An advanced course in data science with real-world applications. Topics will include data management, statistical analyses of data, machine-learning algorithms, estimation of model parameters to collected data, and visualization of data and related findings. Students will employ computational tools and report findings. Cross-listed with MATH 3092.
Grade Basis: L
Credit hours: 3.0
Lecture hours: 3.0
Prerequisites:
- MATH 1114 - Introduction to Statistics
