Delaware provides a supportive environment for continued learning, ensuring you have the resources to achieve your academic and professional goals.