数据科学家&数据工程师与数据分析师的不同细分职能

与其他一些相关工程职位一样，数据科学家的影响力与互联网同进同退。数据工程师和数据分析师与数据科学家携手共同完成这幅“大数据时代”巨作。他们共同努力拟定数据平台要求，基础和高级算法，提供数据分析和展示所需的可视化工具，并将价值创造以易于理解，富于见解的方式反馈给其他部门。

数据科学家是什么样一个存在呢？

通常情况下，数据科学家有数学或物理方面的高等学位。有博士学位的情况并不少见，硕士学位仅是一个前提条件。数据科学家精通统计建模以及如何构建与定制高级数学算法。这既在他们专业范围内，也是他们所擅长的地方。我听到过有人这样形容一个数据科学家“软件工程技能牛过多数人的酷炫统计学家”。但是，当你问这些数据科学家怎么会从事这个职业时，途径是多种多样的。这是一个相对较新的职位，因而我们也无法根据时间历史来追踪一个数据科学家专业技能是如何进步的。数据科学家和数据工程师的定义有点相互重叠。

除了研究先进算法，数据科学家对AB测试亲力亲为并且拥有博学的多变量测试和实验设计知识。一个专业能力非常强的数据科学家能在模型建成后对其进行改进和改制，而能力鹤立鸡群的数据科学家则能根据你的商业问题定制合适模型。

数据工程师如何定义呢？

数据工程师一般被定义成“深刻理解统计学科的明星软件工程师”。如果你正为一个商业问题烦恼，那么你需要一个数据工程师。这些伙计就是那些能提供可建模数据所需平台的人。他们的核心价值在于他们借由清晰数据创建数据管道的能力。充分了解文件系统，分布式计算与数据库是成为一位优秀数据工程师的必要技能。

数据工程师对演算法有相当好的理解。因此，数据工程师理应能运行基本数据模型。商业需求的高端化催生了演算高度复杂化的需求。很多时候，这些需求超过了数据工程师掌握知识范围，这个时候你就需要打电话寻求数据科学家的帮助。

数据分析师如何理解呢？

数据分析师能洞悉一个方程式的商业意义。他们知道如何提出正确的问题，非常善于数据分析，数据可视化和数据呈现。不管是给另一个数据分析师还是C级执行做演讲，数据分析师都是数据提取，模式识别以及从大量数据中洞察问题方面的能手。

如果你或者你的公司正考虑顺应这股大数据浪潮的发展，你应该从明确你想利用大数据解决所面临的商业问题处下手。接着找出你真正的需求：是数据采集，检索，仓储还是数据分析？然后编写相应的职位描述并做好准备，为了在这场大数据游戏中脱颖而出，你可能需要雇佣不止一个人。

英语原文：

he role of data scientist grew alongside the internet as did a few other related engineering jobs. Data engineers and data analysts work with data scientists to complete the whole “big-data” picture. They work together to determine the data platform requirement, basic and advanced algorithms, and to deliver the visual tools needed to analyze and present the data and value creation back to other departments in an easily understood and insightful format.

Who is a data scientist?

Typically a data scientist has an advanced degree in mathematics or physics. A PhD isn’t uncommon and a Master’s degree is a prerequisite. Data scientists strongly understand statistical modeling and how to build and customize advanced mathematical algorithms. This is both within their comfort zone and where they excel. I heard one person describe a data scientist as “a rock star statistician with above average software engineering skills.” Yet, when you ask data scientists how they came into their profession, the path is varied. This is a relatively new position so we don’t have 20 years of history to track how a data scientist progresses. There is a bit of overlap between a data scientist and a data engineer.

Besides working on advanced algorithms, a data scientist is hands on with AB testing and has advanced knowledge of multivariate testing and the design of experiments. A strong data scientist is able to customize and change the models after they are built and especially capable ones are those who can customize models to your business problem.

Who is a data engineer?

A data engineer is “a rock star software engineer with a decent understanding of statistics.” If you have a business problem, you need a data engineer. These are the folks that provide the platform upon which the data can be modeled. Their core value lies in their ability to prepare the data pipeline with clean data. A strong understanding of file systems, distributed computing and database completes the skill set needed to be a good data engineer.

Data engineers have decent understanding of algorithms. Therefore, data engineers should be able to run basic models. With more advanced business needs comes a requirement for more sophisticated algorithms. Many times these needs outstrip the knowledge of the data engineer and that’s when you need to call them a data scientist.

Who is a data analyst?

Data analysts understand the business side of the equation. They know how to ask the right questions and are really good at data analysis, visualization and the presentation of data. Whether presenting to another data analyst or a c-level executive, data analysts are the super stars of data extraction, pattern recognition and finding the insight within reams of data.

If you or your company is thinking about jumping onto the big data bandwagon, start first by defining the business problem that you want to solve with big data. Then figure out what you need: Is it data capture, retrieval, warehousing or analytics? Then write the job description accordingly and be prepared, you may be looking for more than one person in order to truly play the big data game well.

译者： 猪的浪漫 原作者：Vinay Verma

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