大數(shù)據(jù)業(yè)務(wù)學(xué)習(xí)筆記

意見 (Opinion)

A lot of aspiring Data Scientists think what they need to become a Data Scientist is :

許多有抱負(fù)的數(shù)據(jù)科學(xué)家認(rèn)為，成為一名數(shù)據(jù)科學(xué)家需要具備以下條件：

• Coding
  編碼
• Statistic
  統(tǒng)計(jì)
• Math
  數(shù)學(xué)
• Machine Learning
  機(jī)器學(xué)習(xí)
• Deep Learning
  深度學(xué)習(xí)

And any other technical skills.

以及其他任何技術(shù)技能。

The above list is accurate; most of the Data Scientist qualification you need right now is what I list above. It is unavoidable, as many job listing right now always list these skills as a prerequisite. Just look at the example of Data Scientist job requirements and preferences below.

上面的清單是準(zhǔn)確的； 我上面列出的是您現(xiàn)在需要的大多數(shù)數(shù)據(jù)科學(xué)家資格。 這是不可避免的，因?yàn)楝F(xiàn)在很多工作清單總是將這些技能列為前提條件。 只需看下面的數(shù)據(jù)科學(xué)家工作要求和偏好示例。

Taken from indeed.com摘自確實(shí)網(wǎng)站

Most of the requirements sound technical; degree, coding, math, and stats. Although, there is an underlying business understanding requirement that you might not realize at first from this job advertisement.

大部分要求聽起來都是技術(shù)性的； 學(xué)位，編碼，數(shù)學(xué)和統(tǒng)計(jì)信息。 但是，有一個(gè)潛在的業(yè)務(wù)理解要求，您可能首先不會從此招聘廣告中意識到。

If you look closely, they require someone that had experience in applying the analytical method to solve practical business problems. It implies your everyday task would consisting of solving the business problem, which in turn, you need to understand what kind of business the company runs and how the process itself works.

如果您仔細(xì)觀察，他們會要求那些具有應(yīng)用分析方法來解決實(shí)際業(yè)務(wù)問題的經(jīng)驗(yàn)的人。 這意味著您的日常任務(wù)將包括解決業(yè)務(wù)問題 ，而這又需要您了解公司經(jīng)營哪種業(yè)務(wù)以及流程本身如何運(yùn)作。

You might ask, “Why do I need to understand it? Just create the machine learning model and the problem is solved, isn’t it?” Well, that line of thinking is dangerous, and I would explain why.

您可能會問：“為什么我需要了解它？ 只需創(chuàng)建機(jī)器學(xué)習(xí)模型即可解決問題，不是嗎？” 好吧，這種思路很危險(xiǎn)，我將解釋原因。

Just for a reminder, I would argue what makes you great as a Data Scientist is not only how well your coding skill is or how much you understand the statistical theory or even the master of business understanding, but it is a combination of many.

提醒您， 讓我成為數(shù)據(jù)科學(xué)家的不僅僅在于您的編碼技能如何，或者您對統(tǒng)計(jì)理論甚至對業(yè)務(wù)理解的掌握有多少，而且還包括很多方面。

Anybody, of course, could agree or not with my opinion as I believe there are no specific skills that make you a great Data Scientist.

當(dāng)然，任何人都可以同意或不同意我的觀點(diǎn)，因?yàn)槲蚁嘈艣]有特定的技能可以使您成為一名出色的數(shù)據(jù)科學(xué)家。

Data Scientist employment is hard. It would not easy to get in this field. With many applicants and people with a similar set of skills, you need to stand out. Business Understanding is the skill that would certainly separate you from all the fish in the ponds.

數(shù)據(jù)科學(xué)家的工作很難。 進(jìn)入這個(gè)領(lǐng)域并不容易。 由于許多申請人和具有類似技能的人，您需要脫穎而出。 業(yè)務(wù)理解能力無疑會使您與池塘中的所有魚區(qū)分開。

In my experience as a Data Scientist, there is no skill that I felt underrated as much as the business understanding skill. I even thought that you don’t need to understand the business in my early career. How wrong I was.

根據(jù)我作為數(shù)據(jù)科學(xué)家的經(jīng)驗(yàn)，沒有什么比業(yè)務(wù)理解技能低估了。 我什至以為您在我的早期職業(yè)中不需要了解業(yè)務(wù)。 我錯(cuò)了

I am not ashamed, though, to admit that I did not consider the business aspect essential at first because many data science education and books did not even teach us about this.

但是，我并不感到ham愧，因?yàn)槲乙婚_始并不認(rèn)為業(yè)務(wù)方面是必不可少的，因?yàn)樵S多數(shù)據(jù)科學(xué)教育和書籍甚至都沒有教過我們這一點(diǎn)。

So, why is it crucial to learn the business and how it impacts your employment as a Data Scientist?

