Creating Good Data: A Guide to Dataset Structure and Data Representation

Foxwell, Harry

  • 出版商: Apress
  • 出版日期: 2020-10-02
  • 售價: $1,575
  • 貴賓價: 9.5$1,496
  • 語言: 英文
  • 頁數: 105
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 148426102X
  • ISBN-13: 9781484261026
  • 相關分類: 資料庫
  • 立即出貨 (庫存=1)

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商品描述

Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.

Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.

This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.

 

What You Will Learn

 

  • Be aware of the principles of creating and collecting data
  • Know the basic data types and representations
  • Select data types, anticipating analysis goals
  • Understand dataset structures and practices for analyzing and sharing
  • Be guided by examples and use cases (good and bad)
  • Use cleaning tools and methods to create good data

 

 

 

 

 

 

 

Who This Book Is For

Researchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.

商品描述(中文翻譯)

從一開始就創建好的資料,而不是在收集完後再修復。遵循本書的指南,您將能夠進行更有效的分析並及時呈現研究數據。

數據分析師通常會遇到設計不良的數據集,這導致解釋困難並延遲產生有意義的結果。許多數據分析培訓著重於如何在嚴肅分析之前清理和轉換數據集。通過使用良好的數據集設計並了解數據類型如何決定可進行的分析類型,可以避免不適當或混淆的表示、單位選擇、編碼錯誤、缺失值、異常值等問題。

本書討論了數據集創建的原則和最佳實踐,涵蓋了基本數據類型及其相關的適當統計和可視化方法。本書的重點是為什麼選擇某些數據類型來表示概念和測量,而不是典型的討論一旦選擇了特定數據類型如何進行分析。

您將學到什麼:

- 瞭解創建和收集數據的原則
- 知道基本數據類型和表示方法
- 選擇數據類型,預測分析目標
- 理解數據集結構和分析共享的實踐
- 通過示例和用例(好的和壞的)進行指導
- 使用清理工具和方法創建良好的數據

本書適合對研究設計、數據收集、分析和報告結果感興趣的研究人員,他們可以使用本書的最佳實踐來產生更好的描述和解釋。此外,探索和解釋其他研究人員數據的數據分析師將能夠創建更好的數據集。

作者簡介

Harry J. Foxwell is a professor. He teaches graduate data analytics courses at George Mason University in the department of Information Sciences and Technology and he designed the data analytics curricula for his university courses. He draws on his decades of experience as Principal System Engineer for Oracle and for other major IT companies to help his students understand the concepts, tools, and practices of big data projects. He is co-author of several books on operating systems administration. He is a US Army combat veteran, having served in Vietnam as a Platoon Sergeant in the First Infantry Division. He lives in Fairfax, Virginia with his wife Eileen and two bothersome cats.

 

 

作者簡介(中文翻譯)

Harry J. Foxwell是一位教授。他在喬治梅森大學資訊科學與技術系教授研究生數據分析課程,並為該大學的課程設計了數據分析課程。他憑藉自己在Oracle和其他主要IT公司擔任首席系統工程師的數十年經驗,幫助學生理解大數據項目的概念、工具和實踐。他是幾本關於操作系統管理的書籍的合著者。他是一位美國陸軍退伍軍人,在越南服役時擔任第一步兵師的排長。他與妻子Eileen和兩隻令人困擾的貓一起居住在維吉尼亞州費爾法克斯。