What's the difference between a data analyst, scientist and engineer?

數據越來越多的影響并塑造著那些我們每天都要交互的系統。不管是你使用Siri，google搜索，還是瀏覽facebook的好友動態，你都在消費者數據分析的結果。我們賦予了數據如此大的轉變的能力，也難怪近幾年越來越多的數據相關的角色被創造出來。

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.

這些角色的職責范圍，從預測未來，到發現你周圍世界的模式，到建設操作著數百萬記錄的系統。在這篇文章中。我們將討論不同的數據相關的角色，他們如何組合在一起，并且幫你找出那些角色是適合你自己的。

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.

什么是數據分析師？

What is a data analyst?

數據分析通過談論數據來像他們的公司傳遞價值，用數據來回答問題，交流結果來幫助做商業決策。數據分析師的一般工作包括數據清洗，執行分析和數據可視化。

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.

取決于行業，數據分析師可能有不同的頭銜（比如：商業分析師，商業智能分析師，業務/運營分析師，數據分析師）不管頭銜是什么，數據分析師是一個能適應不同角色和團隊的多面手以幫助別人做出更好的數據驅動的決策。

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.

深度解析數據分析師

The data analyst in depth

數據分析師擁有把傳統的商業方式轉換成數據驅動的商業方式的潛質。雖然數據分析師是數據廣泛領域的入門水平，但不是說所有的分析師都是低水平的。數據分析師不僅僅精通技術工具，還是高效的交流者，他們對于那些把技術團隊和商業團隊隔離的公司是至關重要的。

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.

他們的核心職責是幫助其他人追蹤進展，和優化目標。市場人員如何使用分析的數據取幫助他們安排下一次活動？銷售人員如何衡量哪種類型人群能更好的爭??？CEO如何更好的理解最最近公司發展背后潛在原因？這些問題就需要數據分析師通過數據分析和呈現結果來給答案。他們從事的這些和數據打交道的復雜工作能夠為他們所在的組織貢獻價值。

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.

一個高效的數據分析師能夠在商業決策的時候摒棄臆想和猜測，并且幫助整個組織快速成長。數據分析師必須是一個橫跨在不同團隊中的有效橋梁。通過分析新的數據，綜合不同的報告，翻譯整體的產出。反過來，這也能幫助組織對于自身的發展時刻保持警覺。

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.

公司的不同需求決定了數據分析師的技能要求，但是下面這些應該是通用的：

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

清洗和組織未加工的數據

使用描述性統計來得到數據的全局視圖

分析在數據中發現的有趣趨勢

創建數據可視化和儀表盤來幫助公司解讀說明和使用數據做決策

呈現針對商業客戶或者內部團隊的科學分析的結果

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.

數據分析師對公司科技和分科技的兩面都帶來了重大的價值。不管是進行探索性的分析還是解讀經營狀況的儀表盤。分析師都促進了團隊之間更緊密的連接。

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.

什么是數據科學家？

What is a data scientist?

數據科學家是使用他們在統計學和建設機器學習模型方面的專業技術去進行關鍵商業問題預測的專家。

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.

數據科學家也需要像數據分析師一樣去清洗、分析、可視化數據。然而一個數據科學家需要在這些技能上更深入也更專業，他們還可以去訓練和優化機器學習的模型。

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.

深度解析數據科學家

The data scientist in depth

數據科學家能產生巨大的價值，他們處理更多開放式的問題并且利用他們專業的統計學和算法知識發揮更大杠桿的作用。如果說數據分析師專注于從過去和現在數據層面來理解數據的話，那么數據科學家就是專注于做出對未來更可信的預測。

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.

數據科學家通過有監督學習（分類、回歸）和無監督學習（聚類，神經網絡，異常監測？）機器學習模型來揭開隱藏著的規律。本質上來說他們是訓練那些能讓他們更好的識別模型和產出精確預測效果的數學模型的人。

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.

下面是數據科學家完成的一些例子：

The following are examples of work performed by data scientists:

評估統計學模型來決定分析有效性

使用機器學習來建設更好的預測算法

測試和持續提升模型精確度

進行數據可視化來概括分析的結論

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.

數據科學家為預測和理解數據帶來了一種完全嶄新的方式。雖然數據分析師可能也可以去描述趨勢和為商業團隊傳遞這些結果。但是數據科學家能剔除新的問題并且可以去建模來做出對新數據的預測。

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.

什么是數據工程師？

What is a data engineer?

數據工程師建設和優化系統。這些系統幫助數據科學家和數據分析師開展他們的工作。每一個公司里面和數據打交道的人都需要依賴于這些數據是準確的和可獲取的。數據工程師保證任何數據都是正?？山邮盏?，可轉換的，可存儲的并且對于使用者來說是可獲取的。

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.

深度解析數據工程師

The data engineer in depth

數據工程師建立了數據分析師和數據科學家依賴的基礎。數據工程師對構造數據管道并且經常需要去使用復雜的工具和技術來管理數據負責。不想前面說的兩個事業的路徑，數據工程師更多的是朝著軟件開發能力上學習和提升。

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.

在比較大的組織中，數據工程師需要關注不同的方面：比如使用數據的工具，維護數據庫，創建和管理數據管道。不管側重于什么，一個好的數據工程師能夠保證數據科學家和數據分析師專注于解決分析方面的問題，而不是一個數據源一個數據源的去移動、操作數據。

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.

數據工程師往往更加注重建設和優化。下面的任務的示例是數據工程師通常的工作：

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:

為數據消費開發API

在現存的數據管道中整合數據集

在新數據上運用特征轉換提供給機器學習模型

持續不斷的監控和測試系統保證性能優化

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.

你的數據驅動的事業路徑：

Your Data-Driven Career Path

現在你已經了解了這三種數據驅動的工作了，但是問題還在，你適合哪一種呢？雖然都是和數據相關，但是這三種工作是截然不同的。

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.

數據工程師主要工作在后端。持續的提升數據管道來保證數據的精確和可獲取。他們一般利用不同的工具來保證數據被正確的處理了，并且當用戶要使用數據的時候保證數據是可用的。一個好的的數據工程師會為組織節省很多的時間和精力。

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.

數據分析師一般用數據工程師提供的現成的接口來抽取新的數據，然后取發現數據中的趨勢。同時也要分析異常情況。數據分析師以一種清晰的方式來概括和提出他們的結果來讓非技術的團隊更好的理解他們現在在做的東西。

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，數據科學家更傾向于基于分析的發現和在更多可能性上的調查來獲得方向。不管是訓練模型還是進行統計分析，數據科學家試圖去對未來要發生的可能性提出一個更好的預測。

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.

不管你的特殊的路徑是什么，好奇心都是這三個職業最本質的要求。使用數據來更好的提問和進行精確的實驗是數據驅動事業的全部目標。此外，數據科學家領域是不斷的進化的，你必須要有強大的能力去持續不斷的學習。

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，祝愿所有現在的和未來的數據分析師、數據科學家和數據工程師-愿你們好遠，并且持續不斷的學習！

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