????十年之后，決定投身應(yīng)用程序開(kāi)發(fā)行業(yè)的技術(shù)精英們可能會(huì)后悔當(dāng)初沒(méi)上一兩堂應(yīng)用數(shù)學(xué)類課程。即將到來(lái)的數(shù)據(jù)洪流（稍后將展開(kāi)介紹這方面的內(nèi)容）將催生出一波全新的需求，對(duì)計(jì)算機(jī)科學(xué)家的需求。他們既是數(shù)學(xué)家，又是產(chǎn)品開(kāi)發(fā)專家，同時(shí)還是偵探。

????D?J?帕蒂爾是數(shù)據(jù)科學(xué)領(lǐng)域的先驅(qū)。這是一門新興學(xué)科，旨在組織并理解由機(jī)器生成的所有數(shù)據(jù)。未來(lái)十年內(nèi)，這個(gè)挑戰(zhàn)將呈指數(shù)級(jí)增長(zhǎng)。

????目前，大約有4億臺(tái)設(shè)備連接到互聯(lián)網(wǎng)上，其中大部分是手機(jī)和電腦。到2020年，約500億臺(tái)涵蓋汽車到家電的各色設(shè)備將彼此聯(lián)通。屆時(shí)，各公司將需要像帕蒂爾這樣的數(shù)據(jù)科學(xué)家團(tuán)隊(duì)，整理從互聯(lián)網(wǎng)庫(kù)存指標(biāo)到客戶發(fā)布的Twitter消息等各種數(shù)據(jù)。這個(gè)角色非常重要，以至于風(fēng)投基金Greylock Partners已經(jīng)聘請(qǐng)帕蒂爾擔(dān)任“常駐數(shù)據(jù)科學(xué)家”，幫助投資對(duì)象發(fā)掘自身的數(shù)據(jù)，從中找出有用的模式和統(tǒng)計(jì)數(shù)字，幫助它們提高效率，提升決策水平，超越競(jìng)爭(zhēng)對(duì)手。

????帕蒂爾曾經(jīng)在商務(wù)社交網(wǎng)站LinkedIn擔(dān)任了三年的首席科學(xué)家。他深知科學(xué)數(shù)據(jù)的商業(yè)價(jià)值，只不過(guò)他把數(shù)學(xué)家的邏輯成功運(yùn)用到了這項(xiàng)工作中。他的口頭禪是：“量化問(wèn)題，爾后才能解決問(wèn)題。”

????譯者：項(xiàng)航

????A decade from now the smart techies who decided to become app developers may wish they had taken an applied-mathematics class or two. The coming deluge of data (more on that in a moment) will create demand for a new kind of computer scientist -- a gig that's one part mathematician, one part product-development guru, and one part detective.

????D.J. Patil is a pioneer in the field of data science, a new discipline that aims to organize and make sense of all the data generated by machines. It's a challenge that will grow exponentially over the next decade.

????Today there are some 400 million devices connected to the Internet, mostly phones and computers. By 2020 some 50 billion devices, from cars to appliances, will be talking to one another. And companies will need teams of data scientists like Patil to sort through everything from internal inventory metrics to customer tweets. The role is so important that Greylock Partners has hired Patil to serve as a "data scientist in residence" to help its portfolio companies mine their data for patterns or stats that will make them more efficient or smarter than their competitors.

????Patil understands the business value of data science -- he was LinkedIn's (LNKD) chief scientist for three years -- but he brings a mathematician's logic to the job. His mantra? "If you can't measure it, you can't fix it."