Outlier Detection Algorithms in Data Mining and Data Science
3.9 (161 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,996 students enrolled

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
3.9 (161 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,996 students enrolled
Created by KDD Expert
Last updated 1/2019
English
English
Current price: $44.99 Original price: $74.99 Discount: 40% off
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This course includes
  • 2.5 hours on-demand video
  • 10 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • This course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex outlier algorithms
  • You can hone your programming skills because all algorithms you’ll learn have implementation in PYTHON, R and SAS
Course content
Expand all 13 lectures 02:16:01
+ Introduction
2 lectures 20:48
Introduction to Outlier Detection
3 questions
Mean, Median and Variance
3 questions
+ Detection Outliers in Univariate space
5 lectures 46:45
Three Sigma Rule
2 questions
Masking and Swamping effects
04:46
Masking and Swamping effects
1 question
MAD Rule
06:39
MAD Rule
3 questions
Boxplot Rule
08:07
Boxplot Rule
3 questions
Adjusted Boxplot Rule
13:17
Adjusted Boxplot Rule
1 question
+ Detection Outliers in Multivariate space
4 lectures 55:16
Introduction to Linear Algebra, Part1
07:34
Introduction to Linear Algebra, Part1
7 questions
Introduction to Linear Algebra, Part2
11:13
Introduction to Linear Algebra, Part2
2 questions
Mahalanobis Rule
20:24
Mahalanobis Rule
1 question
LOF - Local Outlier Factor
16:05
LOF
2 questions
+ Detection Outliers in High-Dimensional space
1 lecture 12:05
ABOD - Angle-Based Outlier Detection
12:05
ABOD
1 question
+ Final
1 lecture 01:07
Final Lecture
01:07
Requirements
  • Students who have a basic knowledge of statistics and linear algebra(priority but not required)
  • Willingness to learn
Description

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 LearningData 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.


Who this course is for:
  • Data Scientist or Analyst
  • You are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities and et cetera