Robust data analysis in R and Matlab [Free Online Course] - TechCracked

Robust data analysis in R and Matlab

Learn robust data analysis with R and Matlab, the key in Machine Learning, Data Science and Statistics.

This course includes
  • 9 hours on-demand video
  • 13 articles
  • 14 downloadable resources
  • Full lifetime access
  • Access on mobile and TV

What you'll learn
  • Concepts related to Robust Statistics.
  • Performance of outlier detection methods.
  • Learn to differentiate one method from another.
  • Identify the most robust and efficient methods that you should use in practice.
  • Application of the methods with handmade examples.
  • Application of the methods with R and Matlab.


Robust data analysis and outlier detection are crucial in Statistics, Data Analysis, Data Mining, Machine Learning, Pattern Recognition, Artificial Intelligence, Classification, Principal Components, Regression, Big Data , and any field related with data. Researchers, students, data analyst, and mostly anyone who is dealing with real data have to be aware of the problem with outliers and they have to know how to deal with this issue.

This course is intended to study the characteristics of the problem, its consequences and learn how to recognize it through the existing approaches. We will deeply study the performance and the properties of the methods to detect outliers in case we have a single random variable ( univariate data) or in case we have more than one ( multivariate data). We will see the theoretical properties of the methods and we will apply them to examples . In addition, we are going to see the practical performance with the software R and Matlab , and we will learn the different existing packagesin both software for the problem of outlier detection. The implementation and example codes are available in the open Google Drive repository.

You will learn about both classical and recent algorithms for outliers detection

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