Admission Requirements

Math 222, Math 551, Stat 350, or equivalents.

Mathematical Background

• MATH 620 - Convex Optimization for Data Science Credits: 3

Statistics Background

• STAT 705 - Regression and Analysis of Variance Credits: 3

• or

• STAT 713 - Applied Linear Statistical Models Credits: 3

Data Science in Context (Choose one of the following)

• GWSS 435 - Race/Sex/Science Credits: 3
• HIST 320 - History of Technology Credits: 3
• HIST 519 - Science and Technology in the United States  Credits: 3
• PHILO 386 - Philosophy of Computer Science and Software Engineering Credits: 3
• CRIM 550 - Technocrime, Security, and Society  Credits: 3

Advanced Techniques for Data Science (Select two classes from the following)

• IMSE 785 - Big Data Analytics Credits: 3
• MATH 725 - The Mathematics of Data and Networks I Credits: 3
• MATH 726 - The Mathematics of Data and Networks II Credits: 3
• STAT 745 - Statistical Graphics Credits: 3
• STAT 760 - Optimization for Data Science Credits: 3
• STAT 761 - Discrete Optimization and Scalability for Data Science Credits: 3
• STAT 764 - Applied Spatio-Temporal Statistics Credits: 3
• STAT 766 - Applied Data Mining/Machine Learning and Predictive Analytics Credits: 3
• STAT 768 - Applied Bayesian Modeling and Prediction Credits: 3
• CC 535 - Applied Data Science  Credits: 3
• CIS 531 - Introduction to Programming Techniques for Data Science and Analytics  Credits: 3
• Topics courses that address Data Science, with the approval of the Certificate Coordinator.

Students are strongly encouraged to have a class that addresses programming if they don’t already have a strong background in that area.

Total credit hours: 15