IS517: Methods for Data Science

A dramatic increase in computing power has enabled new areas of data science to develop in statistical modeling and analysis. These areas cover predictive and descriptive learning and bridge between ideas and theory in statistics, computer science, and artificial intelligence. We will cover methods including predictive learning: estimating models from data to predict future outcomes. Regression topics include linear regression with recent advances using large numbers of variables, smoothing techniques, additive models, and local regression. Classification topics include linear regression, regularization, logistics regression, discriminant analysis, splines, support vector machines, generalized additive models, naive Bayes, mixture models and nearest neighbor methods as time permits. We situate the course components in the "data science lifecycle" as part of the larger set of practices in the discovery and communication of scientific findings.

The course moves quickly and includes hands-on computing exercises using Python and other relevant languages.

• Gain broad exposure to core data science methods through lectures and discussion.
• Develop working proficiency through hands-on exercises and computational practice.
• Identify opportunities to apply course concepts in new settings through independent exploration and a course project.

Schedule is subject to revision.