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)



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.






- 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



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.




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。