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Available courses
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.
- Teacher: Alex Cunningham
Aim of Course:
This course shell holds an assessment test for Church & Dwight Data Analytics Training program.
Aim of Course:
This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Aim of Course:
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.
- Teacher: Gregg Harrington
- Teacher: Bryce Pilcher
- Teacher: Andrew Stewart
- Teacher: Ramzi Ziade
Aim of Course:
This course will teach you how to estimate descriptive quantities and sampling variances from complex surveys, and also how to fit linear and logistic regression models to complex sample survey data.
- Teacher: Brady West
Aim of Course:
This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model.
- Teacher: James Hardin
Aim of Course:
This course covers the application of Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.
- Teacher: Peter Congdon
This course will cover acceptance sampling to determine whether to accept or reject a particular lot of a product -- how to determine sample size, and how make accept/reject decisions.
- Teacher: Riki Riechstein
Aim of Course:
Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.
- Teacher: Everett Smith
Aim of Course:
This course will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories).
- Teacher: Brian Marx