數據科學與大數據技術的案例

I’ve been in that situation where I got a bunch of data science case studies from different companies and I had to figure out what the problem was, what to do to solve it and what to focus on. Conversely, I’ve also designed case studies for data science and analytics positions, sent them out to candidates and evaluated the submissions I had received.

在這種情況下，我收到了來自不同公司的大量數據科學案例研究，因此我不得不弄清楚問題出在哪里，如何解決以及關注什么。 相反，我還為數據科學和分析職位設計了案例研究，將其發送給候選人并評估了我收到的意見書。

Based on this experience and conversations with others (both candidates and hiring managers) I want to address some typical questions around case studies and explain what they’re good for. I will do this by outlining a set of common expectations from hiring managers. I believe that if you understand the hiring manager’s needs for the case study, you as a candidate will know what to focus on in order to leave a great impression during the process.

基于這種經驗以及與其他人(候選人和招聘經理)的交談，我想解決一些與案例研究有關的典型問題，并說明它們的優點。 我將概述招聘經理的一系列共同期望。 我相信，如果您了解招聘經理對案例研究的需求，作為候選人，您將知道該關注什么，以便在此過程中留下深刻的印象。

進行案例研究 (Making a case for case studies)

Let’s take a look at the purpose of the application process from the hiring company’s perspective: They typically have a problem or a set of problems in their business that they would like someone to solve for them, and they are trying to find out if you could do it. There is no certain way to find this out before actually hiring you to do the job. So what’s the next best thing they can do? Yes, you’ve guessed it: They just ask you to solve their problem in a case study (also often referred to as technical assessment, take-home assessment, technical homework etc.)

讓我們從招聘公司的角度看一下申請流程的目的：他們通常在業務中遇到一個問題或一系列問題，希望有人為他們解決，并且他們試圖找出您是否可以做吧。 在實際雇用您從事這項工作之前，沒有確定的方法可以找到答案。 那么他們能做的下一件最好的事情是什么？ 是的，您已經猜到了：他們只是要求您在案例研究中解決問題(通常也稱為技術評估，實地評估，技術作業等)。

This seems rather sneaky, you might think. Aren’t I just giving away my time for free for working on a problem that their employees get paid for? Even if that’s true, it is still the best thing that can happen to you in an application.

您可能會想，這似乎是偷偷摸摸的。 我不是只是為了解決員工薪水高昂的問題而浪費時間嗎？ 即使是真的，這仍然是應用程序中可能發生的最好的事情 。

If the case study indeed reflects the actual work done in the company, this is the single best insight into the kind of problems and the kind of data that you will be working with once hired. Usually, there is also a follow-up interview where you discuss your solution with the data scientists in the company and you hear how they reason about the problem and the solution space. This is great because you can also get the additional benefit of learning about their progress at the given problem.

如果案例研究確實反映了公司的實際工作，那么這是對問題類型和一旦被雇用將要使用的數據類型的唯一最佳見解。 通常，還會有一次后續采訪，您在采訪中與公司中的數據科學家討論解決方案，并聽到他們如何解決問題和解決方案空間的問題。 這很棒，因為您還可以獲得了解他們在給定問題上的進步的額外好處。

Don’t forget: An interview can be a pleasant conversation between data scientists about an interesting problem.

別忘了：采訪可以使數據科學家之間就一個有趣的問題進行愉快的交談。

Why is this so valuable for you? The application process is also supposed to help you to figure out if you want to work for the company and if their challenges are interesting for you. Especially when you’ve already had some work experience and/or you have already figured out exactly what you want for your next role, the maturity level of the company might be a crucial piece of information for your decision making. Yes, I am saying that you should also evaluate your interviewers and pay attention to what they tell you about how they would approach the problem.

為什么這對您如此有價值？ 申請流程還應該幫助您確定是否要為公司工作以及他們面臨的挑戰對您來說是否有趣。 尤其是當您已經有一定的工作經驗和/或已經明確要出任下一個職務時，公司的成熟度可能是您決策的關鍵信息。 是的，我是說您還應該評估您的面試官，并注意他們告訴您的有關他們如何解決問題的信息。

減少招聘的煩惱 (Minimise the pains of hiring)

Hiring someone who is good at their job is expensive. But hiring someone who really sucks at their job is even more expensive. Ask anyone who has made a bad hire before and needed to take some drastic actions, how difficult and nerve-racking the process was. Not to mention the impact on the team that a bad hire can have. Just think of a bad collaboration you’ve had in the past and now imagine you have to work with this person day in, day out, for the next 2 years. And now imagine several people in the team feel the same as you do. What would that do for the team morale?