那么，為什么學(xué)習(xí)業(yè)務(wù)至關(guān)重要，它又如何影響您作為數(shù)據(jù)科學(xué)家的工作呢？

Just imagine this situation. You work in the data department of the food industry with candy as their main product, and the company plans to release a new sour candy product. The company then ask the sales department to sell the product. Now, the sales department know that the company had a data department and requesting the data team to give new leads where they can sell sour candy.

試想一下這種情況。 您在食品工業(yè)的數(shù)據(jù)部門工作時(shí)，以糖果為主要產(chǎn)品，并且該公司計(jì)劃發(fā)布一種新的酸味糖果產(chǎn)品。 然后，公司要求銷售部門出售產(chǎn)品。 現(xiàn)在，銷售部門知道該公司有一個(gè)數(shù)據(jù)部門，并要求數(shù)據(jù)團(tuán)隊(duì)提供新的線索以銷售酸味糖果。

Before anybody complains that “This is not our job, we create a machine learning model!” or “I work as a data scientist, not in the sales department.” No, this is precisely what Data scientists do in the company; many of the projects are to work with another department for solving the company problem.

在有人抱怨“這不是我們的工作之前，我們創(chuàng)建了機(jī)器學(xué)習(xí)模型！” 或“我是數(shù)據(jù)科學(xué)家，而不是在銷售部門。” 不，這正是數(shù)據(jù)科學(xué)家在公司中所做的； 許多項(xiàng)目將與另一個(gè)部門合作解決公司問題。

Back to our scenario, how do you correctly approach this problem then? You might think, “Just create a machine learning model to generate the leads.” Yes, it is on the right track, but how exactly you create the model? On what basis? Is the business question even viable enough to solved using the machine learning model?

回到我們的情況，那么您如何正確解決此問題？ 您可能會想，“只要?jiǎng)?chuàng)建一個(gè)機(jī)器學(xué)習(xí)模型來生成線索即可。” 是的，它是在正確的軌道上，但是您如何精確地創(chuàng)建模型？ 在什么基礎(chǔ)上？ 業(yè)務(wù)問題是否足夠可行，可以使用機(jī)器學(xué)習(xí)模型解決？

You can’t just suddenly using a machine learning model, right? This is why business understanding is so crucial as a Data Scientist. You need to understand how the candy business in more detail. Keep asking a question like,

您不能只是突然使用機(jī)器學(xué)習(xí)模型，對嗎？ 這就是為什么業(yè)務(wù)理解對數(shù)據(jù)科學(xué)家如此重要的原因。 您需要更詳細(xì)地了解糖果業(yè)務(wù)。 繼續(xù)問一個(gè)問題，

• “What kind of business question exactly we want to solve?”

  “ 我們到底想解決什么樣的業(yè)務(wù)問題？”

• “Would we even need a machine learning model?”

  “我們甚至需要機(jī)器學(xué)習(xí)模型嗎？”

• “What kind of attributes related to candy sales?”

  “與糖果銷售相關(guān)的屬性是什么？”

• “How is the candy selling strategy and practice within and outside of the company?”.

  “公司內(nèi)部和外部的糖果銷售策略和實(shí)踐如何？” 。

And many more business questions you could think of related to the business.

還有更多您可能想到的與業(yè)務(wù)相關(guān)的業(yè)務(wù)問題。

It is important to know what kind of business your company run and everything related to the business as your work as a data scientist would need you to make sense of the data.

了解您的公司經(jīng)營哪種業(yè)務(wù)以及與該業(yè)務(wù)相關(guān)的所有事項(xiàng)非常重要，因?yàn)樽鳛?strong>數(shù)據(jù)科學(xué)家，您需要了解數(shù)據(jù) 。

While it is easy to say that business understanding skill is essential, it is not easy to gain one.

雖然容易理解業(yè)務(wù)理解技能是必不可少的，但要獲得一項(xiàng)技能卻并不容易。

Education is one thing; for example, you might have a higher chance to stand out to applying for a data science position in the PR company if your educational background is communication compared to someone with a biology degree.

教育是一回事； 例如，與具有生物學(xué)學(xué)位的人相比，如果您的教育背景是交流，那么您可能有更大的機(jī)會脫穎而出在PR公司申請數(shù)據(jù)科學(xué)職位。

Although work experience quickly covers this. Working experience with another job title in a similar business industry would provide significant leverage, as you already understand the business process.

