Udemy

Data Mining with R: Go from Beginner to Advanced!

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  • 4,722 Students
  • Updated 8/2020
4.4
(428 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
11 Hour(s) 54 Minute(s)
Language
English
Taught by
Geoffrey Hubona, Ph.D.
Rating
4.4
(428 Ratings)
2 views

Course Overview

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.

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.

Course Content

  • 9 section(s)
  • 80 lecture(s)
  • Section 1 Data Types and Structures in R
  • Section 2 Data and File Input and Output
  • Section 3 Visualizing (Getting to Know) your Data
  • Section 4 Decision Trees and Random Forests
  • Section 5 Linear Modeling (Regression) and Generalized Linear Modeling (GLMs)
  • Section 6 K-Means, K-Medoids, and Hierarchical Cluster Analysis Approaches
  • Section 7 Density-Based and Agglomerative Hierarchical Clustering
  • Section 8 More Cluster Analysis Examples, Graphics, and Detecting Outliers
  • Section 9 K-Means TAM Residuals Cluster Analysis Software Case example

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.


Reviews

  • H
    Huyet Yannick
    2.0

    Confus, j'ai eu bcp de mal à avancer et pouvoir comprendre ce que l'on faisait et comment être autonome sur les notions étudiées.

  • D
    Dominik Wojcicki
    4.0

    Course is good. It is taught by university professor, so you will have to do some additional reading and research. I take it as a plus as any knowledge which was not spoon-fed sticks for longer. However data mining part basically stops on Section 6 or 7. From than on it is just cluster analysis over and over.

  • M
    Monu Survashe
    5.0

    Good course Dr Hubonna helped a lot with all the difficulties during the course, Thanks

  • J
    Joan
    5.0

    Thanks yo Geoffrey, I have made great progress in learning this class. However, since I am a beginner in data mining and machine learning, there is a lot of knowledge that I have no idea theoretically. I hope more theory would be elaborated instead of assuming the learner is advanced in this knowledge.

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