Cluster Analysis- Theory & workout using SAS and R
4.3 (49 ratings)
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Cluster Analysis- Theory & workout using SAS and R

nalytics / Machine Learning : Hierarchical & non hierarchical clustering (k-means), theory & SAS / R program
4.3 (49 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
382 students enrolled
Last updated 2/2017
English
Price: $40
30-Day Money-Back Guarantee
Includes:
  • 5 hours on-demand video
  • 14 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
Learn cluster analysis in crystal clear and simple way
Learn hierarchical and non-hierarchical clustering
Know theory, business apllication, sas program and interpretation of output
R syntax for clustering
View Curriculum
Requirements
  • Basic understanding of statistics
  • Basic knowledge of SAS
  • Basic knowledge of R
Description
  1. About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible.
  2. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes.
  3. Course duration- The course should take roughly 10 hours to understand and internalize the concepts.
  4. Course Structure (contents) The structure of the course is as follows.

Part 01 - cluster analysis theory and workout using SAS

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Motivation

  • Where one applies cluster analysis. Why one should learn cluster analysis?
  • How it is different from objective segmentation (CHAID / CART )

Statistical foundation and practical application: Understand

  • Different type of cluster analysis
  • Cluster Analysis – high level view
  • Hierarchical clustering –
    • Agglomerative or Divisive technique
    • Dendogram – What it is? What does it show?
    • Scree plot - How to decide about number of clusters
    • How to use SAS command to run hierarchical clustering
    • When and why does on need to standardize the data?
    • How to understand and interpret the output
  • Non-hierarchical clustering (K means clustering).
    • Why do we need k means approach
    • How does it work?
    • How does it iterate?
    • How does it decide about combining old clusters?
    • How to use SAS command to run hierarchical clustering
    • When and why does on need to standardize the data?
    • How to understand and interpret the output

Part 02

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Learn R syntax for hierarchical and non hierarchical clustering

Part 03

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Cluster analysis in data mining scenario

Part 04

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Assignment on cluster analysis

Who is the target audience?
  • statistics and analytics professionals / students
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Curriculum For This Course
Expand All 59 Lectures Collapse All 59 Lectures 06:11:04
+
Overall structure of the course
1 Lecture 02:39
+
Part 01 - Cluster Analysis using SAS
3 Lectures 13:32


Difference between Cluster Analysis & Decision tree ( Objective segmentation)
02:45
+
Motivation, Industry Applications & clustering as strategy. Industry Case study
4 Lectures 14:57
Motivation to learn Clustering
01:54

Popular Industry Applications of Clustering
04:24

Clustering as strategy and Industry Case Study
08:39

Check Basic Understanding of cluster analysis
3 questions

PDF for above lectures
20 pages
+
Hierarchical Clustering
12 Lectures 01:00:12
Section outline - what will be explained in this section?
02:54

Hierarchical Clustering High Level
02:36

Hierarchical Clustering Steps and Associated terms
09:50

How to get free access to SAS?
10 pages

Hierarchical Clustering Using SAS and Interpretation of The Output
08:01

Hierarchical Clustering Using Excel and explanation of SAS Output
17:31

Download files used?
02:39

Scree Plot - to decide optimal number of clusters
04:03

Why to standardize variables
03:08

Dendogram- The hierarchical structure
05:47

When to go for Non Hierarchical clustering
03:43

Check Basic Understanding of Hierarchical clustering
4 questions

Section - pdf
22 pages
+
Non Hierarchical clustering - K means clustering
9 Lectures 58:23
Section outline - what will be explained in this section
02:10

K means clustering alogorithm
06:38


Hierarchical vs Non Hierarchical clustering
02:07

K means clustering for Data Mining
01:13

K means clustering using SAS
05:57

Please download the supplementary excl and see it along with video by pausing video time to time

SAS output Explanation pass 01
14:57

SAS output Explanation pass 02
19:00

Section PDF
9 pages
+
Variants of Hierarchical clustering, Different distance and linkage functions
6 Lectures 21:27
Section Outline
01:37

Agglomerative and Divisive Hierarchical Clustering
02:10


Different Linkage function
13:32

Check Basic Understanding of Non Hierarchical clustering and various options
9 questions

Section PDF
20 pages

How to download Excel files?
2 pages
+
Part 02- cluster Analysis using R
5 Lectures 28:14

Details of Hierarchical clustering Function in R
03:10

Demo of Hierarchical clustering using R
09:01


Demo of Non Hierarchical clustering using R
10:52
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Part 03 - Cluster Analysis in data mining scenario (industrial set up)
9 Lectures 41:10

Dealing with Nominal Categorical Variable
03:54

Dealing with Ordinal Categorical Variable
02:30

Dealing with Missing Value of a Numeric Variable
03:22


Standardize Numeric Variable
03:23

Select Numeric Variables by Variable Clustering
09:18

Iterate for final clusters
06:18

Business Presentation of cluster solution
03:26
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Demo of clustering approach for data mining scenario using R
8 Lectures 39:10
Data Detail n Data Sanity check
03:26

Prepare Data for clustering
12:33


decide final number of clusters
02:34

Iterate for final cluster
04:24


Business Presentation of cluster solution
03:07

Concluding Tips
04:03
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Part 04 - Practice Assignment and model solution
2 Lectures 08:20
Practice
01:20

Model solution
07:00
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
795 Reviews
12,289 Students
11 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 15+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.