Udemy course Data Mining with Rattle by Geoffrey Hubona, Ph.D.
Data Mining with Rattle is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Data Mining with Rattle udemy course.
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Course data:
- Author: Geoffrey Hubona, Ph.D.
- Course rating: 3.9
- Category: Development
- Modality: Online
- Status: Available
- Idiom: English
Abouth Geoffrey Hubona, Ph.D.
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master’s and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.
What the udemy Data Mining with Rattle course teaches?
What you’ll 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.
Learn to use the GUI-based comprehensive Data Miner data mining software suite implemented as the rattle package in R
More information about the course Data Mining with Rattle
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.
