Available courses

Bayesian Statistics in R 092322

Aim of Course:

After taking this course, you will be able to install and run rjags, a program for Bayesian analysis within R.  Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.

Mixed and Hierarchical Linear Models 092322

Aim of Course:


This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models. It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. The course aims at providing a basic understanding and knowledge of the mixed effect models that will allow you to use them in practice.

PSAIL Data Sci ML-Ops 091622

Aim of Course:

This course will stress the application of DOE rather than statistical theory. With a 12-step checklist, it covers full and fractional factorial designs, Plackett-Burman, Box-Behnken, Box-Wilson and Taguchi designs.

Survey Analysis 091622

Aim of Course:


This course covers the analysis of data gathered in surveys.

Survival Analysis 091622

Aim of Course:

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

Statistics 3: Anova and Regression 091622

Aim of Course:


To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

Applied Predictive Analytics 090922

Aim of Course:

This course covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.

Integer/Nonlinear Programming and Network Flow 090922

Aim of Course:

This course will teach you a number of advanced topics in optimization: how to formulate and solve network flow problems; how to model and solve optimization problems; how to deal with multiple objectives in optimization problems, and techniques for handling optimization problems.



Introduction to Network Analysis 090922

Aim of Course:

This course covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.

Predictive Analytics 3 - Dimension Reduction, Clustering, and Association Rules: R 090922

Aim of Course:

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.

All courses