Basics of Numpy For Data Analysis and Data Science Free Course Free Programming Course Udemy: Learn robust data analysis with R and Matlab, the key in Machine Learning, Data Science, and Statistics. This course is intended to study the characteristics of the problem, and 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 or in case we have more than one. We will see the theoretical properties of the methods and we will apply them to examples.
Basics of Numpy For Data Analysis and Data Science Free Course
In addition, we are going to see the practical performance of the software R and Matlab, and we will learn the different existing packages in both software for the problem of outlier detection. The implementation and example codes are available in the open Google Drive repository. Free Programming Course Udemy.
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 to data. Researchers, students, data analysts, 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. Basics of Numpy For Data Analysis and Data Science Free Course
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Contents:
- 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.
Requirements:
- Basic statistical knowledge.
