Switch to the store?

Learning Data Mining with R [Video]

More Information
  • Get to know the basic concepts of R: the data frame and data manipulation
  • Discover the powerful tools at hand for data preparation and data cleansing
  • Visually find patterns in data
  • Work with complex data sets and understand how to process data sets
  • Get to know how object-oriented programming is done in R
  • Explore graphs and the statistical measure in graphs
  • Gain insights into the different association types
  • Decide what algorithms actually should be used and what the desired and possible outcomes of the analysis should be
  • Grasp the discipline of classification, the mathematical foundation that will help you understand the bayes theorem and the naïve bayes classifier
  • Delve into various algorithms for classification such as KNN and see how they are applied in R
  • Evaluate k-Means, Connectivity, Distribution, and Density based clustering

Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks.

Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You'll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.

After completing this course, you will be able to solve real-world data mining problems.

Style and Approach

Through the course, you will come to understand the different disciplines of data mining using hands-on examples where you actually solve real-world problems in R. For every category of algorithm, an example is explained in detail including test data and R code.

  • Use powerful R libraries to effectively get the most out of your data
  • Gain a good level of knowledge and an understanding of the data mining disciplines to solve real-world challenges in R
  • This hands-on tutorial covers topics in three dimensions: the mathematical foundations, the actual implementation in R, and practical examples
Course Length 2 hours 17 minute
Date Of Publication 30 Aug 2016


Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.