Please refer to the current undergraduate catalog for full requirements. If any discrepancy exists between the requirements below and those listed in the undergraduate catalog, requirements in the catalog always prevail.
Introduction to Data Science (3 hours)
| DS 1000 | Data Science: How Data Shape Our World |
DS 1000 is an introduction to data science and provides a broad overview of data science applications and techniques. As an introductory course, students are highly encouraged to take DS 1000 early in their academic careers, and should not depend on enrollment in this course in their senior year.
Students who are currently enrolled in or have already taken DS 3100 or PSCI 2300 may also request substitutions which will be considered on a case-by-case basis. Please fill out the DS 1000 substitution form here for consideration. Email undergraduate.datascience@vanderbilt.edu if you have questions and for substitution approvals.
Computer Programming (3 hours)
One of the following (see also What Programming Course To Take?):
| DS 1100 | Applied Programming and Problem Solving with Python |
| CS 1100 | Applied Programming and Problem Solving with Python |
| CS 2201 | Program Design and Data Structures (prereq: CS 1101) |
| CS 2204 | Program Design and Data Structures for Scientific Computing (prereq: CS/DS1100 or CS 1104) |
Introduction to Statistics (3 hours)
One of the following:
| DS 2100 | Statistics for Data Science |
| BME 2400 | Quantitative Methods I: Statistical Analysis |
| BSCI 3270 | Statistical Methods in Biology |
| CE 3300 | Risk, Reliability, and Resilience Engineering |
| ECON 1500 | Economic Statistics |
| ECON 1510 | Intensive Economic Statistics |
| MATH 2810 | Probability and Statistics for Engineering |
| MATH 2821 | Introduction to Applied Statistics |
| PSY 2100 | Quantitative Methods |
| PSY-PC 2110 | Introduction to Statistical Analysis |
| SOC 2100 | Statistics for Social Scientists |
Data Science Fundamentals (4 hours)
| DS 3100 | Fundamentals of Data Science |
Elective (3 hours)
One course from the list of electives below.
Electives in data science are courses with various combinations of computation, visualization, simulation, statistics, psychometrics, and/or machine learning aimed at understanding and explaining data in the physical, life, or social sciences, engineering, arts, or the humanities, or courses that examine the impact of data on society and its institutions. Students and faculty are encouraged to petition for new courses with data science content to be considered as electives for the minor.
A. Intermediate / Advanced Programming, Modeling, Simulation
| ASTR 3800 | Structure Formation in the Universe |
| BME 4310 | Modeling Living Systems for Therapeutic Bioengineering |
| BSCI 3271 | Programming for Biologists |
| CHBE 4830 | Molecular Simulation |
| CHEM 5410 | Molecular Modeling Methods |
| CHEM 5420 | Computational Structural Biochemistry |
| EES 4760 | Agent and Individual Based Computational Modeling |
| MATH 3660 | Mathematical Modeling in Economics |
| ME 4271 | Fundamentals of Robotic Manipulators |
| ME 4284 | Modeling and Simulation of Dynamic Systems |
| ME 4263 | Computational Fluid Dynamics and Multiphysics Modeling |
| ME 4275 | Finite Element Analysis |
| PHYS 3790 | Computational Physics |
| PSY 4218 | Computational Cognitive Modeling |
| PSY 4219 | Scientific Computing for Psychological and Brain Sciences |
| PSY 4775 | Models of Memory |
| SC 3250 | Scientific Computing Toolbox |
| SC 3260 | High Performance Computing |
B. Intermediate / Advanced Probability, Statistics, and Data Analysis
| ASTR 8070 | Astrostatistics |
| 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 |
| BME 4420 | Quantitative and Functional Imaging |
| BSCI 5890 | Special Topics in Biological Sciences: Big Data for Biologists (Offered Spring 2024) |
