Clean Data - Data Science Strategies for Tackling Dirty Data

Megan Squire

Clean Data - Data Science Strategies for Tackling Dirty Data
  • 出版商: Packt Publishing
  • 出版日期: 2015-05-29
  • 售價: $1,730
  • 貴賓價: 9.5 折 $1,644
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Paperback
  • ISBN: 1785284010
  • ISBN-13: 9781785284014
  • 相關分類: Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

Key Features

  • Grow your data science expertise by filling your toolbox with proven strategies for a wide variety of cleaning challenges
  • Familiarize yourself with the crucial data cleaning processes, and share your own clean data sets with others
  • Complete real-world projects using data from Twitter and Stack Overflow

Book Description

Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise.

The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that the rest of the book relies on: file formats, data types, and character encodings. You will also learn how to extract and clean data stored in RDBMS, web files, and PDF documents, through practical examples.

At the end of the book, you will be given a chance to tackle a couple of real-world projects.

What you will learn

  • Understand the role of data cleaning in the overall data science process
  • Learn the basics of file formats, data types, and character encodings to clean data properly
  • Master critical features of the spreadsheet and text editor for organizing and manipulating data
  • Convert data from one common format to another, including JSON, CSV, and some special-purpose formats
  • Implement three different strategies for parsing and cleaning data found in HTML files on the Web
  • Reveal the mysteries of PDF documents and learn how to pull out just the data you want
  • Develop a range of solutions for detecting and cleaning bad data stored in an RDBMS
  • Create your own clean data sets that can be packaged, licensed, and shared with others
  • Use the tools from this book to complete two real-world projects using data from Twitter and Stack Overflow

About the Author

Megan Squire is a professor of computing sciences at Elon University. She has been collecting and cleaning dirty data for two decades. She is also the leader of FLOSSmole.org, a research project to collect data and analyze it in order to learn how free, libre, and open source software is made.

Table of Contents

  1. Why Do You Need Clean Data?
  2. Fundamentals Formats, Types, and Encodings
  3. Workhorses of Clean Data Spreadsheets and Text Editors
  4. Speaking the Lingua Franca Data Conversions
  5. Collecting and Cleaning Data from the Web
  6. Cleaning Data in Pdf Files
  7. RDBMS Cleaning Techniques
  8. Best Practices for Sharing Your Clean Data
  9. Stack Overflow Project
  10. Twitter Project

商品描述(中文翻譯)

主要特點



  • 通過掌握各種清理挑戰的成熟策略，提升您的數據科學專業知識

  • 熟悉關鍵的數據清理過程，並與他人分享您自己的乾淨數據集

  • 使用來自Twitter和Stack Overflow的數據完成真實世界的項目

書籍描述


您是否大部分時間都在進行乏味的任務，如清理骯髒數據、處理丟失數據和準備供他人使用的數據？如果是的話，擁有正確的工具對於提升您的數據科學專業知識至關重要，也是一個很好的投資。


本書首先強調數據清理在數據科學中的重要性，並向您展示如何從改進清理過程中獲得回報。接下來，您將鞏固對基本概念的理解，這些概念是本書其餘部分所依賴的：文件格式、數據類型和字符編碼。您還將通過實際示例學習如何提取和清理存儲在RDBMS、網絡文件和PDF文檔中的數據。


在本書結尾，您將有機會處理幾個真實世界的項目。

您將學到什麼



  • 了解數據清理在整個數據科學過程中的作用

  • 學習正確清理數據的基本文件格式、數據類型和字符編碼

  • 掌握用於組織和操作數據的試算表和文本編輯器的關鍵功能

  • 將數據從一種常見格式轉換為另一種格式，包括JSON、CSV和一些特殊用途的格式

  • 實施三種不同的策略，用於解析和清理網絡上的HTML文件中的數據

  • 揭示PDF文檔的奧秘，學習如何提取您想要的數據

  • 開發一系列解決方案，用於檢測和清理存儲在RDBMS中的壞數據

  • 創建自己的乾淨數據集，可以打包、許可和與他人分享

  • 使用本書中的工具，使用來自Twitter和Stack Overflow的數據完成兩個真實世界的項目

關於作者


Megan Squire是Elon大學的計算科學教授。她已經收集和清理骯髒數據二十年。她還是FLOSSmole.org的領導者，這是一個研究項目，旨在收集數據並進行分析，以了解自由、開源軟件的製作過程。

目錄



  1. 為什麼需要乾淨數據？

  2. 基本格式、類型和編碼

  3. 乾淨數據的工具：試算表和文本編輯器

  4. 說通用語言：數據轉換

  5. 從網絡收集和清理數據

  6. 清理PDF文件中的數據

  7. RDBMS清理技術

  8. 分享乾淨數據的最佳實踐

  9. Stack Overflow項目

  10. Twitter項目

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