Modeling and Representation of Data

Catalog description

Extract actionable insights and draw inference from real world datasets. Methods for dealing with outliers and missing data, data that does not conform to standard modeling assumptions, data representations and particularly time series data analysis. Principles of causal inference and common frameworks for analysis. Develop critical thinking about issues that affect the success of models in data science. This course will lay the foundation for more in-depth study into statistical techniques for practical data analysis. Open only to Interdisciplinary Data Science students.