What Makes Someone a Data Scientist?是什么让某人成为数据科学家？

Course Text Book: 'Getting Started with Data Science' Publisher: IBM Press; 1 edition (Dec 13 2015) Print.
课程教材：《数据科学入门》 出版社：IBM 出版社；第 1 版（2015 年 12 月 13 日）印刷版。

Author: Murtaza Haider  作者：Murtaza Haider

Prescribed Reading: Chapter 1 Pg. 12-15
推荐阅读：第一章 第 12-15 页

What Makes Someone a Data Scientist? 是什么让某人成为数据科学家？

Now that you know what is in the book, it is time to put down some definitions. Despite their ubiquitous use, consensus evades the notions of Big data and Data Science. The question, Who is a data scientist? is very much alive and being contested by individuals, some of whom are merely interested in protecting their discipline or academic turfs. In this section, I attempt to address these controversies and explain Why a narrowly construed definition of either Big data or Data science will result in excluding hundreds of thousands of individuals who have recently turned to the emerging field.
现在你已经了解了书中的内容，是时候给出一些定义了。尽管大数据和数据科学被广泛使用，但人们对这些概念尚未达成共识。关于“谁是一名数据科学家？”的问题仍然存在争议，其中一些人只是为了保护他们的学科或学术领域。在本节中，我试图解决这些争议，并解释为什么对大数据或数据科学进行狭隘的定义将导致排除数百万最近投身于这个新兴领域的人。

Everybody loves a data scientist, wrote Simon Rogers (2012) in the Guardian. Mr. Rogers also traced the newfound love for number crunching to a quote by Google's Hal Varian, who declared that the sexy job in the next ten years will be statisticians.
西蒙·罗杰斯（Simon Rogers，2012）在《卫报》上写道：“每个人都喜欢数据科学家。”罗杰斯先生还追溯了对数字计算的新兴趣源于谷歌的哈尔·范里安（Hal Varian）的一句话，范里安宣称未来十年最热门的工作将是统计学家。

Whereas Hal Varian named statisticians sexy, it is widely believed that what he really meant were data scientists. This raises several important questions:
尽管哈尔·范里安称统计学家为性感，但人们普遍认为他真正指的是数据科学家们。这引发了一些重要的问题：

• What is data science?
  什么是数据科学？
• How does it differ from statistics?
  它与统计学有何不同？
• What makes someone a data scientist?
  是什么让某人成为数据科学家？

In the times of big data, a question as simple as, What is data science? can result in many answers. In some cases, the diversity of opinion on these answers borders on hostility.
在大数据时代，一个简单的问题“什么是数据科学？”可能会引发许多不同的答案。在某些情况下，对这些答案的多样性甚至接近于敌对。

I define a data scientist as someone who finds solutions to problems by analyzing Big or small data using appropriate tools and then tells stories to communicate her findings to the relevant stakeholders. I do not use the data size as a restrictive clause. A data below a certain arbitrary threshold does not make one less of a data scientist. Nor is my definition of a data scientist restricted to particular analytic tools, such as machine learning. As long as one has a curious mind, fluency in analytics, and the ability to communicate the findings, I consider the person a data scientist.
我将数据科学家定义为通过使用适当的工具分析大数据或小数据来解决问题，并讲述故事以向相关利益相关者传达其发现的人。我不以数据规模作为限制条件。低于某个任意阈值的数据并不会让人失去数据科学家的资格。我的数据科学家定义也不局限于特定的分析工具，例如机器学习。只要一个人拥有好奇心、精通分析能力，并能够传达其发现，我就认为这个人是一名数据科学家。

I define data science as something that data scientists do. Years ago, as an engineering student at the University of Toronto, I was stuck With the question: What is engineering? I wrote my master's thesis on forecasting housing prices and my doctoral dissertation on forecasting homebuilders' choices related to What they build, when they build, and where they build new housing. In the civil engineering department, Others were working on designing buildings, bridges, tunnels, and worrying about the stability of slopes. My work, and that of my supervisor, was not your traditional garden-variety engineering. Obviously, I was repeatedly asked by others whether my research was indeed engineering.
我定义数据科学为数据科学家所做的事情。多年前，在多伦多大学学习工程学时，我曾陷入这样的问题：什么是工程学？我的硕士论文是关于预测房价，博士论文则是关于预测建筑商在建造什么、何时建造以及在哪里建造新住房的选择。在土木工程系，其他人正在从事建筑设计、桥梁、隧道等工作，并关注边坡的稳定性。我的工作以及我的导师的工作，并非传统的工程学。显然，我不断被他人问及我的研究是否真的是工程学。

