沒有學位就當不了數據科學家嗎?

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"本文最初發表在 Towards Data Science 博客,經原作者 Kurtis Pykes 授權,InfoQ 中文站翻譯並分享。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"很多人會問這樣的問題:沒有學位,我就當不了數據科學家嗎?在我看來,這個問題的簡單答案是:No! 在數據領域工作的人有很多,但卻沒有學歷證書來證明他們的角色……我就是其中之一。但有趣的是,隨着時間的流逝,我曾經所堅持的學士學位、碩士學位和博士學位在數據科學家崗位很重要的立場,逐漸土崩瓦解。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"儘管我們看到了科技領域最傑出的幾位成功者,其中很多人都輟學了,包括馬克·扎克伯格、比爾·蓋茨和史蒂夫·喬布斯等等。但我還是相信,在某種程度上,關注一個人的實際工作經驗會超過他們所受的教育。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/4d\/4ddaa5dbf5f441a081144e8ff9da92d2.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據科學並不關心你所學的專業或是否獲得學位,重要的是你如何處理數據。"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但我並不是建議所有想要進入數據科學領域的大學生都應該退學。攻讀學位有許多好處,例如,通常來講,大學畢業的人基本上一生賺的錢比未上過大學的人要多,同時,你也能學到一些學科所需要具備的基本技能,如果你選擇了技術學位(科學、技術、工程、數學),你將在數據科學的一個關鍵領域擁有深厚的基礎。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"相反,如果你並沒有好好學習,而是把上學的時間用來拓展人脈網,這會爲你畢業後的發展打下良好的基礎。"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"實質上,一個數據科學家需要有很好的編程、數學、統計和概率方面的知識,以及對商業領域的理解。假如你能有效地證明自己具有這些能力,那麼你的綜合實力就要超過這些證書所賦予你的價值。所以,爲了更有效地找到你的第一份數據科學家的工作,你應該考慮培養以下能力:"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"編程技能"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在編程領域,最常用的數據科學編程語言是 Python 和 R,要想開始研究數據,你至少要學會一種。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/19\/1925038e7c3cc218592b5167f757ce86.jpeg","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在對Python 和 R 兩種語言的選擇上,數據科學家們通常分成了兩派,而有一些數據科學家們也不會對另一種語言極爲排斥。我對兩者都進行了取樣,發現 Python 更容易學習,效率也更高。它的用途也比數據科學更廣泛,而且如果你想在其他領域發展自己的技能,它將對你有所幫助。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"數學、統計與概率"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"簡單地說“這張圖片有可能是貓”並不像“這張圖片有 80% 的機率是貓”那樣令人信服。這個例子很乏味,但是你應該使用統計學和概率來分析和解釋所有你提供的數據。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"此外,模型數據的許多算法都要求對數學中的不同主題有很好的理解,如線性代數和微積分(以及統計學和概率)。這可以幫助你瞭解和調試你的算法,讓你的算法能夠很好地解釋審計中出現的問題或者客戶爲什麼拒絕貸款。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"項目組合"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據被高度評價爲“新石油”,地球上的每一個人都在產生大量的數據,這種觀點對於思考數據科學非常重要。數據科學家必須從正在產生的大量複雜數據中提取信息,並重點強調哪些信息對業務有幫助,因此我們領域專家(這可以是數據科學家,也可以是團隊中的某個人)。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在任何情況下,你都必須能夠證明你能夠完成你所說的所有任務,因此,擁有一個令人信服的項目組合對於研究各種問題以及這些問題的解決方案很有幫助。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"人脈網絡"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在數據科學領域,“你的人脈就是你的淨資產”這句老話比以往更重要。說到底,這是一個全新的領域,沒有多少衡量標準來區分優秀的從業者,除非你僱傭並發現他們,但這就意味着巨大的投資風險。所以當你想要獲得第一份數據科學的工作時,找個人來擔保是很有用的。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"與此同時,將自己融入到數據科學文化中,這也是一個很好的方式,可以發展和學習數據科學的新知識,同時也可以讓你在找到第一份工作的時候感受到數據科學的魅力。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"總結"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"當你決定尋找一名數據科學家時,我個人認爲,你是否有學位並不重要。最終,招聘公司認爲他們有一個包括數據科學相關的問題,他們需要一位數據高手幫助他們深入研究這個問題,這樣公司才能發展壯大——但是公司並沒有說需要獲得學位。但是,要想讓數據科學家的工作更有效率,你必須具備一些必要的技能,好消息是,現在互聯網讓你可以在網上學習很多數據科學家所需要具備的技能,而且不必負擔債務。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"作者介紹:"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Kurtis Pykes,癡迷於數據科學、人工智能和商業技術應用。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"原文鏈接:"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"https:\/\/towardsdatascience.com\/do-i-need-a-degree-to-land-a-job-in-data-science-3e50b9a1a5e9"}]}]}
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