Robust data analysis and outlier detection are crucial in Statistics, Data Analysis, Data Mining, Machine Learning, Artificial Intelligence, Pattern Recognition, Classification, Principal Components, Regression, Big Data, and any field related to the data. With the course you will obtain the FREEBOOKABOUTOUTLIERSwith specific tips and tricks, and the summary of all the robust methods to detect them that will help you obtain accurate results and awsome data analysis. Researchers, students, data analysts, and mostly anyone dealing with real data, should be aware of the problem with outliers (and outliers) and should know how to deal with this problem and what robust methods should be used. . The vast majority of Machine Learning algorithms are capable of detecting characteristics common to the majority of data, but many times they are confused or even ignore those atypical data, which should not be ignored in conditions where the security of people, such as the analysis of medical data, the world of the Internet of Things IoT, or risks and security in companies. What would happen if a virus spread throughout the world because we ignored anomalous data? We would have a pandemic, like that of COVID19, which if the outlier signals detected by neural networks had not been ignored, we could have acted upon beforehand. What would happen if we ignored any signal from a Smart City system? We could miss a gas leak. What would happen if by ignoring an alarm, we miss a meteorite coming towards the earth? We would have to call Bruce Willis, to save us from Armageddon. With this course you will be an expert in robust data analysis, in the detection and treatment of atypical data, both learning the theoretical concepts, and having at your disposal the algorithms implemented in a practical way with two different languages so that you can choose the one that best suits you: R-Studio and Matlab. You will also have access to a community for questions, where all the students are and you can ask what you want about the analysis of outliers. The example implementation codes are available to you in the open Github repository for you to download and use. In addition, we have two sections of basic concepts that will help you to remember some notions necessary to understand atypical detection methods. With this course you will be able to understand and know how to deal with one of the most important topics of today both academically, in the industry and in data analysis or machine learning. The examples will help you to visualize the importance of the analysis of outliers as well as a guide to carry out these analyzes yourself.
