Data Science, Minor

Data Science applies core concepts in computer science, statistics and mathematics to problems in a wide variety of fields, from physical, social, biomedical, and behavioral sciences to arts and humanities.  The minor targets students with strong analytical abilities and some existing programming experience, and requires courses in statistics, data-centric programming, data management, and data analysis.  It also points to courses across the University that deal with data in areas of importance to Data Science.

SEAS Second Major or Minor Option

Students interested in a second major (College students only) or minor with SEAS are required to meet with the Undergraduate Curriculum Chair from the major/minor department you wish to declare to discuss requirements and obtain approval on the Second Major or Minor form. The approved form must be returned to the SEAS Research and Academic Services Office, 109 Towne Building.

Data Science Minor

CIS 120Programming Languages and Techniques I1
CIS 419Applied Machine Learning1
or STAT 471 Modern Data Mining
NETS 212Scalable and Cloud Computing1
ENM 321Engineering Statistics1
or ESE 302 Engineering Applications of Statistics
or STAT 431 Statistical Inference
Select two Data Science electives from two of the following required categories: 12
Data-Centric Programming
Introduction to Computer Programming
Programming Languages and Techniques I
Business Computer Languages
Introduction to Scientific Computing
Statistical Computing with R
Data Analytics and Statistical Computing
Engineering Probability
Engineering Applications of Statistics
Engineering Statistics
Statistical Inference
Modern Data Mining
Applied Probability Models in Marketing
Data Collection, Representation, Management and Retrieval
Big Data Analytics
Database and Information Systems
Database and Information Systems
Scalable and Cloud Computing
Crowdsourcing and Human Computation
Developing Tools for Data Access and Analysis (VBA and SQL Programming)
Sample Survey Design
Data Analysis
Applied Machine Learning
Introduction to Machine Learning
Machine Learning
Artificial Integlligence
Artificial Integlligence
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
Introduction to Large-Scale Data Science
Other Electives
Visualizing the Past.
Technology and Policy
Total Course Units6

Approval required.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2018 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.