雇用一個工作出色的人是昂貴的。 但是，聘請真正精干自己工作的人甚至更昂貴。 詢問曾經做過不好工作并需要采取一些嚴厲措施的人，這個過程有多么困難和令人不安。 更不用說糟糕的錄用對團隊的影響。 試想一下您過去的糟糕合作，現在想像一下，在接下來的兩年中，您必須日復一日地與這個人合作。 現在想象一下團隊中的幾個人與您的感覺相同。 這對團隊士氣有什么作用？

Therefore, as a hiring manager my goal is to minimise the chance of hiring a bad match for my team.

因此，作為一名招聘經理，我的目標是最大程度地減少為我的團隊招募不滿意的人的機會 。

If I take this as an objective, we can start working our way backwards: What would I need to know (and what can I reasonably find out during an interview process) in order to minimise that risk? Out of the things that I need to find out during the interview process, which are the ones that I can more naturally check with a case study? Are there perhaps some things that a work sample from a candidate can tell me more about than just asking some direct questions in an interview?

如果我以此為目標，我們可以開始倒退：為了最小化這種風險，我需要知道什么(在面試過程中可以合理地找到什么)？ 在面試過程中需要找出的東西中，哪些可以通過案例研究更自然地檢查出來？ 候選人的工作樣本中也許有一些事情可以告訴我更多的信息，而不僅僅是在面試中問一些直接的問題？

Following this reasoning I formulated three questions that a hiring manager is typically trying to answer through a case study. Answering these questions will allow them to build up a mental model of the candidate and their fit to the role. Let these questions guide your attention when you are tackling a case study as a candidate. If you make an impression there, you know it will be noticed.

根據這種推理，我提出了三個問題，招聘經理通常會通過案例研究來回答這些問題。 回答這些問題將使他們能夠建立候選人的心理模型，并使其適應職位。 當您進行個案研究時，讓這些問題引導您的注意力。 如果您在該處留下印象，就會知道它會被注意到。

問題1：您如何將您的想法和工具應用于我們的業務問題？ (Q1: How well can you apply your thinking and your tools to our business problem?)

While in theory it might make sense to try to hire someone who has done the exact same job that I’m hiring for before, in practice this might be more difficult (small candidate pool, and retaining this employee might be difficult as they can easily get bored). Therefore, hiring managers usually need to expand their candidate pool, and they need to assess candidates whose experience might only be remotely related to what the job demands. So a thorough hiring manager will try to create a process that gives them several ways to probe the candidate’s fit for the job’s (skill) requirements. This is what the case study does (among other things).

雖然從理論上講，嘗試聘用與我之前從事過的工作完全相同的人可能很有意義，但實際上這可能會更加困難(候選人人數少，并且留住這名員工可能很困難，因為他們很容易感到厭倦)。 因此，招聘經理通常需要擴展他們的候選人庫，并且他們需要評估其經驗可能僅與工作要求密切相關的候選人。 因此，一個徹底的招聘經理將嘗試創建一個流程，為他們提供幾種方法來探究候選人對職位(技能)要求的適合程度。 案例研究就是這樣做的(除其他外)。

If you interview for a role in a different industry or domain, don’t underestimate the value that you can add as a ‘newbie’. I dare to infer from the diversity of typical data science teams that most data science skills are transferable across industries. Sure, whenever you make a switch, there is a lot of domain knowledge that you need to pick up from scratch. But the analysis and modelling tools that you have mastered in one job/industry can still be useful in another job/industry. In fact, they might even be a bigger asset because nobody in the existing team has ever tried your toolset on the problem. You just need to find out how your tools might apply and what are their limitations in the new setup.

如果您面試不同行業或領域中的角色， 請不要低估可以作為“新手”添加的價值 。 我敢于從典型的數據科學團隊的多樣性中推斷出，大多數數據科學技能可以跨行業轉移。 當然，每當進行切換時，都需要從頭開始學習很多領域知識。 但是，您在一個工作/行業中掌握的分析和建模工具仍然可以在另一工作/行業中使用。 實際上，它們甚至可能是更大的資產，因為現有團隊中沒有人嘗試過您的工具集來解決這個問題。 您只需要了解您的工具如何應用以及它們在新設置中的局限性。

Photo by Barn Images on Unsplash Barn Images在Unsplash上拍攝的 照片

One of the best and most thorough submissions I ever received as a hiring manager came from a social psychology graduate. Let’s call her Jane.

