Learn Data Mining - Clustering Segmentation Using R,Tableau
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Learn Data Mining - Clustering Segmentation Using R,Tableau

Learn Data Mining - Clustering Segmentation Using R,Tableau
4.8 (41 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.
1,128 students enrolled
Created by ExcelR Solutions
Last updated 2/2017
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Current price: $10 Original price: $50 Discount: 80% off
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  • 6 hours on-demand video
  • 5 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand the various types of Data Mining Techniques
  • Learn about the K-Means clustering algorithm & how to use R to accomplish this
View Curriculum
  • Knowledge of High school level Mathematics and Statistics
  • Rudimentary knowledge of any programming knowledge though not essential will be an added plus.
  • Download R & RStudio before starting this tutorial
  • Download all the datasets provided as part of this program to be able to practice & replicate the visualisations
  • Download Tableau Desktop Professional software from Tableau website

Learn Data Mining - Clustering Segmentation Using R,Tableau is designed to cover majority of the capabilities of R from Analytics & Data Science perspective, which includes the following:

  • Learn about the usage of R for building Various models
  • Learn about the K-Means clustering algorithm & how to use R to accomplish the same
  • Learn about the science behind Clustering & accomplish the same using R and Tableau

Course is structured to start with introduction to the tool & the principles behind Clustering Using R tool. From there R is explained thoroughly including analytical concepts behind applicable Data Mining Techniques. Finally course ends with explanation of clustering using Tableau and statistics behind clustering along with interview questions for job seekers

Who is the target audience?
  • All working professionals whose organization uses/creates data
  • Personnel who want to make their foray into Data Science
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Curriculum For This Course
37 Lectures
Clustering using R
21 Lectures 03:31:55

Introduction of the trainer & the agenda of the various concepts that you will learn as part of Data Science Learn  Data Mining-Clustering using R and Tableau also Tableau will be discussed.

Preview 04:17

Why do we need to do clustering and how does it help in making a business decision.What is the primary objective of Clustering; learn about it with an example.

Preview 12:23

Data Mining in a Nutshell!What are the two main branches of Data mining and their distributaries? The Two approaches to clustering, and introduction to principle of Hierarchical clustering.

Types Of Data Mining Techniques

Visualisation of Hierarchical clustering, Grouping of records & division of cluster based on distance measure. Rules for the measure of distance

Hierachical Clustering Introduction

Hierarchical clustering using a case study, Computation of distance amongst two records having multiple inputs using the eucleadian method. Understanding the need for standardization of data; Z-score, Other types of distance measures

Hierarchical Clistering Case Study

Learn How to measure of distance between categorical data. Various measurements using binary matrix for twin category data. Distance measurement rule for for more than 2 categories.

Distance For Categorical Data

Learn about Various distance measure criteria for between clusters. “Hands On” exercise on hierarchical clustering and summarizing using Dendrogram

Distance Between Clusters With Case Study

Learn about Measurement of distance between records having both numerical and categorical variables. Method of creation of Dummy variable data from categorical data. The need and the method of standardization the numerical data to the same scale as categorical dummy variable data.

Distance For Mixed Data Case Study Part 1

Learn about Measure of distance using Gower’s General Similarity coefficient for mixed data using weighted means.

Distance For Mixed Data Case Study Part 2

Learn How Clustering helps - the consumer perspective and the supplier perspective; Other insights from clusters and their labeling

Hierarchical Clustering Synopsis

Learn about Reading an XLSX file into R; Understanding the Database (MBA); Scaling the data to make it unitless using Z Scores

Hierarchical Clustering Using R Part 1

Learn about The ‘hclust’ function with complete linkage; Visualization of the clustering as as dendrogram using the ‘plot’ function; splitting the dendrogram into ‘k’ clusters using the ‘cuttree’ function; assigning the records to the respective clusters

Hierarchical Clustering Using R Part 2

Learn about The main difference between Hierarchical and non-hierarchical clustering; similarity within, dissimilarity amongst clusters; Algorithm (or iterative steps) for K means clustering

K Means Clustering Introduction

Learn about The main difference between Hierarchical and non-hierarchical clustering; similarity within, dissimilarity amongst clusters; Algorithm (or iterative steps) for K means clustering

K Means Clustering Using R - Part 1

Learn R code for K means clustering using only two inputs; for ease in visualization; computing the ideal number of clusters; viewing the iterative process of clustering (giving and receiving clusters) as an animation

K Means Clustering Using R - Part 2

Learn about random generation of centroid at the first iteration; The explanation of ‘receiving and giving clusters’ using the euclidean distance; understanding the math of centroid and the clustering based on distance; understanding the various attributes of the k-means output.