When I shared these concerns with my doctoral supervisor, Professor Eric Miller, he had a laugh. Dr Miller spent a lifetime researching urban land use and transportation and had earlier earned a doctorate from MIT. “Engineering is what engineers do,” he responded. Over the next 17 years, I realized the wisdom in his statement. You first become an engineer by obtaining a degree and then registering with the local professional body that regulates the engineering profession. Now you are an engineer. You can dig tunnels; write software codes; design components of an iPhone or a supersonic jet. You are an engineer. And when you are leading the global response to a financial crisis in your role as the chief economist of the International Monetary Fund (IMF), as Dr Raghuram Rajan did, you are an engineer.
当我将这些担忧与我的博士导师，米勒教授分享时，他笑了。米勒博士毕生研究城市土地利用和交通，早先在麻省理工学院获得博士学位。他回应道：“工程就是工程师所做的事情。”在接下来的 17 年里，我逐渐领悟了他这句话的智慧。首先，通过获得学位并在当地负责监管工程行业的专业机构注册，你才能成为一名工程师。现在你就是一名工程师了。你可以挖掘隧道；编写软件代码；设计 iPhone 或超音速飞机的部件。你就是一名工程师。而且，当你作为国际货币基金组织（IMF）的首席经济学家，领导全球应对金融危机时，就像拉贾鲁姆·拉詹博士所做的那样，你依然是一名工程师。

Professor Raghuram Rajan did his first degree in electrical engineering from the Indian Institute of Technology. He pursued economics in graduate studies, later became a professor at a prestigious university, and eventually landed at the IMF. He is currently serving as the 23rd Governor of the Reserve Bank of India. Could someone argue that his intellectual prowess is rooted only in his training as an economist and that the fundamentals he learned as an engineering student played no role in developing his problem-solving abilities?
拉贾鲁姆·拉詹教授首先在印度理工学院获得电气工程学士学位，随后在研究生阶段攻读经济学，后来成为一所著名大学的教授，最终进入国际货币基金组织。他目前担任印度储备银行第 23 任行长。有人能否认他的智力才干仅源于其经济学训练，而他在作为工程学生时所学的基础知识在培养其解决问题的能力方面没有起到任何作用？

Professor Rajan is an engineer. So are Xi Jinping, the President of the People's Republic of China, and Alexis Tsipras, the Greek Prime Minister who is forcing the world to rethink the fundamentals of global economics. They might not be designing new circuitry, distillation equipment, or bridges, but they are helping build better societies and economies and there can be no better definition of engineering and engineers—that is, individuals dedicated to building better economies and societies.
拉贾鲁姆·拉詹教授是一位工程师。中国国家主席习近平和希腊总理阿莱克西斯·齐普拉斯也是工程师，后者正在迫使世界重新思考全球经济的基础。他们可能不会设计新的电路、蒸馏设备或桥梁，但他们正在帮助建设更好的社会和经济，而这正是工程和工程师的最好定义——即致力于建设更好经济和社会的个人。

So briefly, I would argue that data science is what data scientists do.
简而言之，我认为数据科学就是数据科学家所做的事情。

Others have many different definitions. In September 2015, a co-panelist at a meetup organized by BigDataUniversity.com in Toronto confined data science to machine learning. There you have it. If you are not using the black boxes that makeup machine learning, as per some experts in the field, you are not a data scientist. Even if you were to discover the cure to a disease threatening the lives of millions, turf-protecting colleagues will exclude you from the data science club.
其他人有许多不同的定义。2015 年 9 月，在多伦多由 BigDataUniversity.com 组织的一个会议上，一位共同讨论者将数据科学局限于机器学习。就是这样。如果你不使用构成机器学习的黑箱，按照该领域的一些专家的说法，你就不是数据科学家。即使你发现了威胁数百万人生存的疾病的治疗方法，保护地盘的同事也会将你排除在数据科学俱乐部之外。

Dr Vincent Granville (2014), an author on data science, offers certain thresholds to meet to be a data scientist. On pages 8 and 9 in Developing Analytic talent, Dr Granville describes the new data science professor as a non-tenured instructor at a non-traditional university, who publishes research results in online blogs, does not waste time writing grants, works from home, and earns more money than the traditional tenured professors. Suffice it to say that the thriving academic community of data scientists might disagree with Dr Granville.
数据科学作家文森特·格兰维尔博士（2014 年）提出了一些需要达到的门槛才能成为数据科学家。在《Developing Analytic Talent》第 8 页和第 9 页，格兰维尔博士描述了新的数据科学教授是一位非传统大学的非终身制讲师，他在在线博客上发布研究成果，不浪费时间写资助申请，在家工作，并且比传统的终身制教授赚更多钱。话说回来，繁荣的数据科学家学术社区可能会不同意格兰维尔博士的观点。

