Outlier Detection Algorithms in Data Mining and Data Science

Outlier Detection in Data Mining, Data Science, Machine Learning, Data Analysis and Statistics using PYTHON,R and SAS

Welcome to the course " Outlier Detection Techniques ".

Are you Data Scientist or Analyst or maybe you are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities?

Welcome to Outlier Detection Techniques, a course designed to teach you not only how to recognise various techniques but also how to implement them correctly. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you’ll learn have implementation in PYTHON, R and SAS.

So what do you need to know before you get started? In short, not much! This course is perfect even for those with no knowledge of statistics and linear algebra.

Why wait? Start learning today! Because Everyone, who deals with the data, needs to know "Outlier Detection Techniques"!





The process of identifying outliers has many names in Data Mining and Machine learning such as outlier mining, outlier modeling, novelty detection or anomaly detection. Outlier detection algorithms are useful in areas such as: Data Mining, Machine Learning, Data Science, Pattern Recognition, Data Cleansing, Data Warehousing, Data Analysis, and Statistics.

I will present you on the one hand, very popular algorithms used in industry, but on the other hand, i will introduce you also new and advanced methods developed in recent years, coming from Data Mining.

You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn innovative algorithm for detection outliers in High-dimensional space.

I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. So, in my teaching method, I put a stronger emphasis on understanding the material, and less on programming. However, anyone who interested in programming, I developed all algorithms in R , Python and SAS, so you can download and run them.



List of Algorithms:

Univariate space:

1. Three Sigma Rule ( Statistics , R + Python + SAS programming languages)

2. MAD ( Statistics , R + Python + SAS programming languages )

3. Boxplot Rule ( Statistics , R + Python + SAS programming languages )

4. Adjusted Boxplot Rule ( Statistics , R + Python + SAS programming languages )

Low-dimensional Space :

5. Mahalanobis Rule ( Statistics , R + Python + SAS programming languages )

6. LOF - Local Outlier Factor ( Data Mining , R + Python + SAS programming languages)



High-dimensional Space:

7. ABOD - Angle-Based Outlier Detection ( Data Mining , R + Python + SAS programming languages)

I sincerely hope you will enjoy the course.