Data Mining with Rattle
Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R
What you will learn
Perform and support life-cycle data mining tasks and activities using the popular Data Miner ("Rattle") software suite.
Understand the functionalities implicit in the data, explore, test, transform, cluster, associate, model, evaluate, and log tabs in the Data Miner ("Rattle") GUI software platform.
Know how to explore, visualize, transform, and summarize data sets in Rattle.
Know how to create advanced, interactive Ggobi visualizations of data.
Know how to use, estimate and interpret: cluster analyses; association analyses mining rules; decision trees; random forests; boosting; and support vector machines using Rattle.
Why take this course?
Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the 'Rattle' package in R software. Rattle is a popular GUI-based software tool which 'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a 'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional 'in-demand' analytical job skills to offer a prospective employer. The course is also suitable for graduate students seeking to learn a variety of techniques useful to analyze research data. Finally, the course is useful for practicing quantitative analysis professionals who seek to acquire and master a wider set of useful job skills and knowledge. The course topics are scheduled in 10 distinct topics, each of which should be the focus of study for a course participant in a separate week per section topic.