September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第十九章：揭開資料品質分析的神祕面紗：瞭解何時你的資料已經夠好了
小的問題。第二，直覺可能是錯的。你要如何驗證你的直覺告訴你有趣（或是不有趣）
的事情？第三，如上所討論的，直覺依賴進化的經驗。假如你缺少直接的領域知識會發
生什麼事情？這幾乎可以成為一個學術問題；在資料產業中，我們經常會遭遇到全新的
問題、全新的資料集合以及全新的資料來源的狀況。一個系統化的資料品質分析方法，
能夠在你花費大量的時間決定個人或是商務決策之前，有效且一致的導引你，讓你對於
資料的性質以及品質的關心到一個更高層級。
從一個資料品質框架開始運作讓你能夠：
• 不會再擔心你認為你知道或是不知道的資料。
• 走出傳統關於你需要什麼以及不需要什麼的智慧，並且為你的資料以及它的問題建
立新的觀念。
• 跨越廣泛不同的情境以及應用程式，開發並且重新使用資料品質管理的工具。
本章敘述一個用來作資料品質分析的概念性框架以及方法，希望能夠成為在你的目的的
本質下，要如何思考你的資料的導引。這邊討論的概念都是從（多數是痛苦的）經驗而
來，而對每個已經花費一些時間處理資料的人來說很可能不是新的概念；但我們希望它
對於這些新進入資料分析領域的人，以及任何尋求建立或是增進好的資料習慣的人，也
會是有用的。
框架簡介：資料品質分析的 4C
因為在尋找答案時可以從許多的角度來瀏覽資料，所以評估品質就有許多檢視的角度。
在下方，我們描繪了由四個面向所組成的概念性框架。我們將其稱為「資料品質分析的
4C」
2
：
1. 完整（Complete）：所有該在這邊的東西都在嗎？ ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.