| CE 4320 | Data Analytics for Engineers |
| CSET 3410 | Telling Stories with Data |
| ECON 3032 | Applied Econometrics |
| ECON 3035 | Econometric Methods |
| ECON 3330 | Economics of Risk |
| ECON 4050 | Topics in Econometrics |
| EES 3310 | Global Climate Change |
| MATH 3640 | Probability |
| MATH 3641 | Mathematical Statistics |
| MATH 4650 | Financial Stochastic Processes |
| MHS 3120 | Medicine, Technology, and Society |
| PPS 3200 | Research Methods for Public Policy Analysis |
| PPS 3250 | Advanced Quantitative Methods for Public Policy |
| PSCI 2310 | Understanding Policy Data: Analysis and Interpretation |
| PSCI 3249 | American Public Opinion and American Politics |
| PSCI 3893 | Selected Topics in American Government – Media & Data in American Politics |
| PSY 4220 | Bayesian Cognitive Modeling |
| PSY-PC 2120 | Statistical Analysis |
| PSY-PC 3722 | Psychometric Methods |
| PSY-PC 3724 | Psychometrics |
| PSY-PC 3738 | Introduction to Item Response Theory |
| PSY-PC 3743 | Factor Analysis |
| PSY-PC 3749 | Applied Nonparametric Statistics |
| PSY-GS 8867 | Multivariate Statistics (formerly PSY-PC 3746) |
| PSY-PC 3737 | Structural Equation Modeling |
| PSY-PC 3732 | Latent Growth Curve Modeling |
| PSY-PC 3727 | Modern Robust Statistical Methods |
| PSY-PC 7878 | Statistical Consulting |
C. Machine Learning, Visualization, Data Science
| ANTH 3050 | Artificial Intelligence and Material Culture |
| ANTH 3261 | Introduction to Geographic Information Systems and Remote Sensing |
| ANTH 3867 | Digital Archaeology |
| ASTR 8080 | Data Mining in Large Astronomical Surveys |
| BME 3890 | Computational Genomics |
| BME 4420 | Quantitative and Functional Imaging |
| BMIF 6310 | Foundations of Bioinformatics |
| BMIF 6315 | Methodological Foundations of Biomedical Informatics |
| BMIF 7380 | Data Privacy in Biomedicine |
| BSCI 3272 | Genome Science |
| CS 3265 | Introduction to Database Management Systems |
| CS 3891 | Special Topics: Social Network Analysis |
| CS 3892 | Projects in Machine Learning |
| CS 4260 | Artificial Intelligence |
| CS 4266 | Topics in Big Data |
| CS 4267 | Deep Learning |
| CS 6362 | Advanced Machine Learning |
| CS 8395 | Visual Analytics & Machine Learning |
| CS 8395 | Special Topics – Selected Topics in Deep Learning |
| DS 3891 | Special Topics in Data Science – Intro to Generative Artificial |
| DS 3891 | Special Topics in Data Science – Technical Consulting in DS |
| ECE 4363 | Applied Statistical Machine Learning |
| ECE 4354 | Computer Vision |
| ECON 3750 | Econometrics for Big Data |
| HIST 1590 | Artificial Intelligence and Society |
| MATH 3130 | Fourier Analysis |
| MATH 3670 | Mathematical Data Science |
| MATH 4620 | Linear Optimization |
| MATH 4630 | Nonlinear Optimization |
| MHS 3890 | Special Topics – Introduction to Data Visualization |
| MTEC 2350 | Artificial Intelligence and the Creative Musician |
| NSC 3270 | Computational Neuroscience |
| PSY-PC 3751 | Exploratory and Graphical Data Analysis |
| PSY-PC 7500-03 | Special Topics Psychology and Human Development-Neural Network Models of Cog Dev (Offered Spring 2024) |
| SOC-3242 | AI in Social Systems |
D. Research Hours in Data Science
| DS 3850 | Undergraduate Research in Data Science |
Students electing the undergraduate minor in Data Science must follow academic regulations regarding minors in their home school, including but not limited to regulations regarding unique hours. Additional credit hours in Data Science that must be earned because of college-specific regulations regarding unique hours must be earned by taking additional courses chosen from the list of electives.
If you have questions about the Data Science Minor or Immersion opportunities in data science, please email us at undergraduate.datascience@vanderbilt.edu.