数据分析师 & 数据科学家 & 数据工程师——哪个角色最适合你

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       数据分析师 & 数据科学家 & 数据工程师——

                        哪个角色最适合你

              What's the difference between

        a data analyst, scientist and engineer?

数据越来越多的影响并塑造着那些我们每天都要交互的系统。不管是你使用Siri，google搜索，还是浏览facebook的好友动态，你都在消费者数据分析的结果。我们赋予了数据如此大的转变的能力，也难怪近几年越来越多的数据相关的角色被创造出来。

Data is increasingly shaping the systems that we interact with every day. Whether you’re using Siri, searching Google, or browsing your Facebook feed, you’re consuming the results of data analysis. Given its transformational ability, it’s no wonder that so many data-related roles have been created in the past few years.

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点击进入『21世纪数据分析精英聚集地』

点击进入『21世纪大数据高薪就业领地』

这些角色的职责范围，从预测未来，到发现你周围世界的模式，到建设操作着数百万记录的系统。在这篇文章中。我们将讨论不同的数据相关的角色，他们如何组合在一起，并且帮你找出那些角色是适合你自己的。

The responsibilities of these roles range from predicting the future, to finding patterns in the world around you, to building systems that manipulate millions of records. In this post, we’ll talk about the various data-related roles, how they fit together, and help you figure out which role is the right fit.

什么是数据分析师？

What is a data analyst?

数据分析通过谈论数据来像他们的公司传递价值，用数据来回答问题，交流结果来帮助做商业决策。数据分析师的一般工作包括数据清洗，执行分析和数据可视化。

Data Analysts deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions. Common tasks done by data analysts include data cleaning, performing analysis and creating data visualizations.

取决于行业，数据分析师可能有不同的头衔（比如：商业分析师，商业智能分析师，业务/运营分析师，数据分析师）不管头衔是什么，数据分析师是一个能适应不同角色和团队的多面手以帮助别人做出更好的数据驱动的决策。

Depending on the industry, the data analyst could go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst). Regardless of title, the data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions.

深度解析数据分析师

The data analyst in depth

数据分析师拥有把传统的商业方式转换成数据驱动的商业方式的潜质。虽然数据分析师是数据广泛领域的入门水平，但不是说所有的分析师都是低水平的。数据分析师不仅仅精通技术工具，还是高效的交流者，他们对于那些把技术团队和商业团队隔离的公司是至关重要的。

The data analyst has the potential to turn a traditional business into a data-driven one. While often data analyst positions are ‘entry level’ jobs in the wider field of data, not all analysts are junior level. As effective communicators with a mastery over technical tools, data analysts are critical for companies that have segregated technical and business teams.

他们的核心职责是帮助其他人追踪进展，和优化目标。市场人员如何使用分析的数据取帮助他们安排下一次活动？销售人员如何衡量哪种类型人群能更好的争取？CEO如何更好的理解最最近公司发展背后潜在原因？这些问题就需要数据分析师通过数据分析和呈现结果来给答案。他们从事的这些和数据打交道的复杂工作能够为他们所在的组织贡献价值。

Their core responsibility is to help others track progress and optimize their focus. How can a marketer use analytics data to help launch their next campaign? How can a sales representative better identify which demographics to target? How can a CEO better understand the underlying reasons behind recent company growth? These are all questions that the data analyst provides the answer to by performing analysis and presenting the results. They undertake the complex jobof working with data to deliver value to their organization.

一个高效的数据分析师能够在商业决策的时候摒弃臆想和猜测，并且帮助整个组织快速成长。数据分析师必须是一个横跨在不同团队中的有效桥梁。通过分析新的数据，综合不同的报告，翻译整体的产出。反过来，这也能帮助组织对于自身的发展时刻保持警觉。

An effective data analyst will take the guesswork out of business decisions and help the entire organization thrive. The data analyst must be an effective bridge between different teams by analyzing new data, combining different reports, and translating the outcomes. In turn, this is what allows the organization to maintain an accurate pulse check on its growth.

