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Outlier Detection Algorithms in Data Mining and Data Science (Udemy.com)

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

3.9
218 reviews

Created by: KDD Expert

Last updated January 2019

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What you will 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

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating3.9 out of 5among the lower-rated ones
  • Reviews218#335 of 462 by number of reviews
  • Length2.3 hoursshorter than 87% of them
  • Last updatedJanuary 2019earlier than 95% of them

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Quality Score

Content Quality
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Video Quality
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Qualified Instructor
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Overall Score : 78 / 100

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Course 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 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.
Read More All Levels All Levels product length iconTime: 2.3h

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Instructor Details

KDD Expert

I am Data Scientist. I have more than 10 years experience  working with analyzing of data, Data Mining and Machine Learning.

Data Analysis it's more than my job, it's my hobby. I love it and enjoy it.

I am programmer in Python, SAS, R and Matlab. 

I  am interested and developing models for retail market, such as product assortment decisions, predicting customer loyalty, demand forecasting and et cetera.

In addition, i developed and implemented a variety of models in the area of risk management and finance.



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Reviews

3.9

218 ratings on Udemy

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By John Madrid on 11/28/2023

This is not a person speaking and it sounds horrible. Too hard to keep focus when it sound isn’t proficient and sounds robotic

By Hitesh Mehta on 3/11/2020

Content and explanation is good, but it is not exactly as overview suggested. Overview talks about "Statistics , R + Python + SAS programming languages" multiple times, however in actual content he covers only excel for most part. Still, good for basics.

By Arshad Shaik on 1/13/2020

It is a good course for beginners and to get started in outlier detection. However, there are many algorithms that were not covered in this course. Also, this course looked more like a basic statistics course rather than a course centered on machine learning/Data Science surrounding outlier detection. Author should have taken some real world datasets to demonstrate the algorithms workings.

By Nicolas Galan on 7/27/2019

Very nicely explained and helps to develop better understanding and intuition about detecting outliers. I will not hesitate to purchase other lessons from the same instructor.

By Athanasios Anagnostis on 7/16/2018

It is a good subject and covers the most basic parts, but the instructor, although tries a lot, is not able to engage, maybe also due to the low quality notes (they also include mistakes), the accent, and the urge to move forward with his sentences. There is room for improvement, and i believe he can do much better because it seems that he knows his subject.

By Lesley Lane on 6/20/2018

Excellent course. this is a must do. The instructor was very thorough in his presentation. I couldn't imagine that there was so much to outliers before this course. I'm very pleased to have attended and completed it. This will definitely help me later.

By Rusdianto Mahmud on 6/7/2018

Glad found this course to understand the theory behind the outliers detection. The course is short and to the point, plus KDD provides python code as basis for larger exercise. Thanks

By Dawn Pearson on 5/3/2018

I learned a lot about the mathematical models associated with data mining and I view this course as a resource. Since I am a beginner, I still need additional education and I don't know what to learn next. Any suggestions (predictive analytics)?

By Konrad Sztukowski on 1/13/2018

Good for knowing the methods of outlier detection but lacking practical examples of using packages in R or Python on sample data sets and data streams

By Ronald Vides on 11/18/2017

Great course with clear explanations and techniques in an intuitive and practical manner. Sincerely appreciate the efforts of the instructor.

By Tony Lee

good

By Sita Kimic

Awesome material and clarity. Perfect pace.

Showing 12 of 39 reviews

Outlier Detection Algorithms in Data Mining and Data Science
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  • Video Duration: icon 2.3h
  • Price: $10.99
  • Provider: icon Udemy
  • Updated: icon January 2019
  • Level: All Levels All Levels
  • Language: icon English
  • Certificate: icon Yes
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