### Bayesian Statistics in R

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.

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### Bayesian Statistics in R

More Details » ### Customer Analytics in R

More Details » ### Mapping in R

More Details » ### Modeling in R

More Details » ### Predictive Analytics 1 – Machine Learning Tools with R

More Details » ### Predictive Analytics 2 – Neural Nets and Regression with R

More Details » ### Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R

More Details » ### Predictive Analytics for Healthcare

This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.

More Details » ### R for Statistical Analysis

More Details » ### R Programming – Intermediate

More Details » ### R Programming – Introduction Part 1

More Details » ### R Programming – Introduction Part 2

More Details » ### Spatial Statistics for GIS Using R

More Details » ### Structural Equation Modeling (SEM) Using R

More Details » ### Visualization in R with ggplot2

More Details »

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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 spatial data may be written/read and visualized in R, and show how publication quality maps may be produced in R, based on the GISTools package, as well as providing a review of a number of other diverse methods for visually representing geographical information in R.

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

This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of 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 R, to visualize and explore predictive modeling.

This course, with a focus on R, 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 how to use R for basic statistical procedures.

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 provides an easy introduction to programming in R.

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.

Structural Equation Modeling (SEM) allows you to go beyond simple single-outcome models, and deal with multiple outcomes and multi-directional causation. You will learn how to

This course will teach you ggplot as an implementation of the grammar of graphics in R. ggplot combines the advantages of base and lattice graphics while maintaining the ability to build up a plot step by step from multiple data sources.

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