The Computational Modeling and Data Analytics (CMDA) program is a collaborative effort of the departments of Mathematics, Statistics, and Computer Science. It resides in the College of Science's Academy of Data Science. CMDA courses teach the range of emerging concepts and techniques from mathematics and statistics, with a decidedly computational approach, that are most in demand by a data-driven world. The curriculum prepares students as quantitative scientists ready to engage data and modeling problems wherever they may occur. CMDA is Virginia Tech’s Big Data degree.
In addition to the standard degree option, CMDA offers specialized options in: Biological Sciences, Cryptography & Cybersecurity, Economics, Geosciences, and Physics. After graduation, CMDA majors can deploy their skills across many domains, from climate science to sports analytics, from financial modeling to cybersecurity. Diverse job opportunities abound.
During senior year, CMDA majors undertake a major Capstone Project (CMDA 4864), collaborating with a team of students to tackle an open-ended modeling or analytics challenge from a client in industry, government, academia, or the non-profit sector.
Each Spring the CMDA program awards approximately $50,000 in Hamlett Scholarships, primarily to continuing students. Majors are also eligible to apply for CMDA Undergraduate Research Grants, awarded for Fall, Spring, and Summer research.
The graduation requirements in effect during the academic year of admission to Virginia Tech apply. When choosing the degree requirements information, always choose the year you started at Virginia Tech. Requirements for graduation are referred to via university publications as "Checksheets." The number of credit hours required for degree completion varies among curricula. Students must satisfactorily complete all requirements and university obligations for degree completion. The university reserves the right to modify requirements in a degree program.
Please visit the University Registrar's website at https://www.registrar.vt.edu/graduation-multi-brief/checksheets.html for degree requirements.
Please direct advising inquires to firstname.lastname@example.org.
University policy requires that students who are making satisfactory progress toward a degree meet minimum criteria toward the General Education (Curriculum for Liberal Education or Pathways to General Education) (see "Academic Policies") and toward the degree.
Satisfactory progress requirements toward the B.S. in Computational Modeling and Data Analytics can be found on the major checksheet by visiting the University Registrar website at https://www.registrar.vt.edu/graduation-multi-brief/checksheets.html.
Most CMDA courses involve the use of statistical and/or mathematical software, typically including (but not limited to) Python, R, C, Java, and MATLAB. Previous experience with these languages is not expected; students will learn the necessary tools throughout the CMDA curriculum.
Division Leader: M. Embree
Program Manager: H. Caldwell
Undergraduate Advisor: J.S. Whitehead
Principle Faculty: N. Abaid, C. Beattie, P. Cazeaux, L. Childs, J. Datta, E. de Sturler, X. Deng, F. Faltin, R. Gramacy, S. Gugercin, A. Habibnia, P. Haskell, D. Higdon, L. House, L. Johnson, I. Kim, S. Leman, C. Lucero, D. Lucero, M. Liu, G. Matthews, S. Merkes, A. Miedlar, J. P. Morgan, C. North, A. Patterson, L. Pillonen, M. Pleimling, N. Ramakrishnan, C. Ribbens, J. Rudi, E. Smith, E. Ufferman, T. Warburton, J. Wilson, X. Xing, and L. Zeitsman
An introduction to the practice and profession of Computational Modeling and Data Analytics. Acquaints students with foundational computational tools, solving problems with modeling and data, visualization, ethical considerations in data science, professional opportunities in the field, and advising resources at Virginia Tech.
2005: Integrated topics from quantitative sciences that prepare students for advanced computational modeling and data analytics courses. Topics include: probability and statistics, infinite series, multivariate calculus, linear algebra. 2006: Intermediate linear algebra, regression, differential equations, and model validation.
2005: Integrated topics from quantiative sciences that prepare students for advanced computational modeling and data anyalytics courses. Topics include: probablility and statistics, infinite series, multivariate calculus, linear algebra. 2006: Intermediate linear algebra, regression, differential equations, and model validation.
This course develops fundamental analytical and programming skills to complete the “analytic pipeline”, including specifying research questions, selecting/collecting data ethically and responsibly, processing and summarizing datasets, and stating findings, while considering all assumptions made. Students will identify vulnerabilities in analyses, including sources of bias and ethical implications. Some programming skills recommended, but not required. Some prior use of data recommended, but not required.
3605: Mathematical modeling with ordinary differential equations and difference equations. Numerical solution and analysis of ordinary differential equations and difference equations. Stochastic modeling, and numerical solution of stochastic differential equations. 3606: Concepts and techniques from numerical linear algebra, including iterative methods for solving linear systems and least squares problems, and numerical approaches for solving eigenvalue problems. Ill-posed inverse problems such as parameter estimation, and numerical methods for computing solutions to inverse problems. Numerical optimization. Emphasis on large-scale problems.
3605: Mathematical modeling with ordinary differential equations and difference equations. Numerical solution and analysis of ordinary differential equations and differencee equations. Stochastic modeling and numerical solution of stochastic differential equations. 3606: Concepts and techniques from numerical linear algebra, including iterative methods for solving linear systems and least squares problems, and numerical approaches for solving eigenvalue problems. III-posed inverse problems such as parameter estimation, and numerical methods for computing solutions to inverse problems. Numerical optimization. Emphasis on large-scale problems.
Survey of computer science concepts and tools that enable computational science and data analytics. Data structure design and implementation. Analysis of data structure and algorithm performance. Introduction to high-performance computer architectures and parallel computation. Basic operating systems concepts that influence the performance of large-scale computational modeling and data analytics. Software development and software tools for computational modeling. Not for CS major credit.
Basic principles and techniques in data analytics; methods for the collection of, storing, accessing, and manipulating standard-size and large datasets; data visualization; and identifying sources of bias.
Applied econometrics dealing with big data. Theoretical, computational, and statistical underpinnings of big data analysis. The use of econometric models and deep machine learning algorithms to analyze the high-dimensional data sets. Implications in research focusing on economic questions that arise from rapid changes in data availability and computational technology. Materials are hands-on tutorials that come with Python codes and real-world data sets.
Introduction to partial differential equations, including modeling and classification of partial differential equations. Finite difference and finite elements methods for the numerical solution of partial differential equations including function approximation, interpolation, and quadrature. Numerical solution of nonlinear systems of equations. Uncertainty quantification, prediction.
A focused study of concepts and tools that accelerate computational and data science at scale. Design, analysis, optimization, and modeling of application-driven algorithms suitable for state-of-the-art scalable computing platforms. Software development and engineering for scalable computational science.
A technical analytics course. Covers supervised and unsupervised learning strategies, including regression, generalized linear models, regularization, dimension reduction methods, tree-based methods for classification, and clustering. Upper-level analytical methods shown in practice: e.g., advanced naive Bayes and neural networks.
Stochastic modeling methods with an emphasis in computing are taught. Select concepts from the classical and Bayesian paradigms are explored to provide multiple perspectives for how to learn from complex, datasets. There is particular focus on nested, spatial, and time series models.
Capstone research project for Computational Modeling and Data Analytics majors. Cultivates skills including reviewing the literature, creative problem solving, teamwork, critical thinking, and oral, written, and visual communications. Quantitative and computational thinking, informed throughout by ethical reasoning.