Degree Tracks

The concentration requirements can be fulfilled via any of the four tracks:

Stats Principles/Methods

The general track is the most flexible track, providing a foundation in principles and techniques for statistical theory, methods, and applications. This foundation can be applied to a myriad of fields. As John Tukey said, "The best thing about being a statistician is that you get to play in everybody else's backyard."

Data Science

The data science track explores the interface of statistics and computer science. Courses involve a mixture of these fields, with applications to areas such as prediction, recommendation systems, and analysis of massive data sets.

ML

The Machine Learning track focuses on statistical and computational algorithms for tasks such as using data (possibly high-dimensional) to make predictions and decisions under uncertainty, and for classification and clustering. Topics in statistical machine learning such as supervised learning, unsupervised learning, and reinforcement learning are emphasized.

BCB

The Bioinformatics and Computational Biology (BCB) track mixes together biology, statistics, and computation, giving models and tools for studying biological data such as gene and protein sequences. This is motivated in part by the recent explosion of size and complexity of data in the biological sciences, which has required the development of new statistical methods and models, such as models for gene and protein motifs search, phylogenetic reconstruction, and gene expression analysis.