Tableau Advanced Analytics with R

Overview

Moving from data visualization into deeper, more advanced analytics? This course will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau.

Together, Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau.

In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.

Audience

This class is for Tableau users who are comfortable with the product and are ready to transition to from being a data-savvy user to being a data analyst using sound statistical tools to perform advanced analytics.

Prerequisites

Before attending this course, students should have taken or be familiar with the contents presented in Tableau Desktop Level 1: Introduction and the Tableau Desktop 2: Intermediate courses.

What You Will Learn

Integrate Tableaus analytics with the industry-standard, statistical prowess of R.

Make R function calls in Tableau, visualizing R functions with Tableau using RServe.

Use the CRISP-DM methodology to create a roadmap for analytics investigations.

Implement various supervised and unsupervised learning algorithms in R that return values to Tableau.

Get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.

Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau R calculated field functions.

Course Outline

Chapter 1 - Advanced Analytic with R and Tableau

Installing R and R Studio

Installing Rserve

Environment of R

Connecting to Rserve

Chapter 2 – The Power of R

Variables

Vectors and Lists

Matrices

Factors

Data Frames

Control Structures

For Loops

Functions

Using R in Tableau

Chapter 3 – Methodology for Advanced Analytics

CRISP-DM Model – Data Preparation

CRISP-DM – Modeling Phase

CRISP-DM – Evaluation

CRISP-DM – Deployment

CRISP-DM – Process Restarted

CRISP-DM – Summary

Working with Dirty Data

Introduction to Dplyr

Summarizing Data with Dplyr

Chapter 4 – Prediction with R and Tableau Using Regression

Simple Linear Regression

Comparing Actual Values with Predicted Results

Building a Multiple Regression Model

Solving the Business Question

Sharing Data Analysis with Tableau

Chapter 5 – Classifying Data With Tableau

Understanding the Data

Data Preparation

Describing the Data

Modeling in R

Decision Trees in Tableau Using R

Bayesian Methods

Graphs

Chapter 6 – Advanced Analytics Using Clustering

What is Clustering?

Finding Clusters in Data

How Does K-Means Work?

Creating a Tableau Group from Cluster Results

Scaling

Clustering Without K-Means

Statistics For Clustering

Chapter 7 – Advanced Analytics With Unsupervised Learning

What Are Neural Networks?

Backpropagation and Feedforward Neural Networks

Evaluating a Neural Network Model

Lift Scores

Visualizing Neural Network Results

Modeling and Evaluating Data in Tableau

Chapter 8 – Interpreting Your Results For Your Audience

Introduction to Decision System and Machine Learning

Fuzzy Logic

Bayesian Theory

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

Building Your Own Decision System-Based loT

Writing the Program

Testing

Enhancement