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Undergraduate Courses

Students in any major at Vanderbilt can help prepare themselves for future education or employment in Data Science with foundational courses in mathematics, statistics, and computation, along with a variety of elective courses. Examples of existing Vanderbilt courses that cover key Data Science topics are listed below.

Computer Programming

At least two semesters of computer programming:
CS 1101          Programming and Problem Solving (with Java)
OR
CS 1104          Programming and Problem Solving (with Python)
and
CS 2201          Program Design and Data Structures
OR
CS 2204          Program Design and Data Structures for Scientific Computing.

Calculus and Linear Algebra

Multivariate calculus and basic linear algebra highly recommended:
MATH 2310   Multivariable Calculus with Matrix Algebra
MATH 2400   Differential Equations with Linear Algebra
MATH 2410   Methods of Linear Algebra
MATH 2501   Multivariable Calculus and Linear Algebra
MATH 2600   Linear Algebra

Probability and Statistics

A variety of introductory courses are offered:
BME 3200      Analysis of Biomedical Data
BSCI 3270      Statistical Methods in Biology
CE 3300         Risk, Reliability, and Resilience Engineering
ECON 1510    Intensive Economic Statistics
MATH 2810    Probability and Statistics for Engineering
MATH 2820    Introduction to Probability and Mathematical Statistic
PSY 2100        Quantitative Methods
PSY-PC 2110  Introduction to Statistical Analysis
SOC 2100       Statistics for Social Scientists

Machine Learning

CS 4262          Foundations of Machine Learning
HOD 3200      Introduction to Data Science
MATH 3670   Mathematical Data Science
NSC 3270       Computational Neuroscience

Electives

Intermediate / Advanced Programming, Modeling, Simulation
CS 3250          Algorithms
CS 3251          Intermediate Software Design
CS 3274          Modeling and Simulation
PSY 8218       Computational Modeling
SC 3250          Scientific Computing Toolbox
SC 3260          High Performance Computing

Intermediate / Advanced Probability, Statistics, and Data Analysis
BIOS 6311      Principles of Modern Biostatistics
BIOS 6312      Modern Regression Analysis
BIOS 6341      Fundamentals of Probability
BIOS 6342      Contemporary Statistical Inference
BIOS 7362      Advanced Statistical Inference and Statistical Learning
BIOS 8366      Advanced Statistical Computing
CE 3890          Data Analytics for Engineers
ECON 3035    Econometric Methods
ECON 3330    Economics of Risk
ECON 4050    Topics in Econometrics
HOD 3275      Practical Meta-analysis
MATH 2821   Introduction to Applied Statistics
MATH 3640   Probability
MATH 3641   Mathematical Statistics
MATH 4650   Financial Stochastic Processes
PSY 3891       Bayesian Cognitive Modeling
PSY-PC 3738 Introduction to Item Response Theory
PSY-PC 3743 Factor Analysis
PSY-PC 3749 Applied Nonparametric Statistics
PSY-PC 3746 Multivariate Statistics
PSY-PC 3730 Applied Latent Class and Mixture Modeling
PSY-PC 3737 Structural Equation Modeling
PSY-PC 3732 Latent Growth Curve Modeling
PSY-PC 3727 Modern Robust Statistical Methods

Machine Learning, Visualization, Data Science
ANTH 3261    Introduction to Geographic Information Systems and Remote Sensing
BMIF 6310     Foundations of Bioinformatics
BMIF 7380     Data Privacy in Biomedicine
CS 3265         Introduction to Database Management Systems
CS 4266         Topics in Big Data
CS 4287         Principles of Cloud Computing
CS 4260         Artificial Intelligence
CS 6362         Advanced Machine Learning
DS 3860         Undergraduate Research in Data Science
EECE 4354      Computer Vision
ECON 3750    Econometrics for Big Data
MATH 3130   Fourier Analysis
MATH 3670   Mathematical Data Science
MATH 4620   Linear Optimization
MATH 4630   Nonlinear Optimization
PSY-PC 3751 Exploratory and Graphical Data Analysis