作為一名招聘經理，我收到的最好，最徹底的意見之一是社會心理學專業的畢業生。 我們叫她簡。

In the case study’s dataset we included a feature that one could consider using as labels in order to formulate a supervised learning task out of the problem statement. But if you would look more closely, you would realise that this feature contains too many missing values, and even worse, the presence of that feature is biased. So if you were to just train a model to predict this feature, you would train a biased model.

在案例研究的數據集中，我們包含了一項功能，可以考慮將其用作標簽，以便根據問題陳述來制定監督學習任務。 但是，如果仔細觀察，您會發現此功能包含太多的缺失值，更糟糕的是，該功能的存在存在偏差。 因此，如果僅訓練模型來預測此功能，則將訓練有偏差的模型。

Jane had gained a good enough understanding of our business domain through her own research and realised that bias in the data could be a problem. So she tested for interaction effects of the presence of this feature with some other features in the dataset. By doing this she could confidently conclude that there is in fact a bias. So she made the call to not use it as a label.

簡通過自己的研究已經對我們的業務領域有了足夠的了解，并意識到數據中的偏差可能是一個問題。 因此，她測試了此功能與數據集中其他功能的交互作用。 通過這樣做，她可以自信地得出結論，實際上存在偏見。 因此她打了電話，不要將其用作標簽。

While Jane doesn’t have a typical data science experience, she had analysed tons of experiment data in her past academic experience. She knows what tools she can use to tease out information from an unknown dataset and how to draw conclusions from it. By familiarising herself with our product she could also come up with reasonable hypotheses to test. In the end, Jane actually ended up implementing a solution that came very close to our own solution at the time, using an unsupervised technique. Needless to say, we extended an offer to her.

盡管Jane沒有典型的數據科學經驗，但她在過去的學術經驗中曾分析過大量的實驗數據。 她知道自己可以使用哪些工具從未知數據集中獲取信息，以及如何從中得出結論。 通過使自己熟悉我們的產品，她還可以提出合理的假設進行測試。 最后，Jane實際上使用一種無??監督的技術最終實現了一個非常接近我們自己的解決方案的解決方案。 不用說，我們向她提供了報價。

Do it like Jane.

像簡一樣。

Do your homework before you come up with a suitable method for solving the problem. If you have done your homework well, it will also be easy for you to be on top of your results and to explain why you chose your method (see Q3 below).

在找到解決問題的合適方法之前，請先做作業 。 如果您的作業做得不錯，那么您也很容易掌握結果并解釋為什么選擇方法(請參閱下面的第3季度)。

問題2：您如何在不完善的信息下采取行動？ (Q2: How will you act under imperfect information?)

It is very rarely (dare I say: never) the case that you will receive a case study that has a full specification of the problem and what needs to be done. And there is, of course, a very good reason for that: In the real world, there is also very rarely (again, probably never) a full specification of the problem at hand. Often it is your job to identify what information is missing, devise a plan of how to actively fill those knowledge gaps, or at least how to manage the uncertainty that comes with knowledge gaps. This usually also requires you to prioritise where to dig deeper and which gaps to skim over (for the time being).

很少(敢于我說：從不)您會收到一個案例研究，其中包含有關問題和需要完成的操作的完整說明。 當然，這有一個很好的理由：在現實世界中，也很少(再也可能永遠沒有)對即將出現的問題進行全面說明。 通常，您的工作是確定丟失了哪些信息，制定一個計劃來積極填補這些知識空白，或者至少如何管理知識空白帶來的不確定性。 通常，這還要求您優先考慮在何處進行更深入的挖掘以及暫時跳過哪些差距。

Rory Bj?rkman on 羅里·比約克曼在UnsplashUnsplash

I got a case study as a candidate once where I was told to design ‘a data science solution’ to ‘optimise […] operations’. This was literally the instruction I got. No hint about what ‘data science solution’ means. No hint about what to optimise entails. Not even any details about how the current process works. Full freedom.

曾經有一個案例研究作為候選人，我被告知要設計“數據科學解決方案”以“優化[…]運營”。 這實際上是我得到的指示。 沒有暗示“數據科學解決方案”的含義。 沒有關于優化的暗示。 甚至沒有關于當前流程如何工作的任何細節。 充分的自由。

I suggest to work your way backwards in these cases: Who are the users of the system and what would success mean for them? How can we formalise this success criterion in one or a few KPIs? Once we know which KPIs we are trying to optimise, brainstorm about how we can get there from where we are at now? Oh, we don’t know where we are at right now? Let’s do some research and make some educated guesses then. Once we have an idea about success, let’s also think about the risks, or the worst-case scenario. Is it captured by some KPI? What can we do to mitigate it?

