Accelerated Master's Program
Accelerated master's degree programs are for Virginia Tech undergraduate students who are interested in pursuing graduate studies at Virginia Tech. Students must be accepted into the UG/GR program prior to the beginning of the semester in which they would enroll in courses to be used in the accelerated program.
General information about Accelerated Master's Degrees at Virginia Tech is available on the Graduate School's website.
Admission Criteria
- Applicants must be in the last 12 months of their undergraduate degree at the time of acceptance.
- Students must have a minimum GPA of 3.3 at the time of acceptance into the program.
- GRE: Optional at applicant’s discretion.
- Statement of Purpose: Required.
- Letters of Reference: Two required, at least one of which must 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:
- MATH (11 hours): 1225, 1226, & 2114
- STAT (6 hours): 3005 & 3006 or 3615 & 3616 or 4105 & 4106 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.
Course Credits
- Only those courses that are approved for a student's undergraduate degree as well as this graduate degree may be double-counted towards both degrees.
- No more than 12 credits of graded course work may be used in the accelerated program and double-counted for an undergraduate and graduate degree.
- No more than 6 credits of the 12 double-counted credits may be at the 4000 level. All other double-counted coursework must be at the 5000 level.
- A grade of B or higher must be earned in each course to be double-counted.
Suggested Course Sequence (Accelerated 4+1)
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
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.
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 1
Electives are chosen from a list of approved courses.
Elective 2
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 3
Electives are chosen from a list of approved courses.
Elective 4
Electives are chosen from a list of approved courses.