Data Science, Minor

Programming Languages and Techniques I
Applied Machine Learning 2
Modern Data Mining
Machine Learning
Scalable and Cloud Computing
Big Data Analytics
Engineering Statistics
Statistics for Data Science
Statistical Inference
Computational Data Exploration
Introduction to Scientific Computing
Business Computer Languages
Statistical Computing with R (Cannot be taken by SEAS students)
Data Analytics and Statistical Computing
Foundations of Data Science
Applications of Scientific Computing.
Discrete Probability, Stochastic Processes, and Statistical Inference
Engineering Probability
Statistics for Biologists
Probability
Applied Probability Models in Marketing
Internet and Web Systems
Database and Information Systems
Database and Information Systems
Crowdsourcing and Human Computation
Developing Tools for Data Access and Analysis (VBA and SQL Programming)
Sample Survey Design
Applied Machine Learning
Applied Machine Learning
Artificial Integlligence
Machine Learning
Data and Analysis for Marketing Decisions
Special Topics: Experiments for Business Decision Making
Decision Support Systems
Predictive Analytics for Business
Forecasting Methods for Management
Modern Data Mining
Modern Regression for the Social, Behavioral and Biological Sciences
Applied Econometrics I
Theory of Networks
Models for Marketing Strategy
Computer Simulation Models
Mathematical Modeling and its Application in Finance
Stochastic Processes
Visualizing the Past.
Technology and Policy

Approval required.

Both CIS 419/519 and CIS 520 cannot be taken for credit toward the minor.