數據分析英語怎麼說
數據分析是指用適當的統計分析方法對收集來的大量數據進行分析，那麼你知道數據分析用英語怎麼說嗎? 下面和本站小編一起來學習一下數據分析的英語說法吧。
data analysis
數據分析的相關短語數據分析員 Data Analyst
矩陣數據分析法 Matrix Data Analysis
統計數據分析 statistical data analysis
數據分析設備 data analysis facilities
經濟與數據分析 Economic and Statistical Analysis
數據分析的英語例句1. Basic to any analysis of categorical data is a consideration of how the data was collected.
任何範疇數據分析的基礎在於考慮數據是怎樣收集的.
2. Data were analyzed using variance and multiple regression analysis.
數據分析採用方差分析、多元迴歸分析.
3. Hypotheses testing and general data analysis are accomplished with regression analysis.
數據分析過程主要運用迴歸分析完成.
4. Let me make this clear: A bar chart is not analytics.
首先我們必須明白: 一張條形圖不是數據分析.
5. Good skills of data analysis and reporting by computer.
熟練運用辦公自動化進行數據分析和報告.
6. Responsible for temperature monitoring devices management and temperature excursion mation.
負責溫度追蹤儀器的治理及超標溫度數據分析.
7. Good ability at test design and operating, data analysis and summary.
能獨立進行測試計劃設計,測試操作, 及進行數據分析彙總.
8. In data analysis, congregate is a process of large amount original data.
彙總被廣泛應用於數據分析, 是一種對原始數據進行統計加工整理的過程.
9. In chapter 6, the results of experiments and data analysis are provided.
第六章給出了該系統在正式測試中的結果以及數據分析.
10. SPM 99 was used to process data and localize functional areas.
採用SPM99軟件進行數據分析和腦功能區定位.
11. Provide HR information analysis report and track the progress.
提交人力資源數據分析報告並督導執行.
12. VDA dynamically identifies different protocol segments in a packet.
垂直數據分析(VDA)動態識別一個包中不同的協議段.
13. The original analysis detected storm - driven swell shaking the ice.
通過最初的數據分析發現,猛烈的膨脹會對冰產生動搖作用.
14. Providing daily operational analysis report to relevant personals and project managers.
爲項目負責人及相關人員提供日常運營數據分析報告.
15. The reliability life data analysis is an important basis of reliability engineering.
可靠性壽命數據分析是可靠性工程研究的重要基礎.
數據分析相關閱讀：數據會撒謊 大數據並非萬能Clay Christensen tells a good joke about a tour of heaven.
克雷.克里斯坦森(Clay Christensen)講了一個有關天堂旅遊的有趣笑話。
How come there’s no data here? the Harvard professor asks his celestial guide.
這裏怎麼沒有數據呢?這位哈佛教授問他的天堂嚮導。
Because data lies, comes the response.
因爲數據撒謊，對方回答說。
And that is why, Prof Christensen goes on, whenever anyone says ‘Show me the data’, I just say ‘Go to hell’.
克里斯坦森教授接着講，所以每當有人說‘把數據拿給我看’時，我就會說‘下地獄去’。
The gag got a laugh at last week’s Drucker Forum in Vienna,
在近期在維也納舉行的德魯克論壇(Drucker Forum)上，這個笑話引起了笑聲。
where fans of the late Peter Drucker’s claim that management is a liberal art voiced fears about the way data are wielded to crush human insight and inventiveness.
在論壇上，認同已故彼得.德魯克(Peter Drucker)的管理屬於一門文科觀點的粉絲們，表達了對數據被用來碾壓人類洞察力和創造力的擔心。
But there are signs of a backlash against big data even where it has loomed largest.
但目前有跡象表明，即便在大數據運用最廣泛的領域，大數據也遭遇了強烈反彈。
As chief executive of UK supermarket chain J Sainsbury until 2014, Justin King commanded a data set that showed, for instance, that purchases of diet products were the best indication that customers were planning to go on holiday — and that they might therefore be open to some deft direct marketing of suntan lotion.
比如，擔任英國連鎖超市森寶利(J Sainsbury)首席執行官直至2014年的賈斯廷.金(Justin King)掌握的一個數據集顯示，購買減肥食品是顧客打算去度假的最佳信號，因此他們可能很容易接受某些精明的防曬霜直接營銷。
He believes retailers should use such information to represent the shopper better in, say, negotiations with suppliers.
他認爲，零售商應當使用這類數據——比如在與供應商的談判中——更好地代表顧客。
But at a Financial Times 125 Forum I chaired recently, he said he worried data were now used against customers.
但在不久前我主持的英國《金融時報》125論壇(FT 125 Forum)上，他表示，他擔心如今數據的使用是不利於顧客的。
He has, for instance, criticised the use of loyalty card data to game the customer by offering them vouchers to switch brands.
例如，他對利用積分卡數據算計顧客、通過提供代金券誘使他們轉換品牌的做法提出了批評。
It is too soon to declare the triumph of what one ex-colleague used to call big anecdote over the ideology of easy-to-measurism that has held boardrooms in thrall for the past few years.
現在要宣稱我的一名前同事所稱的重磅軼事相對於易於衡量觀念——過去幾年企業董事會牢牢奉行這種觀念——取得了勝利，還爲時尚早。
For example, the hastily declared failure of pollsters to predict a Donald Trump victory in the US election is more likely to be due to unsound one-on-one surveys than yawning deficiencies in wider data-gathering.
