Advanced Analytics with R and Tableau

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R
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Advanced Analytics with R and Tableau

Jen Stirrup, Ruben Oliva Ramos

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R
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Book Details

ISBN 139781786460110
Paperback178 pages

Book Description

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.

Moving from data visualization into deeper, more advanced analytics? This book 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. 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.

By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.

Table of Contents

Chapter 1: Advanced Analytics with R and Tableau
Installing R for Windows
RStudio
Implementing the scripts for the book
Tableau and R connectivity using Rserve
Summary
Chapter 2: The Power of R
Core essentials of R programming
Data structures in R
Data frames
Control structures in R
For loops and vectorization in R
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
Team Data Science Process
Working with dirty data
Introduction to dplyr
Summary
Chapter 4: Prediction with R and Tableau Using Regression
Getting started with regression
Comparing actual values with predicted results
Getting started with multiple regression?
Solving the business question
Sharing our data analysis using Tableau
Summary
Chapter 5: Classifying Data with Tableau
Business understanding
Understanding the data
Modeling in R
Model deployment
Decision trees in Tableau using R
Bayesian methods
Graphs
Summary
Chapter 6: Advanced Analytics Using Clustering
What is Clustering?
Finding clusters in data
Clustering in Tableau
Clustering example in Tableau
Interpreting your results
How Clustering Works in Tableau
Scaling
Clustering without using k-means
Statistics for Clustering
Introduction to R
Summary
Chapter 7: Advanced Analytics with Unsupervised Learning
What are neural networks?
Backpropagation and Feedforward neural networks
Evaluating a neural network model
Neural network performance measures
Visualizing neural network results
Neural network in R
Modeling and evaluating data in Tableau
Summary
Chapter 8: Interpreting Your Results for Your Audience
Introduction to decision system and machine learning
Decision system-based Bayesian
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
Summary
References

What You Will Learn

  • Integrate Tableau's analytics with the industry-standard, statistical prowess of R.
  • Make R function calls in Tableau, and visualize 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 to return values to Tableau.
  • 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.

Authors

Table of Contents

Chapter 1: Advanced Analytics with R and Tableau
Installing R for Windows
RStudio
Implementing the scripts for the book
Tableau and R connectivity using Rserve
Summary
Chapter 2: The Power of R
Core essentials of R programming
Data structures in R
Data frames
Control structures in R
For loops and vectorization in R
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
Team Data Science Process
Working with dirty data
Introduction to dplyr
Summary
Chapter 4: Prediction with R and Tableau Using Regression
Getting started with regression
Comparing actual values with predicted results
Getting started with multiple regression?
Solving the business question
Sharing our data analysis using Tableau
Summary
Chapter 5: Classifying Data with Tableau
Business understanding
Understanding the data
Modeling in R
Model deployment
Decision trees in Tableau using R
Bayesian methods
Graphs
Summary
Chapter 6: Advanced Analytics Using Clustering
What is Clustering?
Finding clusters in data
Clustering in Tableau
Clustering example in Tableau
Interpreting your results
How Clustering Works in Tableau
Scaling
Clustering without using k-means
Statistics for Clustering
Introduction to R
Summary
Chapter 7: Advanced Analytics with Unsupervised Learning
What are neural networks?
Backpropagation and Feedforward neural networks
Evaluating a neural network model
Neural network performance measures
Visualizing neural network results
Neural network in R
Modeling and evaluating data in Tableau
Summary
Chapter 8: Interpreting Your Results for Your Audience
Introduction to decision system and machine learning
Decision system-based Bayesian
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
Summary
References

Book Details

ISBN 139781786460110
Paperback178 pages
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