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Tableau Advanced Analytics with R Course Overview

Moving from information perception into more profound, further developed investigation? This course will strengthen information aptitudes for information viz-astute clients who need to move into investigation and information science keeping in mind the end goal to upgrade their organizations by outfitting the systematic energy of R and the dazzling perception capacities of Tableau.

Together, Tableau and R offer open examination by enabling a blend of simple to-utilize information perception alongside industry-standard, powerful measurable calculation. Perusers will run over an extensive variety of machine learning calculations and figure out how distinct, prescriptive, prescient, and outwardly engaging systematic arrangements can be composed with R and Tableau.

Keeping in mind the end goal to boost learning, hands-on illustrations will facilitate the change from being an information smart client to an information examiner utilizing sound factual devices to perform progressed examination.


This class is for Tableau clients who are OK with the item and are prepared to change to from being an information keen client to being an information investigator utilizing sound factual devices to perform progressed examination.

Tableau Advanced Analytics with R Course Outline

Part 1 - Advanced Analytic with R and Tableau

Introducing R and R Studio

Introducing Rserve

Condition of R

Associating with Rserve

Part 2 – The Power of R


Vectors and Lists



Information Frames

Control Structures

For Loops


Utilizing R in Tableau

Part 3 – Methodology for Advanced Analytics

Fresh DM Model – Data Preparation

Fresh DM – Modeling Phase

Fresh DM – Evaluation

Fresh DM – Deployment

Fresh DM – Process Restarted

Fresh DM – Summary

Working with Dirty Data

Prologue to Dplyr

Outlining Data with Dplyr

Section 4 – Prediction with R and Tableau Using Regression

Straightforward Linear Regression

Contrasting Actual Values and Predicted Results

Building a Multiple Regression Model

Explaining the Business Question

Offering Data Analysis to Tableau

Part 5 – Classifying Data With Tableau

Understanding the Data

Information Preparation

Depicting the Data

Displaying in R

Choice Trees in Tableau Using R

Bayesian Methods


Part 6 – Advanced Analytics Using Clustering

What is Clustering?

Discovering Clusters in Data

How Does K-Means Work?

Making a Tableau Group from Cluster Results


Bunching Without K-Means

Insights For Clustering

Part 7 – Advanced Analytics With Unsupervised Learning

What Are Neural Networks?

Backpropagation and Feedforward Neural Networks

Assessing a Neural Network Model

Lift Scores

Imagining Neural Network Results

Displaying and Evaluating Data in Tableau

Part 8 – Interpreting Your Results For Your Audience

Prologue to Decision System and Machine Learning

Fluffy Logic

Bayesian Theory

Coordinating a Decision System and IoT (Internet of Things) Project

Building Your Own Decision System-Based loT

Composing the Program




Before going to this course, understudies ought to have taken or be comfortable with the substance displayed in Tableau Desktop Level 1: Introduction and the Tableau Desktop 2: Intermediate courses.

What You Will Learn

Coordinate Tableaus investigation with the business standard, measurable ability of R.

  • Influence R to work brings in Tableau, envisioning R capacities with Tableau utilizing RServe.
  • Utilize the CRISP-DM strategy to make a guide for examination examinations.
  • Actualize different managed and unsupervised learning calculations in R that arrival esteems to Tableau.
  • Get to grasps with cutting edge counts in R and Tableau for investigation and expectation with the assistance of utilization cases and hands-on cases.
  • Make speedy, relevant, and information driven choices for your business utilizing progressed expository methods, for example, estimating, forecasts, affiliation rules, grouping, arrangement, and other propelled Tableau R figured field capacities.