2023-2024 Course Catalog
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Code | Title | Credits |
---|---|---|
Degree Core Requirements | ||
CMDA 3605 | Mathematical Modeling: Methods and Tools 1 | 3 |
CMDA 3606 | Mathematical Modeling: Methods and Tools 1 | 3 |
CMDA 3634 | Computer Science Foundations for Computational Modeling and Data Analytics 1 | 3 |
CMDA 3654 | Introductory Data Analytics and Visualization 1 | 3 |
CMDA 4654 | Intermediate Data Analytics and Machine Learning 1 | 3 |
MATH 2114 | Introduction to Linear Algebra | 3 |
Subtotal | 18 | |
Major Requirements | ||
CMDA 1634 | Discovering Computational Modeling and Data Analytics (Any approved First Year Expereince course will satisfy this requirement.) 1 | 3 |
CMDA 2005 | Integrated Quantitative Sciences 1,2 | 6 |
CMDA 2006 | Integrated Quantitative Sciences 1,2 | 6 |
CS 1064 | Introduction to Programming in Python 1,3 | 3 |
CS 2064 | Intermediate Programming in Python 1,3 | 3 |
CS 2114 | Software Design and Data Structures 1 | 3 |
Subtotal | 24 | |
Restricted Electives | ||
Select four of the following: | 12 | |
Big Data Economics 1 | ||
Intermediate Topics in Mathematical Modeling 1 | ||
Scalable Computing for Computational Modeling and Data Analytics 1 | ||
Computational Intensive Stochastic Modeling 1 | ||
Data Structures and Algorithms 1 | ||
Data and Algorithm Analysis 1 | ||
Machine Learning 1 | ||
Introduction to Data Base Management Systems 1 | ||
Applied Combinatorics and Graph Theory 1 | ||
Linear Algebra II 1 | ||
Cryptography 1 | ||
Cryptography 1 | ||
Fourier Series and Partial Differential Equations 1 | ||
Fourier Series and Partial Differential Equations 1 | ||
Introduction to Numerical Analysis 1 | ||
Introduction to Numerical Analysis 1 | ||
Methods of Statistical Computing 1 | ||
Experimental Designs 1 | ||
Methods of Regression Analysis 1 | ||
Introduction to Statistical Genomics 1 | ||
Applied Bayesian Statistics 1 | ||
Applied Multivariate Analysis 1 | ||
Introduction to Categorical Data Analysis 1 | ||
Applied Statistical Time Series Analysis 1 | ||
Introduction to Computational Physics 1 | ||
Introduction to Computational Physics 1 | ||
Subtotal | 12 | |
Free Electives | ||
Select remaining credits of free electives | 19 | |
Subtotal | 19 | |
Pathways to General Education | ||
Pathways Concept 1 - Discourse | ||
Select six credits in Pathway 1f | 6 | |
Select three credits in Pathway 1a | 3 | |
Pathways Concept 2 - Critical Thinking in the Humanities | ||
Select six credits in Pathway 2 | 6 | |
Pathways Concept 3 - Reasoning in the Social Sciences | ||
Select six credits in Pathway 3 | 6 | |
Pathways Concept 4 - Reasoning in the Natural Sciences | ||
Select six credits in Pathway 4 | 6 | |
Pathways Concept 5 - Quantitative and Computational Thinking | ||
MATH 1225 | Calculus of a Single Variable (5F) | 4 |
MATH 1226 | Calculus of a Single Variable (5F) | 4 |
CMDA 4864 | Computational Modeling and Data Analytics Capstone Project (5A) 1 | 3 |
Pathways Concept 6 - Critique and Practice in Design and the Arts | ||
Select three credits in Pathway 6a | 3 | |
Select three credits in Pathway 6d | 3 | |
Pathways Concept 7 - Critical Analysis of Identity and Equity in the United States | ||
Select three credits in Pathway 7 | 3 | |
Subtotal | 47 | |
Total Credits | 120 |
Course will be used for computing the "in major" GPA.
MATH 2204 Introduction to Multivariable Calculus, MATH 2214 Introduction to Differential Equations, STAT 3005 Statistical Methods, STAT 3006 Statistical Methods, & STAT 3104 Probability and Distributions will substitute for CMDA 2005 Integrated Quantitative Sciences and CMDA 2006 Integrated Quantitative Sciences.
CS 1114 Introduction to Software Design will substitute for CS 1064 Introduction to Programming in Python and CS 2064 Intermediate Programming in Python.
120 credit hours are required for graduation. These credits must include the courses required for the major (see above sections). To graduate, a student must have at least a 2.0 in-major GPA and overall GPA. If 120 credit hours are reached and a student does not meet the GPA requirement, the student must take additional in-major courses to raise the in-major GPA to a 2.0.
Some courses have prerequisites. Students are required to double check course prerequisites and equivalents. Please see your advisor or consult the Undergraduate Course Catalog for more information.
Three conditions are required for continuation in the major:
Upon having attempted 72 total credit hours (including transfer, AP, advanced standing, credit by examination, course withdrawal) majors must have completed the following courses with grades of C— or better in a maximum of two attempts (including attempts that were withdrawn): MATH 1225 Calculus of a Single Variable; MATH 1226 Calculus of a Single Variable; MATH 2114 Introduction to Linear Algebra; (CMDA 2005 Integrated Quantitative Sciences and CMDA 2006 Integrated Quantitative Sciences) or (STAT 3005 Statistical Methods, STAT 3006 Statistical Methods, STAT 3104 Probability and Distributions; MATH 2204 Introduction to Multivariable Calculus, MATH 2214 Introduction to Differential Equations).
Upon having attempted 72 total credit hours (including transfer, AP, advanced standing, credit by examination, course withdrawal) majors must have completed the following courses with grades of C or better in a maximum of two attempts (including attempts that were withdrawn): (CS 1064 Introduction to Programming in Python and CS 2064 Intermediate Programming in Python) or CS 1114 Introduction to Software Design; CS 2114 Software Design and Data Structures.
Students who did not successfully complete at least two years of a single foreign, classical, or sign language during high school must successfully complete six credit hours of a single foreign, classical, or sign language at the college level. Courses taken to meet this requirement do not count toward the hours required for graduation. Please consult the Undergraduate Catalog for details.
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