Virginia Tech® home

Northern Virginia

Beginning in Fall 2026, Virginia Tech's M.S. in Data Science will be offered as a part-time option in Northern Virginia.

Classes will be held in a primarily asynchronous format, with weekly synchronous meetings. Each course will also hold a monthly four-hour weekend workshop.

Application Deadline

Fall 2026 Admission: Aug. 1, 2026

Application Materials

  • GRE: OPTIONAL at applicant’s discretion
  • Statement of Purpose: REQUIRED
  • Letters of Reference: TWO REQUIRED. For recent graduates, at least one letter should be from a faculty member familiar with their work.
  • Sample of a data analysis report or a software program the student has written: OPTIONAL
  • The committee may request virtual interviews to further assess your qualifications for the program.
  • International students: Applicants whose first language is not English must submit TOEFL scores of at least 90. Exceptions may be granted to those applicants who have graduated with a bachelor's degree from an accredited university where English is the language of instruction.
    • IELTS (International English Language Testing System) is also accepted; a 6.5 minimum score is required.

The admissions committee will review the entirety of the applicant's academic and work experience to make an informed decision regarding admission to the M.S. in Data Science graduate program.

Note for international students: Because the Northern Virginia program option is only offered on a part-time basis (six hours per semester), it does not meet the requirements to obtain an F1/J1 visa through Virginia Tech.

Recommended Prerequisites

Generically speaking, the recommended prerequisites for the MSDS include:

  • two semesters of calculus;
  • one semester of linear algebra;
  • two semesters of statistics;
  • and one semester of computer programming.    

Specific course numbers for classes taken at Virginia Tech are listed below; for courses taken outside of Virginia Tech, the registrar's office has multiple databases to determine course equivalencies.

  • MATH (11 hours): 1225, 1226, & 2114
  • STAT (6 hours): 3005 & 3006; or 3615 & 3616; or 4105 & 4106; or 4705 & 4706; or 5615 & 5616
  • CS (3 hours): 1064 (preferred) or 1044 or 1054

Note: When evaluating an applicant in regard to prerequisites, the Admissions Committee will review the applicant’s entire history of coursework.

Apply Now

Suggested Course Sequence

Year One

ADS 5064

Foundations of Data Science

Fundamental principles and concepts in data science in the context of the end-to-end data science project life cycle. 

Elective 1

Electives are chosen from a list of approved courses.

ADS 5224

Communication in Team-Based Data Science

Oral, written, and visual communication skills required to plan, oversee, and execute end-to-end data science projects. 

ADS 5525

Statistical Learning

Methods of supervised statistical and machine learning for regression and classification. 

ADS 5526

Statistical Learning

Supervised and unsupervised statistical and machine learning for complex or high-dimensional data.

Elective 2

Electives are chosen from a list of approved courses.

Year Two

CS 5054

Programming Models for Big Data

Survey of computer science concepts and tools that enable efficient computational science and data analytics with big data.

ADS 5804

Capstone Experience I: Data & Definition

Application of underlying principles and initial phases of the data science process to a specific project.

Elective 3

Electives are chosen from a list of approved courses.

ADS 5814

Capstone Experience II

Application of latter phases of the data science process to a specific project.

Elective 4

Electives are chosen from a list of approved courses.