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INFO 213 Data Science Programming II 3.0 Credits

Discusses the latest analytic and predictive techniques to solve real world business problems. Focuses on practice rather than theory by using existing Python libraries and tools to produce solutions. Covers practical Python implementations of the basic concepts in mathematics and statistics that are at the core of data science. Introduces Python libraries for the most common models and techniques for data analytics such as clustering, classification, regression, and decision trees.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: INFO 212 [Min Grade: D] and (STAT 201 [Min Grade: D] or MATH 311 [Min Grade: D])

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