Data Mining - Clustering/Segmentation Using R, Tableau

BY
Udemy

Learn and comprehend data mining - clustering and segmentation using R and Tableau.

Mode

Online

Fees

₹ 649 2299

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Its fast data analysis makes tableau popular. Dashboards and worksheets visualize data. Tableau lets one create dashboards with actionable insights to advance the business. When configured properly, Tableau products run in virtualized environments. Data scientists use tableau for unlimited visual analytics. maps, graphs, charts, and other visuals are used to visualize data. Data visualization makes dataset trends, insights, patterns, and connections easy to understand. Many enterprises and businesses use Tableau to better understand their data and provide the best customer experience. Data Mining - Clustering/Segmentation Using R, Tableau certification is made available by Udemy to candidates who want to learn about the usage of R for building various models

Data Mining - Clustering/Segmentation Using R, Tableau online training Include seven hours of video,  one article, six downloadable resources, and a digital certificate upon course completion.

Data Mining - Clustering/Segmentation Using R, Tableau online classes consists of clustering using R, understanding tableau, analysis of Variance, clustering using tableau, and cluster analysis.

The highlights

  • Full Lifetime Access
  • 7 hours on-demand video
  • One Article
  • Six Downloadable Resources
  • Access on Mobile and TV
  • Certificate of Completion

Program offerings

  • Online course
  • Learning resources
  • 30-day money-back guarantee
  • Unlimited access

Course and certificate fees

Fees information
₹ 649  ₹2,299
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Tableau knowledge Knowledge of data mining

Data Mining - Clustering/Segmentation Using R, Tableau certification course, the applicant will learn everything about various types of data mining techniques, K-Means clustering algorithm & how to use R to accomplish. The candidate will learn about types of data mining techniques, hierarchical clustering, tableau, tableau analytics, analysis of variance, statistics of clustering using tableau, and cluster analysis.

The syllabus

Clustering using R

  • Introduction to Clustering Using R and Tableau
  • Introduction To Clustering
  • Types Of Data Mining Techniques
  • Hierarchical Clustering Introduction
  • Hierarchical Clistering Case Study
  • Distance For Categorical Data
  • Distance Between Clusters With Case Study
  • Distance For Mixed Data Case Study Part 1
  • Distance For Mixed Data Case Study Part 2
  • Hierarchical Clustering Synopsis
  • Hierarchical Clustering Using R Part 1
  • Hierarchical Clustering Using R Part 2
  • K Means Clustering Introduction
  • K Means Clustering Using R - Part 1
  • K Means Clustering Using R - Part 2
  • K Means Clustering Using R - Part 3
  • kselection, CLARA and PAM Clustering Using R - Part 4
  • Difference Between K Means And Hierarchical
  • Summary Of K Means Clustering
  • K Means Clustering Case Study
  • Recap Data Mining Clustering
  • Quiz-1

Understanding Tableau

  • Introduction Why Tableau..?
  • Tableau Analytics - Ice breaker
  • Tableau Architecture Part 1
  • Tableau Architecture Part 2
  • Tableau Architecture Part 3
  • An Introduction to Tableau Desktop and History of Tableau
  • Tableau Start Page
  • Location Based Analytics using Tableau
  • Tableau User Interface
  • Basic Tableau Visualization
  • Quiz-2

Prerequisite to Understand Clustering

  • Analysis of Variance(ANOVA) - Part 1
  • Analysis of Variance(ANOVA) - Part 2
  • Statistics of ANOVA
  • Quiz-3

Clustering using Tableau

  • CLUSTERING USING TABLEAU
  • Statistics of Clustering
  • Statistics of Clustering using Tableau
  • Whats Next.....?

Quiz - Cluster Analysis knowledge check

  • Cluster Analysis knowledge check

Clustering on Mixed Data

  • Mixed data-Dummy variable creation for Categorical variables
  • Mixed data-Normalizing entire data to a similar scale
  • Mixed data - Distance Matrix, Hierarchy and Dendrogram
  • Mixed data-Hierarchical clustering and interpretation
  • Mixed data-Scree plot / Elbow curve part1
  • Mixed data-Scree plot / Elbow curve part2
  • Mixed data-K Means clustering and Insights

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