School of Data Science*

The School of Data Science is designed to organize data science academic and research activities (with degrees and certificates tailored to regional workforce needs) while leveraging research partnerships with nearby national labs (Jefferson Lab, NASA Langley, and Wallops Flight Facility) to develop a targeted scientific focus in data science.  The School’s objectives include developing high-impact, cross-disciplinary research initiatives that center on data science and conducting outreach and community engagement, being a source of data science expertise to the community, the Hampton Roads region, the Commonwealth of Virginia, and the nation.

*Pending approval from the State Council of Higher Education for Virginia.

Master of Science in Data Science and Analytics

Mohammed Zubair, Graduate Program Director and Computational Data Analytics Concentration Coordinator
Dean Chatfield,  Business Intelligence & Analytics Concentration Coordinator

This programs will provide students with a foundation to use state-of-the-art programming tools and software packages to develop statistical models. Students will learn how to use data for identifying trends and patterns, solving problems, communicating results, and recommending optimal solutions. Students will choose one of two concentration areas: computational data analytics and, business intelligence analytics.

Coursework for the computational data analytics concentration focuses on teaching programming language, use of complex statistical tools, and mathematical modeling. Graduates will be able to enter data science, analytical, and statistical fields. Coursework for the business intelligence and analytics concentration focuses on providing students with the skills to gather, analyze, and use data to make informed decisions. Graduates will be prepared to enter business and organizations that need educated professionals to help make informed recommendations. This program is available on-campus and online.

Artificial Intelligence & Machine Learning

In this concentration, students will prepare to enter rapidly emerging fields related to data science and analytics. The coursework addresses relevant data analytics topics such as video analytics, algorithms and data structures, and information retrieval. Students will learn computational data analysis, data visualization, and natural language processing. Students will select four courses in consultation with the faculty advisor.

Business Intelligence & Analytics Concentration

This concentration will prepare students for organizations looking for data-driven recommendations. The course work addresses the use of tools to store, access, and analyze data to support making informed business recommendations. Students will learn how to retrieve data, to gain insights, make decisions, and communicate solutions to various constituents in specific settings of data science and analytics. Students will select four courses in consultation with the faculty advisor.

Engineering & Big Data Analytics Concentration

The purpose of this concentration is to provide students with a thorough understanding of the methods and technologies to handle big data and to instill engineering problem-solving skills rooted in big data solutions. It will further prepare them to become professionals trained in advanced data analytics, with the ability to transform large streams of multiple data sources into understandable and actionable information for the purpose of making decisions. The coursework (12 credits) will enable students to learn and practice the following competencies: data collection, data storage, processing and analyzing data, reporting statistics and patterns, drawing conclusions and insights and making actionable recommendations.

Geospatial Analytics Concentration

This concentration enables MS Data Science students to develop advanced skills and expertise in geospatial science and technology. Incorporating Geographic Information Systems (GIS), remote sensing, and location-based data allows data scientists to uncover spatial patterns. The concentration provides for a foundation across the breadth of geospatial technology to prepare data for analysis, perform suitability analysis, spatial predictive modeling, geostatistics, and space-time pattern mining and object detection. The concentration coursework (12 credits) incorporates advanced geovisualization and webmapping technology to also enhance cartography analytics and communications.


The requirements for admission to the Master of Science in Data Science and Analytics are as follows:

  1. A baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system and technology or a related field from a regionally-accredited institution or an equivalent institution outside the U.S.; students holding a bachelor's degrees in an unrelated field will need competency in topics related to basic statistics and computer science.
  2. GRE scores with a 50% or better attainment on quantitative reasoning.
  3. Current scores on the Test of English as a Foreign Language (TOEFL) of at least 230 on the computer based TOEFL or 80 on the TOEFL iBT.

Students with previously completed work at a regionally-accredited institution may submit a request for a maximum of 12 elective graduate credit hours to be transferred into the program. If approved by the admission committee, it will be added to the transcript.

Curriculum and Requirements

The program requires 30 credit hours. The curriculum includes two concentrations: computational data analytics and, business intelligence and analytics. A capstone project is required.

Core Courses15
Introduction to Data Science and Analytics
Data Analytics and Big Data
Data Visualization
Statistical/Probability Models for Data Science and Analytics
Statistical Tools for Data Science and Analytics
Capstone Course3
Choose one of the following concentrations:
Artificial Intelligence & Machine Learning
Select four of the following:
Introduction to Machine Learning
Web Science
Database Concepts
Data Analytics for Cybersecurity
Introduction to Artificial Intelligence
Machine Learning
Information Visualization
Natural Language Processing
Introduction to Information Retrieval
Business Intelligence & Analytics Concentration
Select two of the following:
Data Visualization and Exploration
Advanced Business Analytics/Big Data Applications
Simulation Modeling for Business Systems (BNAL 576 may be substituted with permission of instructor.) *
Select two of the following:
Database Management Systems
Business Intelligence
Information and Communications Technology for Big Data
Engineering & Big Data Analytics
Select two core courses from (6 credits):
Big Data Fundamentals
High Performance Computing and Simulations
Machine Learning I
Select two elective courses from (6 credits):
Computer Vision
Topics in Modeling and Simulation (Visualization for Big Data Analytics)
Transportation Data Analytics
Autonomous and Robotic Systems Analysis and Control
Cluster Parallel Computing
Statistical Analysis and Simulation
Machine Learning II
Geospatial Analytics Concentration
Required core courses for this concentration (6 credits):
Geospatial Data Analysis
Spatial Statistics and Modeling
Select two elective courses (6 credits)
Internet Geographic Information Systems
Advanced GIS
Advanced Spatial Analysis
Applied Cartography/GIS
Spatial Analysis of Coastal Environments
Marine Geography
GIS Programming
Geographic Information Systems for Emergency Management
Topics in Geography (Geospatial Field Techniques)
Total Hours30

BNAL 576 may be substituted for BNAL 721 with permission of the concentration coordinator.


DASC 596. Topics in Data Science. 3 Credits.

The advanced study of selected topics designed to permit small groups of qualified students to work on subjects of mutual interest which, due to their specialized nature, may not be offered regularly. These courses will appear in the course schedule and will be more fully described in information distributed to academic advisors.

DASC 597. Independent Study. 1-3 Credits.

Independent reading and study on a topic to be selected under the direction of an instructor. Conferences and papers as appropriate. Prerequisites: approval of the program coordinator.

DASC 620. Introduction to Data Science and Analytics. 3 Credits.

This course will explore data science as a burgeoning field. Students will learn fundamental principles and techniques that data scientists employ to mine data. They will investigate real life examples where data is used to guide assessments and draw conclusions. This course will introduce software and computing resources available to a data scientist to process, visualize, and model different types of data including big data. Cross-listed with CS 620.

DASC 690. Data Science Capstone Project. 3 Credits.

The culminating course in the proposed MS in Data Science and Analytics degree program will bring students together with faculty and external partners. In consultation with a faculty advisor and a business or industry or government representative, students will be required to develop a project that aims to solve a data science/analytics problem in a real-world business, industry, or government setting. Faculty and business/industry/government representatives will serve as external mentors for the students during this experience. Note that an external mentor is not mandatory but encouraged. Pre- or corequisite: DASC 620/CS 620, CS 624, CS 625, STAT 603, and STAT 604.