Available courses

Aim of Course:

This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.

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Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

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After taking this course, participants will be able to design appropriate conjoint and choice studies, using surveys, panels, and designed experiments. They will also be able to analyze and interpret the resulting data.

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Aim of Course:

This course covers how to read, understand, manipulate, and use data. There is no prerequisite knowledge for this course, but it does require access to Excel.

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This course covers how to develop and implement data science projects in a responsible and ethical way. It includes a review of predictive modeling, but some prior familiarity is helpful.

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This course will teach you how to build and monitor a machine learning pipeline, given a predictive model that has been provided by a data science team.

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This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

Aim of Course:

This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.