Course Prerequisites
- Graduate standing
- Recommended: Familiarity with programming language Python and basic knowledge of multivariate calculus, statistical inference, and linear algebra
Course Description
- A broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, regression, clustering, and classification; with applications to scientific and online data. Cross Listings: CSCE 676 and STAT 639
Course Objectives
- Acquire knowledge of foundations and application of methods in data mining and data analysis; prepare students to use methods and tools of data science in research in methods or applications.
- Conduct exploratory data analysis including visualization and summarization
- Apply selected unsupervised machine learning methods to data analytical problems
- Apply selected supervised machine learning methods to data analytical problems
- Select appropriate machine learning method applicable to common problem types
- Understand usage rationale, underpinnings, and limitations of machine learning methods
- Apply common libraries and tools to data analytical problems