This course will teach you how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data Using R and the associated R package JAGS.

In this course you will work through a customer analytics project from beginning to end, using R.

This course will teach you how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.

This course will introduce you to the basics of programming in Python on either Windows or Mac platform.

This course provides an easy introduction to programming in R.

This course will show you how to use R to create statistical models and use them to analyze data.

This course will teach you the use of mathematical models for managerial decision making and covers how to formulate linear programming models where multiple decisions need to be made while satisfying a number of conditions or constraints.

This online course introduces the basic paradigm of predictive modeling: classification and prediction.

This course introduces the basic paradigm for predictive modeling: classification and prediction.

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.

This course will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

This course will teach you the basic Python skills and data structures – how to load data from different sources and aggregate it, and how to analyze and visualize it to create high-quality products.

This course will teach experienced data analysts a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code. After completing the course, students should be able to manipulate data programmatically using R functions of their own design.

This course is a continuation of the introduction to R programming.

This course will teach you spatial statistical analysis methods to address problems in which spatial location. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.

This course will teach you how to extract data from a relational database using SQL and merge data into a single file in R so that you can perform statistical operations.