Data Mining with R: Go from Beginner to Advanced!
What you'll learn
- 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 real data and R scripts to new and unique data analysis and data mining problems.
- 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).
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 real 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.
Who this course is for:
- 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.
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.