K Means Clustering Using R - Part 3

Learn The best value for ‘k’ using scree plot; determining the value of ‘k’ based on information gain; the point of the elbow; the ‘aggregate’ function for viewing each cluster as one record and analysis of the same; Labeling of clusters

K Means Clustering Using R - Part 4

Learn about The Pros and Cons of K means clustering and Hierarchical Clustering

Difference Between K Means And Hierarchical

Learn about Selection of ‘k’ based on the simplicity or adequacy; risks with the ‘k’ value - Local Minima problem; Cross checking of clusters for consistency;

Summary Of K Means Clustering

Learn about accelerometer data set for analysing the steps taken by different user as tracked by their smart phones; Importance of Domain knowledge

K Means Clustering Case Study

Learn about Data Mining unsupervised; Hierarchical Clustering; Non Hierarchical Clustering; Distance measure for continuous and discrete; Types of Linkage; Dendrogram;  Sum of Sqares distances between & within Clusters

Recap Data Mining Clustering

21 questions
Understanding Tableau
10 Lectures 01:25:33

Learn about the concepts that would be discussed as part of this section. Learn on why Tableau is extremely valid to be in the current world alongside learning Tableau's dominance in the space of Business Intelligence & Analytics. You will notice that Tableau exists in the leaders quadrant of Gartner's magic quadrant since 4 years in a stretch.

Introduction Why Tableau..?

Learn about Tableau and its importance in the space of Analytics. Learn on why data visualization is extremely imperative in the world of big data. Get a glimpse into the amount of data getting generated via various sources. Finally you will learn the importance of data visualization in identifying interesting insights from the data.

Tableau Analytics - Ice breaker

Learn about the various products which Tableau provides us to cater to our business needs. This includes Tableau Desktop, Server, Online, Mobile, Public, Reader. Learn about the Tableau architecture and various components including various data source formats, data connectors and APIs.

Tableau Architecture Part 1

Learn about the various components of Tableau server including Data Server, VizQL Server, Application Server. Learn about the load balancing and failover support with Tableau. Also learn on what are the various browsers, mobile devices on which end users can view the visualization using Tableau

Tableau Architecture Part 2

Learn on how to extract the data from various datasources using Tableau Desktop or Tableau Server. Also discussed about is the way to establish live connection with data sources using Tableau Desktop & Server. Learn on how we can extract inly relevant fields of the data source by writing queries. One can also automate the reports & establish security settings in Tableau.

Tableau Architecture Part 3

Learn about the operating systems on which Tableau Desktop can be installed. Pros and Cons of using it on the two operating systems. Also learn about VSA of Tableau, the word coined by ExcelR Solutions. 

An Introduction to Tableau Desktop and History of Tableau

Learn about Tableau start page & how to connect to wide variety of various datasources. Also learn about thumbnails & discover pane, which will help you gain access to amazing real-world projects which are solved in current world. 

Tableau Start Page

Learn about an interesting case study solved using data visualisation using Tableau, which is called as Location Based Analytics. 

Location Based Analytics using Tableau

Learn about the worksheet interface of Tableau including dimensions & measures. Learn on how to covert dimensions to measures & vice-versa. Learn about the terminologies of various components of a worksheet & about show me panel. 

Tableau User Interface

Learn on how to accomplish a simple visualisation on Bar plot, function of Filters & Marks shelf. Also learn about the usage of double clicking on measures & dimensions. 

Basic Tableau Visualization

7 questions
Prerequisite to Understand Clustering
3 Lectures 36:55
Analysis of Variance(ANOVA) - Part 1

Analysis of Variance(ANOVA) - Part 2

Statistics of ANOVA

3 questions
Clustering using Tableau
3 Lectures 27:15

Statistics of Clustering

Statistics of Clustering using Tableau
About the Instructor
ExcelR Solutions
4.2 Average rating
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7,024 Students
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Pioneer in professional management trainings & consulting


Certified Six Sigma Master Black Belt

Project Management Professional (PMP)

Agile Certified Practitioner (PMI - ACP)

Risk Management Professional (PMI-RMP)

Certified Scrum Master

Agile Project Management – Foundation & Practitioner from APMG

Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15+ years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc.

He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes.

He has 8 years of extensive experience in corporate, open house and online training.

He is a thorough implementer with abilities in Business Analytics and Agile projects.

He worked in Delivery management focusing on maximizing business value articulation.

He has a comprehensive experience in leading teams and multiple projects.

Quality Management: A thorough implementer with abilities in Quality management focusing on maximizing customer satisfaction, process compliance and business value articulation; comprehensive experience in leading teams & multiple projects. A result-oriented leader with expertise in devising strategies aimed at enhancing overall organizational growth, sustained profitability of operations and improved business performance.

Project Management: Project Management Professional involved in Initiation, Planning, Execution, Monitoring & Controlling and Closing phases of project activities. Devising and implementing project plans within preset budgets and deadlines and managing the projects towards successful delivery of project deliverables and meeting project objectives.

Training: Close to 8 years training experience and conducted multiple trainings in PMP, Agile, Six Sigma, Business Analytics and Process Excellence across the globe. Understands the individual differences of the attendees and possesses excellent training skills and considered as one of the best trainers in his areas of expertise.