Introduction to R for Business Intelligence

Jay Gendron

Introduction to R for Business Intelligence
  • 出版商: Packt Publishing
  • 出版日期: 2016-08-30
  • 售價: $1,550
  • 貴賓價: 9.5 折 $1,473
  • 語言: 英文
  • 頁數: 228
  • 裝訂: Paperback
  • ISBN: 1785280252
  • ISBN-13: 9781785280252
  • 相關分類: R 語言

    • 下單後立即進貨 (約3~4週)

商品描述

Key Features

  • Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful.
  • This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R.
  • Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide.

Book Description

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.

In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.

After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.

What you will learn

  • Extract, clean, and transform data
  • Validate the quality of the data and variables in datasets
  • Learn exploratory data analysis
  • Build regression models
  • Implement popular data-mining algorithms
  • Visualize results using popular graphs
  • Publish the results as a dashboard through Interactive Web Application frameworks

About the Author

Jay Gendron is an associate data scientist working with Booz Allen Hamilton. He has worked in the fields of machine learning, data analysis, and statistics for over a decade, and believes that good questions and compelling visualization make analytics accessible to decision makers. Jay is a business leader, entrepreneurial employee, artist, and author. He has a B.S.M.E. in mechanical engineering, an M.S. in management of technology, an M.S. in operations research, and graduate certificates for chief information officer and IT program management.

Jay is a lifelong learner—a member of the first cohort to earn the 10-course specialization in data science by Johns Hopkins University on Coursera. He is an award-winning speaker who has presented internationally and provides pro bono data science expertise to numerous not-for-profit organizations to improve their operational insights. Connect with Jay Gendron at https://www.linkedin.com/in/jaygendron, visit http://jgendron.github.io/, or Twitter @jaygendron.

Table of Contents

  1. Extract, Transform, and Load
  2. Data Cleaning
  3. Exploratory Data Analysis
  4. Linear Regression for Business
  5. Data Mining with Cluster Analysis
  6. Time Series Analysis
  7. Visualizing the Data's Story
  8. Web Dashboards with Shiny
  9. References
  10. Other Helpful R Functions
  11. R Packages Used in the Book
  12. R Code for Supporting Market Segment Business Case Calculations

商品描述(中文翻譯)


主要特點



  • 使用這本易於理解的指南，充分利用 R 分析的威力，使您的業務數據更具洞察力。

  • 這本高度實用的指南教您如何開發能夠幫助您做出明智決策的儀表板。

  • 通過這本全面指南，學習與商業智能相關的數據工作的 A 到 Z。


書籍描述


通過一位在一家成功且不斷發展的公司中擔任分析師的眼睛，探索商業智能的世界。通過支持該公司不同功能的使用案例來學習 R。本書提供了以數據驅動和分析為重點的方法，幫助您回答運營、市場和財務方面的問題。


在第一部分中，您將學習從不同來源提取數據、清理數據以及探索數據結構。在第二部分中，您將探索商業智能的預測模型和聚類分析，並分析金融時間序列。最後，在第三部分中，您將學習如何通過銳利的可視化和互動式的基於 Web 的儀表板來傳達結果。


完成使用案例後，您將能夠在 R 編程環境中處理商業數據，並了解數據科學如何幫助做出明智決策和制定業務策略。在此過程中，您將找到有關 R 和商業智能的有用提示。


您將學到什麼



  • 提取、清理和轉換數據

  • 驗證數據和數據集中變量的質量

  • 學習探索性數據分析

  • 構建回歸模型

  • 實施常用的數據挖掘算法

  • 使用常見圖形來可視化結果

  • 通過互動式 Web 應用程序框架將結果發布為儀表板


關於作者


Jay Gendron 是一位與 Booz Allen Hamilton 合作的副數據科學家。他在機器學習、數據分析和統計領域工作超過十年，並相信良好的問題和引人入勝的可視化使分析對決策者易於理解。Jay 是一位商業領袖、創業員工、藝術家和作者。他擁有機械工程學士學位、技術管理碩士學位、運營研究碩士學位，以及首席信息官和 IT 項目管理的研究生證書。


Jay 是一位終身學習者，是約翰霍普金斯大學在 Coursera 上首批完成 10 門數據科學專業課程的學員之一。他是一位屢獲殊榮的演講者，曾在國際上發表演講，並向眾多非營利組織提供免費的數據科學專業知識，以改善其運營洞察力。請訪問 https://www.linkedin.com/in/jaygendron 與 Jay Gendron 聯繫，或訪問 http://jgendron.github.io/，或在 Twitter 上關注 @jaygendron。


目錄



  1. 提取、轉換和加載數據

  2. 數據清理

  3. 探索性數據分析

  4. 商業線性回歸

  5. 使用聚類分析進行數據挖掘

  6. 時間序列分析

  7. 將數據的故事可視化

  8. 使用 Shiny 創建 Web 儀表板

  9. 參考資料

  10. 其他有用的 R 函數

  11. 書中使用的 R 套件

  12. 支持市場區段業務案例計算的 R 代碼


類似商品