Advanced analytics with R and Tableau advanced visual analytical solutions for your business
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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing
2017
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Includes bibliographical references and index
- 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
- 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?