Although we find ourselves in unprecedented times of uncertainty, current events have shown just how valuable the fields of Data Science and Computer Science truly are. Technologies — like the Johns Hopkins dashboard, contact tracing, and data analytics — compose the “virtual front lines” of our attack on the pandemic and continuously prove to be driving sources of change. However one question still remains: Exactly how valuable are these fields?

一個 lthough我們以前所未有的不確定的時候發現自己，目前的事件表明，多么的有價值數據科學和計算機科學領域確實是。 諸如Johns Hopkins儀表板 ， 聯系人跟蹤和數據分析之類的技術構成了我們對大流行的攻擊的“虛擬前線”，并不斷被證明是推動變化的源頭。 然而，仍然存在一個問題：這些領域究竟有多有價值 ？

In this article we will take a snapshot of where Data Science is in 2020 and take a deep dive into where salaries and other forms of compensation stand.

在本文中，我們將簡要介紹2020年數據科學的現狀，并深入探討薪資和其他形式的薪酬水平。

數據科學家的薪水是多少？ (What is the Salary of a Data Scientist?)

To answer this, let’s move on over and check out Payscale: a salary insights platform. Taking a look, we can see that it reports that the current median salary for a Data Scientist is $95,973 with a range spanning from 66k to 134k.

為了回答這個問題，讓我們繼續并檢查Payscale ：一個薪資洞察平臺。 看一下，我們可以看到它報告說，數據科學家的當前中位數工資為95,973美元 ，范圍從66k到134k。

However, that’s not the whole picture. If we look closely, we also see that Payscale reports an average bonus of $9000 along with $976 — $25k in profit sharing and $1k — $10k in commission.

但是，這還不是全部。 如果我們仔細觀察，我們還可以看到Payscale報告的平均獎金 為9000美元，以及976美元( 2.5 萬美元的利潤分成和1000美元-1萬美元的傭金)。

體驗如何影響薪酬？ (How Does Experience Affect Pay?)

As you’d expect, the amount of experience does have a direct correlation to pay. Take a look at this graph for a quick breakdown!

正如您所期望的那樣，經驗的數量確實有直接的相關性。 快看一下這張圖吧！

The key takeaway can be deduced from the binned years of experience. What pops out is the fact that the median salary can be met fairly quickly: according to this graph, within the first five years, you can be making more than the median salary of a Data Scientist!

關鍵要點可以從多年的經驗推論得出。 突然出現的事實是，工資中位數可以很快達到：根據此圖，在頭五年內，您可以獲得的收入將超過數據科學家的工資中位數！

位置呢？ (What About Location?)

Moving on, let’s take a look at how location affects salary:

繼續，讓我們看一下地理位置如何影響薪資：

As expected, silicon valley is once again raking in the big bucks. However, I wouldn’t jump to conclusions too quick; these salaries are highly correlated to the cost of living within the region.

不出所料，硅谷再次賺了大錢。 但是，我不會太快得出結論。 這些工資與該地區的生活成本高度相關。

For example, even though working in San Francisco nets a~30 percent salary increase, you could still be making less than someone working in Atlanta after accounting for CoL (Cost of Living). Let’s look a bit deeper into this: using this CoL calculator, we can see just how big of a discrepancy there is between San Francisco and Atlanta:

例如，即使在舊金山工作凈增加了30％的薪水，但考慮到CoL(生活費用)后，您的收入仍然可能比在亞特蘭大工作的人要少。 讓我們更深入地了解一下：使用此CoL計算器 ，我們可以看到舊金山和亞特蘭大之間的差異有多大：

Atlanta is significantly cheaper when it comes to every department! Just to show you how significant this is, let’s take a look at the average national rent: $1,468. This comes out to the average monthly rent being $1380 in Atlanta and a whopping $3392 in San Francisco. In all, this nets an annual difference of $24,144! This still does not account for other fees like utilities and groceries; both of which vary significantly between the two cities. So before you take that six figure salary, make sure it actually is a six figure salary!

每個部門的亞特蘭大便宜得多！ 為了說明這有多重要，讓我們看一下平均租金： $ 1,468 。 這樣算來 ， 亞特蘭大的平均月租金為1380 美元 ， 舊金山的平均月租金為3392美元 。 總共，每年的凈差額為24,144美元！ 這仍然不包括其他費用，例如水電費和雜貨； 兩者在兩個城市之間差異很大。 因此，在您獲得六位數的薪水之前，請確保它實際上 是六位數的薪水！

與相關薪資比較 (Comparing to Related Salaries)

So now that we have a good understanding of how much a Data Scientist makes, how does it stack up to other Computer Science professions?

