Johnsonhsieh.github.io Website Review


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Website Value $50
Alexa Rank 6516232
Monthly Visits 555
Daily Visits 19
Monthly Earnings $2.78
Daily Earnings $0.09
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Johnsonhsieh.github.io Server Location

Country: United States
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Latitude: 37.751
Longitude: -97.822




Summarized Content

事實上,從古到今資料科學的本質並沒有不同,改變的只是因應人類活動型態的轉變,使得數據的收集方式及其分析方法跟著轉變罷了。現在Big Data很熱門,卻也陷入Big的迷思,忘了資料科學的本質其實是在Data上。談到Data,必須先說明資料如何收集。一般來說可以分成top-down與bottom-up兩種。所謂的TOP-DOWN係指企業主在資料收集前,有既定的問題想要解答,譬如某樂利杯咖啡的企業想要訂定飲品的優惠行銷策略,這時候必須透過收集各個分店每種飲品在各種時間與季節的銷售數據,甚至是特殊優惠活動時飲品的銷售記錄等等資料,進行數據分析以回答問題。另一種BOTTOM-UP是從既有資料中尋找規則,譬如從前述飲品的銷售數據中,歸納出那些飲品組合最受到消費者喜愛,讓企業達到飲品事先推薦、飲品銷售預測等等目的。當然,資料的收集並不侷限是top-down或是bottom-up,而是兩者上上下下的循環,藉由不斷的調整為企業創造最大的利益。以上例子,可以被應用在一間小店上,也可以用在全台展店數百家的企業上,甚至可以用在全球五大洲擁有數千間連鎖分店的企業之上。對於資料科學而言,無論資料規模的大小,透過Data來解決問題的本質是始終不變的。差別僅在於時代的進步,資料收集的方式比以往方便也更加多元。當然相對於可以在中小規模的數據上使用的統計方法,套到大數據時會有運算效率不彰的問題,但我認為這是技術上的問題,並不影響基本的統計思維。 把問題收斂,如果企業主想要利用Data來 (無論是Big Data, Middle Data 還是 Small Data) 課程設計的目標,除了各自培養三個階段的人才之外,同時也注重這三種人才的交流與整合。 其中DSP團訓班 總之,我想要強調的有兩點:1. 現階段的資料科學正處於BIG


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