Outlier Detection Algorithms in Data Mining and Data Science

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.