Data Mining with R: Go from Beginner to Advanced!
Data Mining with R: Go from Beginner to Advanced!. Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques.
The name of this course is Data Mining with R: Go from Beginner to Advanced!. The knowledge you will get with this indescribable online course is astonishing. Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques..
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 R: Go from Beginner to Advanced!
Course Description This is a “hands-on” business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using verdadero data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on “best practices” for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer’s hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can “take them home” and apply them to their own unique data analysis and mining cases. There are also “hands-on” exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities.
Requirements of this course: Data Mining with R: Go from Beginner to Advanced!
What are the requirements? Download and install no-cost R software (complete, easy-to-follow instructions are provided). Download and install no-cost RStudio IDE software (complete, easy-to-follow instructions are provided).
What will you learn in this course: Data Mining with R: Go from Beginner to Advanced!?
What am I going to get from this course? Use R software for data import and export, data exploration and visualization, and for data analysis tasks, including performing a comprehensive set of data mining operations. Effectively use a number of popular, contemporary data mining methods and techniques in demand by industry including: (1) Decision, classification and regression trees (CART); (2) Random forests; (3) Linear and logistic regression; and (4) Various cluster analysis techniques. Apply the dozens of included “hands-on” cases and examples using verdadero data and R scripts to new and unique data analysis and data mining problems.
Target audience of this course: Data Mining with R: Go from Beginner to Advanced!
Who is the target audience? Anyone who wants to learn more about performing data analysis using a variety of popular, contemporary data mining techniques. Data Mining beginners and professionals who wish to enhance their data mining knowledge and skill levels Individuals seeking to gain more proficiency using the popular R and RStudio software suites. Undergraduate students seeking to acquire in-demand analytics skills to enhance employment opportunities. Graduate students seeking to acquire a wider repertoire of analytics skills for research data analysis tasks.