I. Preparatory Courses (8 credits)

• a. One introductory course in computer programming selected from the following list:

• CSCI 149 - Introduction to Computer Science in JavaScript
• CSCI 150 - Introduction to Computer Science in Python
• STAT 120 - Statistical Computing in R

•  

• b. Introductory Statistics

• MATH 117 - Introductory Statistics

II. Core Courses (10 credits)

• DATA 200 - Data Science: Introduction, History, and Case Studies
• DATA 390 - Applied Data Science

•  

• One of the following courses (4 credits) in principles, methods, and tools:

• DATA 251 - Data Visualization
• DATA 252 - Modeling and Simulation
• DATA 253 - Data Analysis

III. Elective Courses (8 credits)

At least 4 credits separate from DATA 251-253

• DATA 240 - Statistical Machine Learning

• OR

• STAT 240 - Statistical Machine Learning

•  

• DATA 251 - Data Visualization
• DATA 252 - Modeling and Simulation
• DATA 253 - Data Analysis
• DATA 299 - Data Science Across the Curriculum
• HIST 215 - History by the Numbers

•  

• ESS 302 - Geographic Information Systems

• OR

• BIOL 302 - Geographic Information Systems

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• CSCI 330 - Databases & Information Management

• OR

• DATA 330 - Information Management

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• PH 340 - Epidemiology

•  

• NEUR 366 - Computational Modeling of Neural Systems

• OR

• PHYS 366 - Computational Modeling of Neural Systems