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BST 261 - Data Science II or return to Course Catalog Search

203514 – Section 1   

SchoolDepartmentFaculty
Harvard T.H. Chan School of Public HealthBiostatisticsHeather Mattie
TermDay and TimeLocation
Spring 2 2018  (show academic calendar)MW   9:45 a.m. - 11:15 a.m.Kresge 200 (HSPH)
Credits
2.5  (show credit conversion for other schools)
Credit Level
Graduate

Description
This course is the second course in the foundational sequence of the School?s newly approved Master?s Degree in Health Data Science. The course will build upon our existing course, BST260 ?Introduction to Data Science?, in presenting a set of tools for modeling and understanding complex datasets. Specifically, the course will provide practical regression and tree-based techniques for big data. Specific topics that will be covered include: linear model selection and regularization: LASSO and regularization; principal component regression and partial least squares; tree-based methods: decision trees; bagging, random forests, and boosting; unsupervised learning: principal components analysis, cluster analysis.Programming (Python and R) and case studies will be used throughout the course to provide hands-on training in these concepts.Prerequisites: BST260 or permission of instructor

Prerequisite(s)
Prerequisite: BST260

 
Cross Registration
Eligible for cross-registration
With permission of instructor/subject to availability

MIT students please cross register from MIT's Add/Drop application.

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