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Data Mining with Rattle

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Data Mining with Rattle. Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R

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Descripción

The name of this course is Data Mining with Rattle. The knowledge you will get with this indescribable online course is astonishing. Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Geoffrey Hubona, Ph.D., one of the very best experts in this field.

Description of this course: Data Mining with Rattle

Course Description 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.

Requirements of this course: Data Mining with Rattle

What are the requirements? Students will need to install the R console and RStudio software (instructions are provided).

What will you learn in this course: Data Mining with Rattle?

What am I going to get from this course? 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.

Target audience of this course: Data Mining with Rattle

Who is the target audience? Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package. Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers.

Información adicional

Instructor

Geoffrey Hubona, Ph.D.

Lectures

82

Length

15

Skill Level

All Levels

Languages

English

Includes

Lifetime access <br/> 30 day money back guarantee! <br/> Available on iOS and Android <br/> Certificate of Completion

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