Cover

Copyright

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Advanced Analytics with R and Tableau

Installing R for Windows

RStudio

Prerequisites for RStudio installation

Implementing the scripts for the book

Testing the scripting

Tableau and R connectivity using Rserve

Installing Rserve

Configuring an Rserve Connection

Summary

Chapter 2: The Power of R

Core essentials of R programming

Variables

Creating variables

Working with variables

Data structures in R

Vector

Lists

Matrices

Factors

Data frames

Control structures in R

Assignment operators

Logical operators

For loops and vectorization in R

For loops

Functions

Creating your own function

Making R run more efficiently in Tableau

Summary

Chapter 3: A Methodology for Advanced Analytics Using Tableau and R

Industry standard methodologies for analytics

CRISP-DM

Business understanding/data understanding

CRISP-DM model — data preparation

CRISP-DM — modeling phase

CRISP-DM — evaluation

CRISP-DM — deployment

CRISP-DM — process restarted

CRISP-DM summary

Team Data Science Process

Business understanding

Data acquisition and understanding

Modeling

Deployment

TDSP Summary

Working with dirty data

Introduction to dplyr

Summarizing the data with dplyr

Summary

Chapter 4: Prediction with R and Tableau Using Regression

Getting started with regression

Simple linear regression

Using lm() to conduct a simple linear regression

Coefficients

Residual standard error

Comparing actual values with predicted results

Investigating relationships in the data

Replicating our results using R and Tableau together

Getting started with multiple regression?

Building our multiple regression model

Confusion matrix

Prerequisites

Instructions

Solving the business question

What do the terms mean?

Understanding the performance of the result

Next steps

Sharing our data analysis using Tableau

Interpreting the results

Summary

Chapter 5: Classifying Data with Tableau

Business understanding

Understanding the data

Data preparation

Describing the data

Data exploration

Modeling in R

Analyzing the results of the decision tree

Model deployment

Decision trees in Tableau using R

Bayesian methods

Graphs

Terminology and representations

Graph implementations

Summary

Chapter 6: Advanced Analytics Using Clustering

What is Clustering?

Finding clusters in data

Why can't I drag my Clusters to the Analytics pane?

Clustering in Tableau

How does k-means work?

How to do Clustering in Tableau

Creating Clusters

Clustering example in Tableau

Creating a Tableau group from cluster results

Constraints on saving Clusters

Interpreting your results

How Clustering Works in Tableau

The clustering algorithm

Scaling

Clustering without using k-means

Hierarchical modeling

Statistics for Clustering

Describing Clusters – Summary tab

Testing your Clustering

Describing Clusters – Models Tab

Introduction to R

Summary

Chapter 7: Advanced Analytics with Unsupervised Learning

What are neural networks?

Different types of neural networks

Backpropagation and Feedforward neural networks

Evaluating a neural network model

Neural network performance measures

Receiver Operating Characteristic curve

Precision and Recall curve

Lift scores

Visualizing neural network results

Neural network in R

Modeling and evaluating data in Tableau

Using Tableau to evaluate data

Summary

Chapter 8: Interpreting Your Results for Your Audience

Introduction to decision system and machine learning

Decision system-based Bayesian

Decision system-based fuzzy logic

Bayesian Theory

Fuzzy logic

Building a simple decision system-based Bayesian theory

Integrating a decision system and IoT project

Building your own decision system-based IoT

Wiring

Writing the program

Testing

Enhancement

Summary

References

Index