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 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 key concepts for writing advanced R code, emphasizing the design of functional and efficient code. After completing the course, students should be able to read, understand, modify, and create complex functions to perform a variety of tasks.
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 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.