Dr Granville uses restrictions on data size and methods to define what data science is. He defines a data scientist as one who can easily process a So-million-row data set in a couple of hours, and who distrusts (statistical) models. He distinguishes data science from statistics. Yet he lists algebra, calculus, and training in probability and statistics as necessary background to understand data science (page 4).
格兰维尔博士通过数据规模和方法来界定数据科学。他将数据科学家定义为能够在几小时内轻松处理一个包含数百万行数据的数据集的人，并且不信任（统计）模型。他将数据科学与统计学区分开来。然而，他列出了代数、微积分以及概率和统计方面的培训作为理解数据科学的必要背景（第 4 页）。

Some believe that big data is merely about crossing a certain threshold on data size or the number of observations, or is about the use of a particular tool, such as Hadoop. Such arbitrary thresholds on data size are problematic because, with innovation, even regular computers and off-the-shelf software have begun to manipulate very large data sets. Stata, a commonly used software by data scientists and statisticians, announced that one could now process between 2 billion to 24.4 billion rows using its desktop solutions. If Hadoop is the password to the big data club, Stata's ability to process 24.4 billion rows, under certain limitations, has just gatecrashed that big data party.
有些人认为大数据仅仅是跨越某个数据规模或观察数量的阈值，或者是指使用特定的工具，如 Hadoop。这种关于数据规模的任意阈值是有问题的，因为随着创新的发展，即使是普通计算机和现成的软件也开始能够处理非常大的数据集。Stata 是数据科学家和统计学家常用的软件，宣布现在可以使用其桌面解决方案处理 20 亿到 244 亿行数据。如果 Hadoop 是大数据俱乐部的密码，那么 Stata 在特定限制下能够处理 244 亿行数据，已经闯入了这个大数据派对。

It is important to realize that one who tries to set arbitrary thresholds to exclude others is likely to run into inconsistencies. The goal should be to define data science in a more exclusive, discipline- and platform-independent, size-free context where data-centric problem solving and the ability to weave strong narratives take center stage.
认识到试图设定任意标准以排除他人的人很可能遇到不一致性是很重要的。目标应该是在一个更排他性、独立于学科和平台、不受规模限制的背景下定义数据科学，使以数据为中心的问题解决和编织强有力的叙事能力成为焦点。

Given the controversy, I would rather consult others to see how they describe a data scientist. Why don't we again consult the Chief Data Scientist of the United States? Recall Dr Patil told the Guardian newspaper in 2012 that a data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data. What is admirable about Dr Patil's definition is that it is inclusive of individuals of various academic backgrounds and training, and does not restrict the definition of a data scientist to a particular tool or subject it to a certain arbitrary minimum threshold of data size.
鉴于争议，我宁愿咨询他人，看看他们如何描述数据科学家。我们为什么不再次咨询美国首席数据科学家呢？回想一下，帕蒂尔博士在 2012 年告诉《卫报》记者，数据科学家是那种独特的技能组合，既能解锁数据的洞察力，又能通过数据讲述精彩的故事。帕蒂尔博士定义的可贵之处在于，它包含了具有不同学术背景和培训的个体，并且没有将数据科学家的定义限制于特定工具，也没有将其置于一定的任意最小数据规模标准之下。

The other key ingredient for a successful data scientist is a behavioral trait: curiosity. A data scientist has to be one with a very curious mind, willing to spend significant time and effort to explore her hunches. In journalism, the editors call it having the nose for news. Not all reporters know where the news lies. Only those Who have the nose for news get the Story. Curiosity is equally important for data scientists as it is for journalists.
成功的另一位关键要素是一种行为特质：好奇心。数据科学家必须具备非常好奇的头脑，愿意投入大量时间和精力去探索自己的直觉。在新闻业中，编辑们称之为具有新闻嗅觉。并非所有记者都知道新闻在哪里。只有那些具有新闻嗅觉的人才能获得故事。好奇心对数据科学家来说与对记者来说同样重要。

Rachel Schutt is the Chief Data Scientist at News Corp. She teaches a data science course at Columbia University. She is also the author of an excellent book, Doing Data Science. In an interview With the New York Times, Dr Schutt defined a data scientist as someone who is a part computer scientist, part software engineer, and part statistician (Miller, 2013). But that's the definition of an average data scientist. "The best", she contended, "tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them."
Rachel Schutt 是新闻集团的首席数据科学家。她在哥伦比亚大学教授数据科学课程。她也是一本优秀书籍《Doing Data Science》的作者。在《纽约时报》的采访中，Schutt 博士将数据科学家定义为计算机科学家、软件工程师和统计学家三者的结合体（Miller，2013）。但这只是普通数据科学家的定义。"最好的"，她主张，"往往是非常好奇的人，善于提出好问题，并且能够处理非结构化情况，试图在其中寻找结构。"