????十年之后，決定投身應用程序開發行業的技術精英們可能會后悔當初沒上一兩堂應用數學類課程。即將到來的數據洪流（稍后將展開介紹這方面的內容）將催生出一波全新的需求，對計算機科學家的需求。他們既是數學家，又是產品開發專家，同時還是偵探。

????D?J?帕蒂爾是數據科學領域的先驅。這是一門新興學科，旨在組織并理解由機器生成的所有數據。未來十年內，這個挑戰將呈指數級增長。

????目前，大約有4億臺設備連接到互聯網上，其中大部分是手機和電腦。到2020年，約500億臺涵蓋汽車到家電的各色設備將彼此聯通。屆時，各公司將需要像帕蒂爾這樣的數據科學家團隊，整理從互聯網庫存指標到客戶發布的Twitter消息等各種數據。這個角色非常重要，以至于風投基金Greylock Partners已經聘請帕蒂爾擔任“常駐數據科學家”，幫助投資對象發掘自身的數據，從中找出有用的模式和統計數字，幫助它們提高效率，提升決策水平，超越競爭對手。

????帕蒂爾曾經在商務社交網站LinkedIn擔任了三年的首席科學家。他深知科學數據的商業價值，只不過他把數學家的邏輯成功運用到了這項工作中。他的口頭禪是：“量化問題，爾后才能解決問題。”

????譯者：項航

????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."