公司的不同需求决定了数据分析师的技能要求，但是下面这些应该是通用的：

The nature of the skills required will depend on the company’s specific needs, but these are some common tasks:

清洗和组织未加工的数据

使用描述性统计来得到数据的全局视图

分析在数据中发现的有趣趋势

创建数据可视化和仪表盘来帮助公司解读说明和使用数据做决策

呈现针对商业客户或者内部团队的科学分析的结果

Cleaning and organizing raw data.

Using descriptive statistics to get a big-picture view of their data.

Analyzing interesting trends found in the data.

Creating visualizations and dashboards to help the company interpret and make decisions with the data.

Presenting the results of a technical analysis to business clients or internal teams.

数据分析师对公司科技和分科技的两面都带来了重大的价值。不管是进行探索性的分析还是解读经营状况的仪表盘。分析师都促进了团队之间更紧密的连接。

The data analyst brings significant value to both the technical and non-technical sides of an organization. Whether running exploratory analyses or explaining executive dashboards, the analyst fosters greater connection between teams.

什么是数据科学家？

What is a data scientist?

数据科学家是使用他们在统计学和建设机器学习模型方面的专业技术去进行关键商业问题预测的专家。

A data scientist is a specialist that applies their expertise in statistics and building machine learning models to make predictions and answer key business questions.

数据科学家也需要像数据分析师一样去清洗、分析、可视化数据。然而一个数据科学家需要在这些技能上更深入也更专业，他们还可以去训练和优化机器学习的模型。

A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models.

深度解析数据科学家

The data scientist in depth

数据科学家能产生巨大的价值，他们处理更多开放式的问题并且利用他们专业的统计学和算法知识发挥更大杠杆的作用。如果说数据分析师专注于从过去和现在数据层面来理解数据的话，那么数据科学家就是专注于做出对未来更可信的预测。

The data scientist is an individual that can provide immense value by tackling more open-ended questions and leveraging their knowledge of advanced statistics and algorithms. If the analyst focuses on understanding data from the past and present perspectives, then the scientist focuses on producing reliable predictions for the future.

数据科学家通过有监督学习（分类、回归）和无监督学习（聚类，神经网络，异常监测？）机器学习模型来揭开隐藏着的规律。本质上来说他们是训练那些能让他们更好的识别模型和产出精确预测效果的数学模型的人。

The data scientist will uncover hidden insights by leveraging both supervised (e.g. classification, regression) and unsupervised learning (e.g. clustering, neural networks, anomaly detection) methods toward their machine learning models. They are essentially training mathematical models that will allow them to better identify patterns and derive accurate predictions.

下面是数据科学家完成的一些例子：

The following are examples of work performed by data scientists:

评估统计学模型来决定分析有效性

使用机器学习来建设更好的预测算法

测试和持续提升模型精确度

进行数据可视化来概括分析的结论

Evaluating statistical models to determine the validity of analyses.

Using machine learning to build better predictive algorithms.

Testing and continuously improving the accuracy of machine learning models.

Building data visualizations to summarize the conclusion of an advanced analysis.

数据科学家为预测和理解数据带来了一种完全崭新的方式。虽然数据分析师可能也可以去描述趋势和为商业团队传递这些结果。但是数据科学家能剔除新的问题并且可以去建模来做出对新数据的预测。

Data scientists bring an entirely new approach and perspective to understanding data. While an analyst may be able to describe trends and translate those results into business terms, the scientist will raise new questions and be able to build models to make predictions based on new data.

什么是数据工程师？

What is a data engineer?

数据工程师建设和优化系统。这些系统帮助数据科学家和数据分析师开展他们的工作。每一个公司里面和数据打交道的人都需要依赖于这些数据是准确的和可获取的。数据工程师保证任何数据都是正常可接收的，可转换的，可存储的并且对于使用者来说是可获取的。

Data engineers build and optimize the systems that allow data scientists and analysts to perform their work. Every company depends on its data to be accurate and accessible to individuals who need to work with it. The data engineer ensures that any data is properly received, transformed, stored, and made acessible to other users.

