### Computational Modeling and Data Analytics

**Division Leader:** M. Embree

**Program Manager:** N. Dragovic

**Principle Faculty:** J. Chung, M. Chung, E. de Sturler, X. Deng, R. Gramacy, S. Gugercin, A. Habibnia, P. Haskell, R. Hewitt, L. House, L. Johnson, I. Kim, S. Leman, C. Lucero, E. Martin, C. North, L. Pillonen, M. Pleimling, N. Ramakrishnan, C. Ribbens, S. Sengupta, E. Smith, T. Warburton, and L. Zeitsman

### Overview

The Computational Modeling and Data Analytics (CMDA) program is a joint effort of the departments of Mathematics, Statistics, and Computer Science. It resides in, and is organized as a division of, the College of Science's Academy of Integrated Science. CMDA courses impart the emerging concepts and techniques from mathematics and statistics, with a decidedly computational approach, that are most in demand by a data-driven world. They prepare students as quantitative scientists ready to engage data and modeling problems wherever they may occur. CMDA is Virginia Tech's Big Data degree.

### Bachelor of Science in Computational Modeling and Data Analytics

All CMDA majors are required to own specified personal computers and software. Consult the division for details.

A special brochure describing the division and the B.S. program is available from the division's webpage or upon request.

#### Degree Requirements

The graduation requirements in effect at the time of graduation apply. When choosing the degree requirements information, always choose the year of your expected date of graduation. 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. However, the university will not alter degree requirements less than two years from the expected graduation year unless there is a transition plan for students already in the degree program.

Please visit the University Registrar website at http://registrar.vt.edu/graduation-multi-brief/index1.html for degree requirements.

### 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) (see "Academics") 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 http://registrar.vt.edu/graduation-multi-brief/index1.html.

### Computer Literacy

Most CMDA courses involve the use of statistical and/or mathematical software, primarily (but not limited to) MATLAB, R, and Python. Experience with the software is not expected, but students should have familiarity with either the Windows or Macintosh operating system.

### Undergraduate Course Descriptions (CMDA)

2005-2006: INTEGRATED QUANTITATIVE SCIENCES

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. Pre: CS 1114, (MATH 1206 or MATH 1226) for 2005; 2005, MATH 2114, CMDA 2206 for 2006. Co: MATH 2114 for 2005. (6H,6C)

2984: SPECIAL STUDY

Variable credit course.

3605-3606: MATHEMATICAL MODELING: METHODS AND TOOLS

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. Pre: (CS 1114 or MATH 3054), (MATH 2114 or MATH 2114H), (MATH 2204 or MATH 2204H or CMDA 2006), (MATH 2214 or MATH 2214H or CMDA 2006) for 3605; 3605 for 3606. (3H,3C)

3634 (CS 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. 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. Pre: CS 2114. (3H,3C)

3654 (CS 3654) (STAT 3654): INTRODUCTORY DATA ANALYTICS & 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. Pre: (CS 1114 or CS 1044 or CS 1054 or CS 1064), (MATH 2224 or MATH 2224H or MATH 2204 or MATH 2204H or MATH 2406H or CMDA 2005), (STAT 3006 or STAT 4705 or STAT 4714 or CMDA 2006). (3H,3C)

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. Numerical solution of nonlinear systems of equations. Uncertainty quantification, prediction. Pre: 3606. (3H,3C)

4654 (CS 4654) (STAT 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. Upper-level analytical methods shown in practice: e.g., advanced naive Bayes and neural networks. Pre: (STAT 3654 or CMDA 3654 or CS 3654), (STAT 3104 or STAT 4705 or STAT 4714 or CMDA 2006). (3H,3C)

4664 (STAT 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. There is particular focus on nested, spatial, and time series models. Pre: 2006. (3H,3C)

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. Pre: 3605, 3634, 3654. (3H,3C)

4964: FIELD STUDY

Variable credit course.

4974: INDEPENDENT STUDY

Variable credit course.

4984: SPECIAL STUDY

Variable credit course.

4994: UNDERGRADUATE RESEARCH

Variable credit course.