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Bachelor of Science in Computational Modeling and Data Analytics

The B.S. in Computational Modeling and Data Analytics (CMDA) is Virginia Tech's big data degree. Program allows students to learn how to use fast algorithms to model the world and discover hidden patterns in massive data sets. CMDA students can combine mathematics, statistics, and computer science to solve important practical problems in applications like social network analysis, homeland security, disease spread, cancer therapy, and tsunami prediction.

The CMDA program draws on expertise from three primary departments at Virginia Tech with strengths in quantitative science: Mathematics, Statistics, and Computer Science. By combining elements of these disciplines in innovative, integrated courses that emphasize techniques at the forefront of applied computation, CMDA teaches a rich suite of quantitative skills for tackling today's massive data-based problems. CMDA courses focus on extracting information from large data sets, and on analyzing and solving problems through fast algorithms, accurate models, evolving statistical methodology, and quantifying uncertainty. Drawing on massive computational resources, these skills enable powerful analytic techniques impossible just a few years ago. Graduates are qualified for positions in industry, business, the sciences, engineering, and more – anywhere top-flight quantitative scientists are needed.

CMDA majors learn practical skills that are valued by industry:

  • machine learning algorithms for analysis of big data sets
  • differential equation modeling of physical and biological phenomena
  • high performance computing, nimble programming skills (Java, C, MATLAB, R)
  • data visualization
  • practical problem solving in teams
  • ethics of data and math modeling

Possible jobs CMDA majors qualify for:

  • analytics for internet/social media/start-up companies
  • consulting in defense/space/homeland security
  • modeling in the oil/gas/alternative energy sector
  • data analysis for medical/pharmaceutical firms
  • quantitative modeling in finance/insurance
  • and a host of other possibilities: anyone who seeks to better understand the world through data and computation.


  • CMDA 2005: Integrated topics from quantitative sciences
  • CMDA 2006: Intermediate linear algebra, regression, differential equations, and model validation.
  • CMDA 3605: Math Modeling: Methods, Tools
  • CMDA 3606: Math Modeling: Methods, Tools
  • CS/CMDA 3634 Computer Science Foundations for Computational Modeling & Data Analytics
  • CMDA 3654 Introductory Data Analytics and Visualization
  • CMDA 4604 Intermediate Topics in Mathematical Modeling
  • CMDA 4654 Intermediate Data Analytics and Machine Learning
  • CMDA 4664 Computational Stochastic Modeling
  • CMDA 4864 CMDA Capstone


CMDA is directed by Professor Mark Embree of the Math Department and Nora Sullivan is the program manager and academic advisor. Please contact Dr. Embree at or Nora Sullivan at for more information on the CMDA program.