没有学位就当不了数据科学家吗？

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"本文最初发表在 Towards Data Science 博客，经原作者 Kurtis Pykes 授权，InfoQ 中文站翻译并分享。"}]},{"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":"很多人会问这样的问题：没有学位，我就当不了数据科学家吗？在我看来，这个问题的简单答案是：No! 在数据领域工作的人有很多，但却没有学历证书来证明他们的角色……我就是其中之一。但有趣的是，随着时间的流逝，我曾经所坚持的学士学位、硕士学位和博士学位在数据科学家岗位很重要的立场，逐渐土崩瓦解。"}]},{"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":"尽管我们看到了科技领域最杰出的几位成功者，其中很多人都辍学了，包括马克·扎克伯格、比尔·盖茨和史蒂夫·乔布斯等等。但我还是相信，在某种程度上，关注一个人的实际工作经验会超过他们所受的教育。"}]},{"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":"数据科学并不关心你所学的专业或是否获得学位，重要的是你如何处理数据。"}]}]},{"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":"但我并不是建议所有想要进入数据科学领域的大学生都应该退学。攻读学位有许多好处，例如，通常来讲，大学毕业的人基本上一生赚的钱比未上过大学的人要多，同时，你也能学到一些学科所需要具备的基本技能，如果你选择了技术学位（科学、技术、工程、数学），你将在数据科学的一个关键领域拥有深厚的基础。"}]},{"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，如果你并没有好好学习，而是把上学的时间用来拓展人脉网，这会为你毕业后的发展打下良好的基础。"}]}]},{"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":"实质上，一个数据科学家需要有很好的编程、数学、统计和概率方面的知识，以及对商业领域的理解。假如你能有效地证明自己具有这些能力，那么你的综合实力就要超过这些证书所赋予你的价值。所以，为了更有效地找到你的第一份数据科学家的工作，你应该考虑培养以下能力："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"编程技能"}]},{"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":"在编程领域，最常用的数据科学编程语言是 Python 和 R，要想开始研究数据，你至少要学会一种。"}]},{"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":"在对Python 和 R 两种语言的选择上，数据科学家们通常分成了两派，而有一些数据科学家们也不会对另一种语言极为排斥。我对两者都进行了取样，发现 Python 更容易学习，效率也更高。它的用途也比数据科学更广泛，而且如果你想在其他领域发展自己的技能，它将对你有所帮助。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"数学、统计与概率"}]},{"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":"简单地说“这张图片有可能是猫”并不像“这张图片有 80% 的机率是猫”那样令人信服。这个例子很乏味，但是你应该使用统计学和概率来分析和解释所有你提供的数据。"}]},{"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，模型数据的许多算法都要求对数学中的不同主题有很好的理解，如线性代数和微积分（以及统计学和概率）。这可以帮助你了解和调试你的算法，让你的算法能够很好地解释审计中出现的问题或者客户为什么拒绝贷款。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"项目组合"}]},{"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":"数据被高度评价为“新石油”，地球上的每一个人都在产生大量的数据，这种观点对于思考数据科学非常重要。数据科学家必须从正在产生的大量复杂数据中提取信息，并重点强调哪些信息对业务有帮助，因此我们领域专家（这可以是数据科学家，也可以是团队中的某个人）。"}]},{"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":"在任何情况下，你都必须能够证明你能够完成你所说的所有任务，因此，拥有一个令人信服的项目组合对于研究各种问题以及这些问题的解决方案很有帮助。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"人脉网络"}]},{"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":"在数据科学领域，“你的人脉就是你的净资产”这句老话比以往更重要。说到底，这是一个全新的领域，没有多少衡量标准来区分优秀的从业者，除非你雇佣并发现他们，但这就意味着巨大的投资风险。所以当你想要获得第一份数据科学的工作时，找个人来担保是很有用的。"}]},{"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":"与此同时，将自己融入到数据科学文化中，这也是一个很好的方式，可以发展和学习数据科学的新知识，同时也可以让你在找到第一份工作的时候感受到数据科学的魅力。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"总结"}]},{"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":"当你决定寻找一名数据科学家时，我个人认为，你是否有学位并不重要。最终，招聘公司认为他们有一个包括数据科学相关的问题，他们需要一位数据高手帮助他们深入研究这个问题，这样公司才能发展壮大——但是公司并没有说需要获得学位。但是，要想让数据科学家的工作更有效率，你必须具备一些必要的技能，好消息是，现在互联网让你可以在网上学习很多数据科学家所需要具备的技能，而且不必负担债务。"}]},{"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":"作者介绍："}]},{"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，痴迷于数据科学、人工智能和商业技术应用。"}]},{"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":"原文链接："}]},{"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"}]}]}