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Advanced Analytics with R and Tableau.
Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R About This Book A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R Combine Tableau analytics and visualizati...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2016.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 framesControl 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 understandingModeling; 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 matrixPrerequisites; 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 ClusteringWhat 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.