September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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108
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第七章：可否請壞資料站出來？
0
0.1
0.2
0.3
0.4
0.5
0.6
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1
0 25 50 75 100 125 150 175 200 225
250
Cumulative Fraction
Page Views per User and Month
Users
Page Views
圖 7-5 依照圖 7-4 資料的累積分佈函式。請注意水平軸刻度的減少
學習到的事
我們可以藉由這些不同的故事學到什麼呢？先讓我們回憶一下：製造者的減少損毀方法
遇到了問題，因為他們沒有辦法確認資料的品質是如他們所預期的。在電話流量的研究
中，在資料與理論模型之間的無預期衝突，導致在資料中發現別的結構以及資訊，若不
是這樣，根本不會發現這些。而第三個（也是更通泛的）範例指出了現實世界中常見的
錯誤方式，多數基礎總和的統計方式（平均值或是中位數）沒有辦法對真實的行為表現
出實際的狀態。
在這些情境中的共同點就是，資料不是問題的所在。問題在於，資料本身與我們認為該
資料
應該
是如何之間的不一致。更清楚的說：並不是說這類的資料是「壞資料」，而是
因為我們對其貧乏的假設。然而，第二個故事告訴我們，若我們注意到實際資料與我們
期望之間的不同，那麼這個不一致性便能導致「創造性張力」，帶給我們擁有額外洞見
的機會。
學習到的事
|
109
在我的經驗中，對於資料的基礎假設驗證失敗（以品質、可達性、點分佈以及基礎性
質的方面來說），在資料導向專案中是最
常見的錯誤
。我認為有三個因子會影響這個現 ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.