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M.S. in Data Science

Beginning in Fall 2026, Virginia Tech will offer a standard two-year in-person option for the M.S. in Data Science. The full-time program will be based at the Blacksburg campus.

The M.S. in Data Science combines rigorous technical training with real-world projects, preparing graduates to navigate organizational complexities and translate data into action and value. The faculty is comprised of industry experts whose knowledge gives our students a distinct edge.

With this non-thesis degree, students gain real-world experience through a two-semester Capstone course. Additionally, students enrolled at our Blacksburg campus have the opportunity to choose one of several different concentrations, demonstrating the use of data science in various domains.

Admission Criteria

  • Receipt of a bachelor's degree from an accredited college or university
  • An undergraduate cumulative grade point average of 3.0 or higher on a 4.0 base for the equivalent of the last two years of undergraduate study
    • Note: Exceptions may be made to this rule upon recommendation of the department, providing that the applicant can present other substantial evidence of his or her ability to pursue graduate work at a satisfactory level.
  • GRE: OPTIONAL at applicant’s discretion.
  • Statement of Purpose: REQUIRED.
  • Letters of Reference: TWO REQUIRED. For recent graduates (within the last five years), 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.

Recommended Prerequisites

Generically speaking, the recommended prerequisites for the M.S. in Data Science 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 registar'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.

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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. 

CS 5045*

Computation for Data Sciences

Covers fundamentals of computer science and background in data sciences needed by graduate students without a computer science background.
*May be waived pending review of student’s undergraduate coursework or relevant experience

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. 

Elective 2

Electives are chosen from a list of approved courses.

Year Two

ADS 5526

Statistical Learning

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

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 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.