Course Website Locator: epi288-01

Harvard School of Public Health

The following course websites match your request:

Winter 2010

Dr. N. Cook, Dr. E.F. Cook
2.50 credits
Lecture, computer lab. Eight 3-hour lectures and with daily 2-hour computer labs over two weeks.

This course will present an introduction to the methods of data mining and predictive modeling, with applications to both genetic and clinical data. Basic concepts and philosophy of supervised and unsupervised data mining as well as appropriate applications will be discussed. Topics covered will include multiple comparisons adjustment, cluster analysis, self-organizing maps, principal component analysis, and predictive model building through logistic regression, classification and regression trees (CART), multivariate adaptive splines (MARS), neural networks, random forests, and bagging and boosting.
Course Activities: Computer labs.
Course Note: Students should be familiar with logistic regression (EPI236, BIO213, BIO210, or equivalent); signature of instructor required; no auditors.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Winter 2009

Dr. N. Cook, Dr. E.F. Cook
2.50 credits
Lecture, computer lab. Eight 3-hour lectures and with daily 2-hour computer labs over two weeks.

This course will present an introduction to the methods of data mining and predictive modeling, with applications to both genetic and clinical data. Basic concepts and philosophy of supervised and unsupervised data mining as well as appropriate applications will be discussed. Topics covered will include multiple comparisons adjustment, cluster analysis, self-organizing maps, principal component analysis, and predictive model building through logistic regression, classification and regression trees (CART), multivariate adaptive splines (MARS), neural networks, random forests, and bagging and boosting.
Course Activities: Computer labs.
Course Note: Students should be familiar with logistic regression (EPI236, BIO213, BIO210, or equivalent); signature of instructor required; no auditors.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Winter 2008

Dr. N. Cook, Dr. E.F. Cook
2.50 credits
Lecture, computer lab. Eight 3-hour lectures and with daily 2-hour computer labs over two weeks.

This course will present an introduction to the methods of data mining and predictive modeling, with applications to both genetic and clinical data. Basic concepts and philosophy of supervised and unsupervised data mining as well as appropriate applications will be discussed. Topics covered will include multiple comparisons adjustment, cluster analysis, self-organizing maps, principal component analysis, and predictive model building through logistic regression, classification and regression trees (CART), multivariate adaptive splines (MARS), neural networks, random forests, and bagging and boosting.
Course Activities: Computer labs.
Course Note: Students should be familiar with logistic regression (EPI236, BIO213, BIO210, or equivalent); signature of instructor required; no auditors. Course dates: January 14-24, from 9:30 am to 12:20 pm, labs from 1:30 pm to 3:20 pm.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Winter 2007

Dr. N. Cook, Dr. E.F. Cook
2.50 credits
Lecture, computer lab. Eight 3-hour lectures and 5 2-hour computer labs over two weeks.

This course will present an introduction to the methods of data mining and predictive modeling, with applications to both genetic and clinical data. Basic concepts and philosophy of supervised and unsupervised data mining as well as appropriate applications will be discussed. Topics covered will include multiple comparisons adjustment, cluster analysis, principal component analysis, and predictive model building through logistic regression, classification and regression trees (CART), multivariate adaptive splines (MARS), and neural networks.
Course Activities: Computer labs.
Course Note: Students should be familiar with logistic regression (EPI236, BIO213, BIO210, or equivalent); signature of instructor required; no auditors. Course dates TBA, from 9:30 am to 12:30 pm, labs from 1:30 pm to 3:30 pm. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

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