Cluster Analysis- Theory workout using SAS and R

Cluster Analysis- Theory & workout using SAS and R, available at $64.99, has an average rating of 4.5, with 64 lectures, 3 quizzes, based on 264 reviews, and has 1980 subscribers.

You will learn about 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 This course is ideal for individuals who are statistics and analytics professionals / students It is particularly useful for statistics and analytics professionals / students.

Enroll now: Cluster Analysis- Theory & workout using SAS and R

Summary

Title: Cluster Analysis- Theory & workout using SAS and R

Price: $64.99

Average Rating: 4.5

Number of Lectures: 64

Number of Quizzes: 3

Number of Published Lectures: 64

Number of Published Quizzes: 3

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

statistics and analytics professionals / students

Target Audiences

statistics and analytics professionals / students

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

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

Learn R syntax for hierarchical and non hierarchical clustering

Part 03

Cluster analysis in data mining scenario

Part 04

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Assignment on cluster analysis

Course Curriculum

Chapter 1: Overall structure of the course

Lecture 1: Course details – what is in four parts

Chapter 2: Part 01 – Cluster Analysis using SAS

Lecture 1: What is covered in part 01 – cluster analysis using SAS

Lecture 2: Intuitive Understanding of clusters

Lecture 3: Difference between Cluster Analysis & Decision tree ( Objective segmentation)

Chapter 3: Motivation, Industry Applications & clustering as strategy. Industry Case study

Lecture 1: Motivation to learn Clustering

Lecture 2: Popular Industry Applications of Clustering

Lecture 3: Clustering as strategy and Industry Case Study

Lecture 4: PDF for above lectures

Chapter 4: Hierarchical Clustering

Lecture 1: Section outline – what will be explained in this section?

Lecture 2: Hierarchical Clustering High Level

Lecture 3: Hierarchical Clustering Steps and Associated terms

Lecture 4: How to get free access to SAS?

Lecture 5: Hierarchical Clustering Using SAS and Interpretation of The Output

Lecture 6: Hierarchical Clustering Using Excel and explanation of SAS Output

Lecture 7: Download resources files (Excel).

Lecture 8: Scree Plot – to decide optimal number of clusters

Lecture 9: Why to standardize variables

Lecture 10: Dendrogram- The hierarchical structure

Lecture 11: When to go for Non Hierarchical clustering

Lecture 12: Section – pdf

Chapter 5: Non Hierarchical clustering – K means clustering

Lecture 1: Section outline – what will be explained in this section

Lecture 2: K means clustering alogorithm

Lecture 3: Graphical Explanation of K means clustering

Lecture 4: Hierarchical vs Non Hierarchical clustering

Lecture 5: K means clustering for Data Mining

Lecture 6: K means clustering using SAS

Lecture 7: SAS output Explanation pass 01

Lecture 8: SAS output Explanation pass 02

Lecture 9: Section PDF

Lecture 10: Section FAQ – Non Hierarchical Clustering

Chapter 6: Variants of Hierarchical clustering, Different distance and linkage functions

Lecture 1: Section Outline

Lecture 2: Agglomerative and Divisive Hierarchical Clustering

Lecture 3: Generic Distance formula

Lecture 4: Different Linkage function

Lecture 5: Section PDF

Lecture 6: How to download Excel files?

Chapter 7: Part 02- cluster Analysis using R

Lecture 1: Introduction to Cluster Analysis Using R

Lecture 2: Details of Hierarchical clustering Function in R

Lecture 3: Demo of Hierarchical clustering using R

Lecture 4: Scree plot for hierarchical clustering in R

Lecture 5: Details of Non Hierarchical clustering Function in R

Lecture 6: Demo of Non Hierarchical clustering using R

Chapter 8: Part 03 – Cluster Analysis in data mining scenario (industrial set up)

Lecture 1: Section Overview

Lecture 2: Dealing with Nominal Categorical Variable

Lecture 3: Dealing with Ordinal Categorical Variable

Lecture 4: Dealing with Missing Value of a Numeric Variable

Lecture 5: Outlier detection n Treatment

Lecture 6: Standardize Numeric Variable

Lecture 7: Select Numeric Variables by Variable Clustering

Lecture 8: Iterate for final clusters

Lecture 9: Business Presentation of cluster solution

Chapter 9: Demo of clustering approach for data mining scenario using R

Lecture 1: Data Detail n Data Sanity check

Lecture 2: Prepare Data for clustering

Lecture 3: Variable selection by Variable clutsering

Lecture 4: decide final number of clusters

Lecture 5: Iterate for final cluster

Lecture 6: Investigate the clusters

Lecture 7: Business Presentation of cluster solution

Lecture 8: Concluding Tips

Chapter 10: Part 04 – Practice Assignment and model solution

Lecture 1: Practice

Lecture 2: Model solution using R

Lecture 3: Model Solution using SAS

Lecture 4: FAQ (will keep growing overtime based on students queries)

Lecture 5: Bonus Topic – Analytics / Data Science / Machine Learning Interview questions

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 5 votes

2 stars: 18 votes

3 stars: 37 votes

4 stars: 98 votes

5 stars: 106 votes

Frequently Asked Questions

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