September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第十五章：資料科學的黑暗面
個案研究：一間世界 500 強的公司擁有一個程序，會基於每天的動作產生資料。歷
史上看來，它們的資料倉儲團隊執行多數的分析任務，如傳統的商業智慧（Business
Intelligence，BI）。這個團隊的主要責任就是開發關聯式資料庫導向的工具，儘管他們
也涉足較不傳統的分析專案。
他們決定要為了這些不傳統的分析專案聘雇一個資料科學家。他們深信資料科學家會很
神奇的在資料中發現隱藏於其中的價值，並且將其轉換為一些結果。
管理方針很簡單：「去找出資料中有價值的東西！」他們並沒有再給更多的狀況或是方
向，而是單純的設定一個不實際的期待，希望這個巫師能夠施展一些魔法然後就回覆所
有的答案。
在這個狀況中的適當回應，也是某個資料科學家所提供的，就是反對這個命令，並且詢
問方向、瞭解整體狀況然後設定在分析工作中的參數。資料科學家的一部份工作以及價
值，就是幫助組織詢問對的問題，並且清除無意義的工作。
區別知識而不分享
這條戒律是很直覺的。這裡的概念是，身為一個組織，你應該要分享你的知識。當公司
之間的不同部門都在執行分析工作時，這點特別重要。
分享你的發現。若你在執行分析，然後你發現任何稍早所提到的陷阱—不論你發現遺失
資料、格式錯誤或是完成事情的捷徑—分享它們。若你因為一些原因而已經將資料聚合
在一起，而你認為這對其他的分析可能會有幫助，分享它。當你完成一個工作時，分享
你的發現（程式碼、結果、圖表或是其他的文件）。將你的假設寫成文件。將你的程式
碼寫成文件。召集一個非正式會議來分享並且討論。 ...
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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.