因此，現在我們對數據科學家的收入有了很好的了解，它如何與其他計算機科學專業相結合？

This list figure gives us a good snapshot of the ranges similar career salaries fall into. The most surprising thing to me personally is that the median salary for a Data Scientist is over $10,000 more than a Senior Software Engineer! Likewise, the median salary of a Data Scientist is significantly greater than every profession listed — crazy right?!

這個清單數字使我們可以很好地了解類似的職業薪資范圍。 對我個人而言，最令人驚訝的是，數據科學家的薪水比高級軟件工程師的薪水高出10,000美元！ 同樣，數據科學家的中位數工資顯著高于列出的每個職業-瘋狂吧？

For a more in depth look into the salaries of different positions, let’s dive into the Stack Overflow Coding Salary Calculator. As stated within the page, the calculator “is based on the comprehensive data from the Stack Overflow Developer Survey, and this large, extensive survey data allows us to build an accurate model that reflects trends in how coding work is being compensated around the world.”

要更深入地了解不同職位的薪水，讓我們深入研究Stack Overflow Coding Salary Calculator 。 如該頁面中所述，“計算器”基于來自Stack Overflow開發人員調查的綜合數據，而這一龐大而又廣泛的調查數據使我們能夠建立一個準確的模型，以反映世界各地如何補償編碼工作的趨勢。 ”

The thing that pops out immediately is the placement of Data Engineer and Data Scientist as the top three paying developer roles in every country the data was collected from. When looking at the countries, you can notice how much different roles and their compensation vary depending on region; for example, take a look at the ranking of QA or test developers amongst the different countries. The fact that the data roles are so consistently ranked is an amazing sign. It shows that all countries require and value data scientists to the same extent!

突然出現的事情是，數據工程師和數據科學家在每個收集數據的國家/地區中排名前三位，都是付費開發人員。 在查看國家/地區時，您會注意到不同的角色及其薪酬因地區而異。 例如，請查看不同國家/地區的質量檢查或測試開發人員的排名。 數據角色如此穩定地排名的事實是一個了不起的跡象。 它表明，所有國家都在同等程度上要求和重視數據科學家！

However, there’s a catch. Before you go off and think that the best way to make money is becoming a data scientist check this excerpt the stack overflow researcher made in the calculator:

但是，有一個陷阱。 在您開始思考并認為賺錢的最佳方法是成為數據科學家之前，請檢查以下摘錄，這是計算器產生的堆棧溢出研究人員：

[W]e have evidence here that high salaries for data scientists and data engineers can be accounted for by high education and high experience levels alone. Data scientists are highly paid, but not more so than a similarly educated developer doing other kinds of work. (Both bachelor’s degrees and even higher degrees are associated with significantly increased pay for people who code.) Over the past several years, data science and data engineering work have been moving away from an extreme outlier position into the mainstream of software work.

[這里]有證據表明，僅通過高學歷和高經驗水平就能為數據科學家和數據工程師帶來高薪。 數據科學家的薪水很高，但比從事其他工作的受過類似教育的開發人員的薪水更高。 (學士學位甚至更高的學位都與編碼人員的薪水顯著增加有關。)在過去的幾年中，數據科學和數據工程工作已經從極端的局面轉移到軟件工作的主流。

In short, although data engineers and data scientists do make the most money, a major factory that plays into the salaries is higher education and experience. As noted above, the individuals who tend to pick up data roles are much more likely to have degrees and years of experience.

簡而言之，盡管數據工程師和數據科學家確實賺錢最多，但發揮薪資的主要工廠是高等教育和經驗。 如上所述，傾向于擔任數據角色的個人更有可能擁有學位和多年的經驗。

結論 (Conclusion)

So, how much is a data scientist worth in 2020? Well, if you want a straight answer ~$100,000 on average.

那么，2020年數據科學家的身價是多少？ 好吧，如果您想直接回答，平均費用為$ 100,000。

BUT before you go off and start applying to data scientist roles chasing money, let me highlight something. As stated in the developer calculator above, data scientists do not make significantly more than a similarly educated developer.

但是，在您開始申請數據科學家職位之前，讓我重點介紹一下。 如上面的開發人員計算器所述，數據科學家的收入遠不及受過類似教育的開發人員。

It is important that you come to the realization that, more than the money you make when you pick up the job, you should value the amount of enjoyment you get from the role. At the end of the day, similarly educated and experienced developers will make fairly identical salaries.

重要的是，您必須認識到，除了擔任工作所賺的錢以外，您還應該珍視從角色中獲得的樂趣。 最終，受過類似教育和有經驗的開發人員將獲得完全相同的薪水。

Find the role you’re passionate about and the money will follow. Don’t flip that around and chase the money hoping to find your passion!

找到您熱衷的角色，金錢就會隨之而來。 不要四處亂逛，追逐金錢，希望找到自己的激情！