Master of Science in Data Science

Program Purpose

The MS in Data Science at Wilmington University is a 30-credit interdisciplinary graduate program designed to equip students with the technical, analytical, and mathematical expertise needed to excel in a data-driven world. The program is offered as a general data science degree or with Business Analytics (36 credit) or Computational Biology (39 credit) concentrations. The general track allows students to tailor electives to their interests across multiple applied areas, while the Business Analytics Concentration adds advanced coursework in business problem-solving, forecasting, optimization and simulation. The Computational Biology Concentration adds advanced coursework in bioinformatics, advanced genomics, machine learning as applied to biology including protein folding, and computational approaches to solving an array of biological questions. The curriculum integrates applied statistics, computer science, and mathematics to provide a rigorous foundation in programming, data modeling, analytical reasoning, and applied analytics. Machine learning methods are embedded throughout the curriculum, giving students hands-on experience with modeling techniques commonly used in modern data science practice.

Durable and Technical Skills

Students develop practical skills in algorithms, data management, data mining, and visualization using industry-standard tools and cloud-based platforms. Courses will emphasize both the mathematical underpinnings of data science and their practical applications across diverse industries, ensuring that graduates can translate complex data into actionable insights.

To support individual and diverse goals, the program offers several electives in multiple applied areas, such as Business Analytics, Technology, Environmental Science, Computational Science, and Criminal Justice, allowing students to tailor electives to their professional interests. For the 30-credit no-concentration degree, these electives serve as suggested areas of application rather than formal concentrations, providing flexibility to explore diverse data contexts.

The program culminates in a capstone case study course or internship, where students integrate their learning by solving real-world problems. Working with authentic datasets, students will design and implement data-driven solutions, practicing the skills of problem framing, analysis, and communication expected of professional data scientists. This interdisciplinary structure reflects the University’s mission to integrate business and technology education while responding directly to employer demand for graduates who can apply data science in real organizational settings.

Program Competencies

Upon graduation, students are expected to have gained an advanced level of applicable knowledge in the following five program competencies:

1. Communicate data science findings through professional written, visual, and/or oral formats tailored to technical and non-technical audiences.
2. Lead and evaluate collaborative, data-driven projects by applying sound analytical judgment in diverse team environments.
3. Analyze complex data problems using quantitative reasoning and analytical methodologies to frame questions, evaluate data, interpret results and produce evidence-based conclusions and recommendations.
4. Design data-driven solutions by evaluating, selecting, and applying appropriate computational tools, technologies, and workflows to effectively solve complex data problems.
5. Evaluate the ethical, legal, and societal implications of data practices when developing and applying data science methods and decision-making.

Data Science Requirements (30 credits)

All students in the MS in Data Science program will complete the following required courses (9 credits), core courses (15 credits), and elective courses (6 credits).

Students will also be required to take the following course as a pre-requisite to MBA 6300 if a comparable undergraduate calculus or statistics course has not been completed:

• MAT 308 or MAT 312

In addition, students in the Computational Biology concentration will be required to take the following courses prior to enrolling if comparable undergraduate biology courses have not been completed:

• BIO 251
• BIO 252

Required Courses

CSC 5000 Intro to Computer Science

MBA 6300 Quantitative Business Analysis

Choose one of the following two courses:

CSC 7002 Python Programming

DTA 7400 Data Science

Data Science Core Courses

CSC 7021 Cloud-Based Machine Learning

CSC 7025 Data Analytics and Visualization

IST 7000 Data Management

MBA 7715 Predictive Analytics

DTA 8000 Data Science Capstone

Elective Courses (6 credits)

Students must choose two of following courses:

Business Electives

MAC 6200 Data Analytics for Accountants

MBA 7720 Forecasting for Business Analytics

MBA 7725 Optimization for Business Analytics

MBA 7730 Simulation for Business Analytics

Computational Science Electives

BIO 7050 Advanced Genomics

DTA 6100 Biometry

DTA 7450 Computational Science

Criminal Justice Electives

MAJ 6604 Technology for Modern Policing

MAJ 6633 Research Methods in Criminal Justice

Environmental Science Electives

DTA 6100 Biometry

DTA 7300 Geographic Information Systems

Technology and Computer Science Electives

CSC 7003 Algorithms and Advanced Data Structures

CSC 7020 Theory of Artificial Intelligence

CSC 7024 Predictive Analytics: Data Mining

IST 7080 Cloud Management

Concentration in Business Analytics (36 credits)

Required Courses

CSC 5000 Intro to Computer Science

MBA 6300 Quantitative Business Analysis

Choose one of the following two courses:

CSC 7002 Python Programming

DTA 7400 Data Science

Business Analytics Concentration Courses

CSC 7024 Predictive Analytics: Data Mining

MBA 7720 Forecasting for Business Analytics

MBA 7725 Optimization for Business Analytics

MBA 7730 Simulation for Business Analytics

Data Science Core Courses

CSC 7021 Cloud-Based Machine Learning

CSC 7025 Data Analytics and Visualization

IST 7000 Data Management

MBA 7715 Predictive Analytics

DTA 8000 Data Science Capstone

Concentration in Computational Biology (39 credits)

All students in the MS in Data Science with a concentration in Computational Biology program will complete the following required courses (9 credits), core courses (12 credits), and concentration courses (18 credits).

Data Science Core Courses

CSC 7021 Cloud-Based Machine Learning

CSC 7025 Data Analytics and Visualization

IST 7000 Data Management

MBA 7715 Predictive Analytics

Computational Biology Concentration Courses

BIO 6600 Cell and Molecular Systems

BIO 7050 Advanced Genomics

DTA 6100 Biometry

DTA 7450 Computational Science

DTA 8000 Data Science Capstone

DTA 8001 Data Science Internship

Required Courses

CSC 5000 Intro to Computer Science

MBA 6300 Quantitative Business Analysis

Choose one of the following two courses:

CSC 7002 Python Programming

DTA 7400 Data Science

This information applies to new students who enter this degree program during the 2026-2027 Academic Year. All enrolled students should log in to MyWilmU Degree Works to view their personalized course and program completion requirements. You may also refer to the academic catalog for the general curriculum for this program from previous academic years.