舉報 
會員
Advanced Analytics with R and Tableau
最新章節(jié)：
Index
ThisbookwillappealtoTableauuserswhowanttogobeyondtheTableauinterfaceanddeploythefullpotentialofTableau,byusingRtoperformadvancedanalyticswithTableau.AbasicfamiliaritywithRisusefulbutnotcompulsory,asthebookwillstartoffwithconcreteexamplesofRandwillmovequicklyintomoreadvancedspheresofanalyticsusingonlinedatasourcestosupporthands-onlearning.ThoseRdeveloperswhowanttointegrateRinTableauwillalsobenefitfromthisbook.
目錄(86章)
倒序
- 封面
- 書名頁
- Advanced Analytics with R and Tableau
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- 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
- Index 更新時間：2021-07-02 20:26:18
推薦閱讀
- 演進式架構(gòu)（原書第2版）
- Raspberry Pi for Python Programmers Cookbook（Second Edition）
- WildFly：New Features
- HTML5+CSS3基礎開發(fā)教程（第2版）
- INSTANT Sencha Touch
- SQL Server 2016數(shù)據(jù)庫應用與開發(fā)習題解答與上機指導
- 微信小程序入門指南
- MATLAB 2020從入門到精通
- Python機器學習算法: 原理、實現(xiàn)與案例
- Android項目實戰(zhàn)：手機安全衛(wèi)士開發(fā)案例解析
- Everyday Data Structures
- Julia數(shù)據(jù)科學應用
- Hands-On Robotics Programming with C++
- 百萬在線：大型游戲服務端開發(fā)
- 優(yōu)化驅(qū)動的設計方法
- 循序漸進Vue.js 3前端開發(fā)實戰(zhàn)
- Spark for Data Science
- 趣學Python游戲編程
- SQL優(yōu)化核心思想
- Objective-C入門教程
- 設計模式的藝術
- Mastering Python Data Analysis
- 計算機組裝與維護實用教程
- Visual C#程序設計應用教程（第2版）
- 零基礎學Python編程實戰(zhàn)
- 從零開始：HTML5+CSS3快速入門教程
- 強化學習：原理與Python實現(xiàn)
- 自然語言處理與Java語言實現(xiàn)
- H5安全開發(fā)實踐教程
- C++程序設計（第2版）