例如，有人倉促宣佈民意調查機構未能預測到唐納德.特朗普(Donald Trump)在美國大選中獲勝，但預測失敗的原因更有可能是不可靠的一對一調查，而不是宏觀數據收集方面的巨大缺點。
The science of data analytics, when combined with cognitive computing and even neuroscientific and behavioural research, is also going to get more sophisticated and precise.
數據分析科學，跟認知計算、甚至還有神經科學與行爲研究結合在一起，也將變得更先進、更精確。
For now, some of the tools measuring customer satisfaction are as blunt as those smiley-face pads you find at airports, asking you to assess your experience.
目前，有些衡量顧客滿意度的工具就像你在機場發現的邀請你給旅途體驗打分的笑臉打分板一樣生硬。
I still wonder how the airline I flew with last summer interpreted the input from the cheerful toddler who was repeatedly stabbing the angry-face icon on the machine at our departure gate.
我仍在好奇，今年夏季我乘坐飛機的那家航空公司，對於那個開心的學步小童反覆去戳登機口旁那臺機器上的憤怒臉圖標意味着什麼如何解釋。
Separately, Facebook — whose access to vast user-created troves of information retailers and airlines can only dream about — has got into trouble with its advertising customers after admitting mistakes measuring the time users spend viewing video advertisements and articles.
另外，Facebook在廣告客戶那裏遇到了麻煩，因爲Facebook承認，在衡量用戶觀看視頻廣告和閱讀文章的時間上出了錯誤。Facebook掌握着零售商和航空公司只能夢想一番的海量用戶生成信息。
Too often, computer-generated facts come close to overruling common sense.
有太多時候，計算機生成的事實幾乎碾壓常識。
When Pope John Paul II died in 2005, a senior editor noted that the news had surged to the top of the FT website’s most-read stories and ordered me (I was then editing our opinion pages), to commission insights into Vatican policies, Catholic mores and papal history — none of which was a hit.
當2005年教皇約翰.保羅二世(Pope John Paul II)去世時，一名資深編輯注意到，該消息已猛升至英國《金融時報》網站熱門文章首位，然後命令我(當時我是觀點版面的編輯)約一些有關梵蒂岡政策、天主教習俗和教皇曆史的分析文章，結果這些文章沒有一篇受到追捧。
Three days later, Saul Bellow died.
三天後，索爾.貝婁(Saul Bellow)去世，
His obituary also topped the rankings.
他的訃告也登上了榜首，
There was no corresponding call to deepen our coverage of US novelists and their work.
但沒人打電話讓我們做美國小說家及其作品的深度報道。
Insights from only a few users can still be valuable.
就算只是少數用戶的意見，也可能很有價值。
Mr King advises against ignoring the shopper who complains she waited 15 minutes at the self-service tills, even if your spreadsheet shows the average wait was two minutes.
金建議，不要忽視抱怨自己在自助收銀機那裏等待了15分鐘的顧客，即使你的電子表格顯示平均等待時間是2分鐘。
Her perception that it took much longer may tell you more than whole dashboards of data.
她感到等待的時間長得多，這或許能告訴你全部數據以外的東西。
Similarly, asked what Spotify would do with the customers from hell, Joakim Sundén, senior tech leader at the music streaming service, told the Drucker Forum that their deep pain might be telling you about a problem you had not identified.
同樣，當被問到Spotify如何應對來自地獄的顧客時，這家音樂流媒體服務公司的資深技術主管若阿基姆.鬆登(Joakim Sundén)在德魯克論壇上說，他們的深度痛苦或許正在告訴你一個你之前未曾發現的問題。
Remember, too, that there are some situations in which data may never be much help.
也要記住，在某些情況下，數據或許永遠幫不上大忙。
One is innovation, where the tyranny of the business plan cramps ideas and narrows options, according to experts gathered in Vienna last week.
德魯克論壇上的專家認爲，一個是創新，專橫的商業計劃束縛了思想，侷限了選項。
As Rita Gunther McGrath of Columbia Business School puts it: It’s always easier to go back to the spreadsheet.
正如哥倫比亞商學院(Columbia Business School)的麗塔.岡瑟.麥格拉思(Rita Gunther McGrath)所說：回去看電子表格，總是更容易的。
Roger Martin, who heads the Rotman management school’s Martin Prosperity Institute, says he would ban the word proven from organisations that wish to innovate.
羅特曼管理學院(Rotman School of Management)馬丁繁榮研究所(Martin Prosperity Institute)所長羅傑.馬丁(Roger Martin)說，他會禁止希望創新的機構使用經過驗證的這個詞。
It’s hard to explore possibilities if you have to know the answer before you start, adds Tim Brown, chief executive of Ideo.
如果你必須在開始前知道答案，那就很難探索可能性了，Ideo首席執行官蒂姆.布朗(Tim Brown)補充說。
Knowing your customer will never be a zero-sum contest between a researcher with a clipboard and IBM’s Watson.
理解你的客戶，永遠不是拿着帶夾子的寫字板的研?a href="">咳嗽焙虸BM的沃森(Watson)之間的零和競爭。
Nor should it be.
也不應該是。
The best insights come from some hard-to-define blend of what you know from listening to individual users, what you can learn from their collective past behaviour and what you intuit they will want in future.
最好的理解產生於一種難以定義的混合認知：你傾聽單個用戶所瞭解到的東西，你從他們的集體過往行爲中學到的東西，以及你從直覺知道他們未來想要的東西。
The really flawed assumption is that a capsule of data inserted into the analytics machine will always generate the perfect brew.
真正錯誤的假設是，把一些數據輸入分析機器，總會生成最佳答案。
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