深度解析数据工程师

The data engineer in depth

数据工程师建立了数据分析师和数据科学家依赖的基础。数据工程师对构造数据管道并且经常需要去使用复杂的工具和技术来管理数据负责。不想前面说的两个事业的路径，数据工程师更多的是朝着软件开发能力上学习和提升。

The data engineer establishes the foundation that the data analysts and scientists build upon. Data engineers are responsible for constructing data pipelines and often have to use complex tools and techniques to handle data at scale. Unlike the previous two career paths, data engineering leans a lot more towards a software development skillset.

在比较大的组织中，数据工程师需要关注不同的方面：比如使用数据的工具，维护数据库，创建和管理数据管道。不管侧重于什么，一个好的数据工程师能够保证数据科学家和数据分析师专注于解决分析方面的问题，而不是一个数据源一个数据源的去移动、操作数据。

At larger organizations, data engineers can have different focuses such as leveraging data tools, maintaining databases, and creating and managing data pipelines. Whatever the focus may be, a good data engineer allows a data scientist or analyst to focus on solving analytical problems, rather than having to move data from source to source.

数据工程师往往更加注重建设和优化。下面的任务的示例是数据工程师通常的工作：

The data engineer’s mindset is often more focused on building and optimization. The following are examples of tasks that a data engineer might be working on:

为数据消费开发API

在现存的数据管道中整合数据集

在新数据上运用特征转换提供给机器学习模型

持续不断的监控和测试系统保证性能优化

Building APIs for data consumption.

Integrating external or new datasets into existing data pipelines.

Applying feature transformations for machine learning models on new data.

Continuously monitoring and testing the system to ensure optimized performance.

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关键词：数据分析师 数据科学家 数据工程师

【补充】

你的数据驱动的事业路径：

Your Data-Driven Career Path

现在你已经了解了这三种数据驱动的工作了，但是问题还在，你适合哪一种呢？虽然都是和数据相关，但是这三种工作是截然不同的。

Now that we’ve explored these three data-driven careers, the question remains - where do you fit in? The key is to understand that these are three fundamentally different ways to work with data.

数据工程师主要工作在后端。持续的提升数据管道来保证数据的精确和可获取。他们一般利用不同的工具来保证数据被正确的处理了，并且当用户要使用数据的时候保证数据是可用的。一个好的的数据工程师会为组织节省很多的时间和精力。

The data engineer is working on the “back-end,” continuously improving data pipelines to ensure that the data the organisation relies upon is accurate and available. They will leverage all sorts of different tools to ensure the data is processed correctly and that the data is available to the user when they need it. A good data engineer saves a lot of time and effort for the rest of the organization.

数据分析师一般用数据工程师提供的现成的接口来抽取新的数据，然后取发现数据中的趋势。同时也要分析异常情况。数据分析师以一种清晰的方式来概括和提出他们的结果来让非技术的团队更好的理解他们现在在做的东西。

The data analyst may then extract a new dataset using the custom API that the engineer built and begin identifying interesting trends in that data, as well as running analyses on these anomalies. The analyst will summarize and present their results in a clear way that allows their non-technical teams to better understand where they are and how they’re doing.

最后，数据科学家更倾向于基于分析的发现和在更多可能性上的调查来获得方向。不管是训练模型还是进行统计分析，数据科学家试图去对未来要发生的可能性提出一个更好的预测。

Finally, the data scientist will likely build upon the analyst’s initial findings and research into even more possibilities to derive insights from. Whether by training machine learning models or by running advanced statistical analyses, the data scientist is going to provide a brand new perspective into what may be possible for the near future.

不管你的特殊的路径是什么，好奇心都是这三个职业最本质的要求。使用数据来更好的提问和进行精确的实验是数据驱动事业的全部目标。此外，数据科学家领域是不断的进化的，你必须要有强大的能力去持续不断的学习。

Regardless of your specific path, curiosity is a natural pre-requisite of all three of these careers. The ability to use data to ask better questions and run more precise experiments is the entire purpose of a data-driven career. Furthermore, the data science field is constantly evolving and thus, there is a great need to continuously learn more.

所以，祝愿所有现在的和未来的数据分析师、数据科学家和数据工程师-愿你们好远，并且持续不断的学习！

So, to all the current and future data analysts, scientists, and engineers out there – good luck and keep learning!

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