Program Overview

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Overview of the Computational Modeling and Data Analytics Program

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.

Program Options

In addition to the standard degree option, CMDA offers specialized options in:

• Biological Sciences
• Cryptography & Cybersecurity
• Economics
• Geosciences
• Physics

Capstone Project

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.

Scholarships and Research Grants

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.

Bachelor of Science in Computational Modeling and Data Analytics

Satisfactory Progress

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) and toward the degree.

Computer Literacy

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.

Faculty and Staff

• 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

Undergraduate Course Descriptions

CMDA 1634 - Discovering Computational Modeling and Data Analytics

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.

CMDA 1984 - Special Study

Variable credit course.

CMDA 2005 - Integrated Quantitative Sciences

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.

CMDA 2006 - Integrated Quantitative Sciences

Intermediate linear algebra, regression, differential equations, and model validation. A student can earn credit for at most one of CMDA 2006 and MATH 2214.

CMDA 2014 - Data Matter

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.

CMDA 2974 - Independent Study

Variable credit course.

CMDA 2984 - Special Study

Variable credit course.

CMDA 2984E - Special Study

Variable credit course.

CMDA 2994 - Undergraduate Research

Variable credit course.

CMDA 3274 - Introduction Sports Analytics

Introduction to sports analytics, sources of sports analytics data and data collection methods, visualization techniques, game performance statistics, inferential statistics and predictive modeling techniques for sports data.

CMDA 3605 - Mathematical Modeling: Methods and Tools

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.

CMDA 3606 - Mathematical Modeling: Methods and Tools

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.

CMDA 3634 - Computer Science Foundations for Computational Modeling & Data Analytics

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.

CMDA 3654 - Introductory Data Analytics and Visualization

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.

CMDA 3900 - Bridge Experience

Application of academic knowledge and skills to in a work-based experience aligned with post-graduation goals using research-based learning processes.

CMDA 4274 - Sports Analytics Statistical Research

Statistical analysis of sports data. Game performance statistics and expected scores. Analysis of player performance, player tracking, team performance, and sports betting. Bayesian methods and prediction models applied to sports data.

CMDA 4314 - Big Data Economics

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.

CMDA 4604 - Intermediate Topics in Mathematical Modeling

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.

CMDA 4634 - Scalable Computing for Computational Modeling and Data Analytics

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.

CMDA 4654 - Intermediate Data Analytics and Machine Learning

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.

CMDA 4664 - Computational Intensive Stochastic Modeling

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.

CMDA 4864 - Computational Modeling and Data Analytics Capstone Project

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.

CMDA 4964 - Field Study

Variable credit course.

CMDA 4974 - Independent Study

Variable credit course.

CMDA 4984 - Special Study

Variable credit course.

CMDA 4994 - Undergraduate Research

Variable credit course.