盡管工作經(jīng)驗(yàn)很快就涵蓋了這一點(diǎn)。 由于您已經(jīng)了解業(yè)務(wù)流程，因此在類似的業(yè)務(wù)行業(yè)中擁有另一個(gè)職務(wù)的工作經(jīng)驗(yàn)將提供重要的影響。

For a fresher, it might be a hard industry to break in, but in hindsight, there are many benefits as a fresher as well. I remember Tyler Folkman’s post on his LinkedIn why the industry should consider recent graduates, and I agree. The recent graduate could:

對于新生，這可能是一個(gè)很難進(jìn)入的行業(yè)，但是事后看來，新生也有很多好處。 我記得泰勒·福克曼(Tyler Folkman)在其LinkedIn上的帖子，為什么該行業(yè)應(yīng)考慮應(yīng)屆畢業(yè)生，我也同意。 應(yīng)屆畢業(yè)生可以：

Come with preparation

附帶準(zhǔn)備

Hungry to learn about the business

渴望了解業(yè)務(wù)

Make an impact

產(chǎn)生影響

Freshers should a target for companies that have established their data journeys. The company could teach many things about business more easily as fresher have no experience at all in the business world. In my opinion, never count out the freshers.

新生應(yīng)該成為建立數(shù)據(jù)旅程的公司的目標(biāo)。 該公司可以更輕松地教授有關(guān)業(yè)務(wù)的許多事情，因?yàn)閯傞_始的新手根本沒有業(yè)務(wù)領(lǐng)域的經(jīng)驗(yàn)。 我認(rèn)為，永遠(yuǎn)不要指望新生。

I also would tell you about my experience, as well. When I first get the data project, I was not thinking about the business at all and just tried to build the machine learning model. And how disastrous it turns out to be.

我也將告訴您我的經(jīng)歷。 當(dāng)我第一次獲得數(shù)據(jù)項(xiàng)目時(shí)，我根本沒有考慮業(yè)務(wù)，只是嘗試構(gòu)建機(jī)器學(xué)習(xí)模型。 事實(shí)證明這是多么的災(zāi)難。

I present the model to the related parties with hype in my brain. My model result is good, I know everything about the data, and I know the theory of the model I used. Easy peasy, right? So, wrong. It turns out that the user did not care about the model I used. They are more interested in knowing if I already consider a business approach “A” or why I used the data that should not relate at all to the business. It ends with a discussion that I need more business training.

我在腦海中大肆宣傳該模型。 我的模型結(jié)果很好，我了解所有有關(guān)數(shù)據(jù)的知識，并且知道我使用的模型的理論。 輕輕松松吧？ 大錯(cuò)特錯(cuò)。 事實(shí)證明，用戶并不關(guān)心我使用的模型。 他們更想知道我是否已經(jīng)考慮過業(yè)務(wù)方法“ A”，或者為什么我使用了與業(yè)務(wù)根本不相關(guān)的數(shù)據(jù)。 最后，我需要更多的業(yè)務(wù)培訓(xùn)。

It is embarrassing, but I am not ashamed at all to admit that it is my fault not to consider business understanding. I could be the best in model creation or statistic, but not knowing the business turns out to be a disaster. Since that day, I try to learn more about the business process itself, even before considering any of the technical things.

令人尷尬，但我完全不as愧承認(rèn)不考慮業(yè)務(wù)了解是我的錯(cuò)。 在模型創(chuàng)建或統(tǒng)計(jì)方面，我可能是最好的，但我不知道這業(yè)務(wù)真是一場災(zāi)難。 從那天開始，即使在考慮任何技術(shù)問題之前，我也會嘗試進(jìn)一步了解業(yè)務(wù)流程本身。

結(jié)論 (Conclusion)

In my opinion, fresher or not, try to learn the business as much as possible.

我認(rèn)為，無論是否新鮮，都應(yīng)盡可能多地學(xué)習(xí)業(yè)務(wù)。

Focus on one industry you feel interested in; finance, banking, credit, automotive, candy, oil, etc. Every single business has a different approach and strategy; you just need to focus on learning the industry you like.

專注于您感興趣的一個(gè)行業(yè)； 金融，銀行，信貸，汽車，糖果，石油等。每一項(xiàng)業(yè)務(wù)都有不同的方法和策略； 您只需要專注于學(xué)習(xí)自己喜歡的行業(yè)即可。

Data scientist employment is hard. It was not easy to get into this field. With many applicants and many people with a similar set of skills, you need to stand out. Business understanding is the skill that will undoubtedly separate you from all the fish in the pond.

數(shù)據(jù)科學(xué)家的工作很難。 進(jìn)入這個(gè)領(lǐng)域并不容易。 在許多申請人和具有相似技能的許多人中， 您需要脫穎而出。 業(yè)務(wù)理解能力無疑會使您與池塘中的所有魚類區(qū)分開。

翻譯自: https://towardsdatascience.com/learn-the-business-to-become-a-great-data-scientist-635fa6029fb6

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