我建議在以下情況下退后一步：誰是系統的用戶，成功對他們意味著什么？ 我們如何才能在一個或幾個KPI中正式化此成功標準？ 一旦知道了我們要優化的KPI，就如何從現在的位置實現目標進行集思廣益？ 哦，我們不知道我們現在在哪里？ 讓我們做一些研究，然后做出一些有根據的猜測。 一旦有了關于成功的想法，就讓我們考慮一下風險或最壞的情況。 它是否被某些KPI捕獲？ 我們可以采取什么措施來緩解這種情況？

Each of these questions might require some research, some thinking and some hypothesising. And that is okay, because on the job this process will look quite similar. You might have to scan a lot of internal documentation, interview product owners/stakeholders, or consult external resources to find the answers.

這些問題中的每一個都可能需要一些研究，一些思考和一些假設。 沒關系，因為在工作過程中此過程看起來將非常相似。 您可能需要掃描大量內部文檔，采訪產品所有者/利益相關者或咨詢外部資源以找到答案。

You will not get every single assumption or conclusion right for the case study, but that is also never the expectation. Rather, your interviewers want to see that you can come up with a plan to tackle the unknowns while making a reasonable prioritisation. Maybe in the follow-up interview they will correct some of your assumptions. Be prepared to be challenged and to adapt your solution to new information. This is also why doing your homework (see previous section, Q1) can help you to think on your feet in this discussion.

對于案例研究，您將不會獲得每個正確的假設或結論，但這絕不是期望。 相反，您的面試官希望看到您可以提出一個計劃，以解決未知的問題，同時進行合理的優先排序。 也許在后續采訪中，他們將糾正您的一些假設。 準備好迎接挑戰，并使您的解決方案適應新的信息。 這也是為什么要做功課(請參閱上一節，第1季度)可以幫助您在此討論中思考的原因。

問題3：您與團隊的合作程度如何？ (Q3: How well will you collaborate with the team?)

A question I sometimes hear from data scientists is how much attention they should pay to code quality in their case study submissions. Well, what does good code quality help with?

我有時從數據科學家那里聽到的一個問題是，他們在案例研究提交中應該對代碼質量給予多大的關注。 那么，良好的代碼質量有什么幫助？

It’s about if other people in the team can understand your code, collaborate with you on your code, and maintain your code when you’re on holidays. So ask yourself, can someone else who is seeing your code for the first time understand it easily? Would they be able to work on it without going crazy? If you can’t answer these questions, ask yourself as a proxy: Can I understand this code 3 months or 1 year from now and work on it without hating my past self?

團隊中的其他人是否可以理解您的代碼，在代碼上與您合作以及在休假期間維護代碼。 因此，問問自己，第一次看到您的代碼的其他人可以輕松理解嗎？ 他們能夠在不發瘋的情況下進行工作嗎？ 如果您無法回答這些問題，請以代理人的身份問自己：我可以從現在開始的3個月或1年后理解此代碼，并且在不討厭自己過去的情況下進行工作嗎？

Photo by Markus Winkler on Unsplash Markus Winkler在Unsplash上拍攝的照片

More fundamentally, it is a question of collaboration: Will people in the team enjoy working with you? Will they understand and trust your work? Will they be able to build upon your work (and you on theirs)?

從根本上講，這是協作的問題 ：團隊中的人會喜歡和您一起工作嗎？ 他們會理解并信任您的工作嗎？ 他們將能夠在您的工作(以及您在他們的工作)的基礎上發展嗎？

The understanding and the trust will depend on another important collaboration related skill (if not the most important) that a hiring manager will check with the case study (and throughout the process). It’s communication.

理解和信任將取決于另外一個重要的協作相關的技能(如果不是最重要的 )，一個招聘經理將與案例研究(以及整個過程中)檢查。 是交流 。

As for most case studies there is not one single correct solution, so it’s important that you can explain the approach you took and the reasoning behind it. You can easily imagine situations on the job when a data scientist has to communicate their work to team mates or justify it to stakeholders. Especially with the latter you will probably be more effective if you show some sort of storytelling skills, i.e. your work communicates a clear message in a compelling and easy-to-follow way (e.g. through visualisations, illustrative examples etc.).

對于大多數案例研究，沒有一個單一的正確解決方案，因此，重要的是您可以解釋所采用的方法及其背后的原因。 您可以輕松想象工作中的情況，即數據科學家必須將其工作傳達給隊友或將其證明給利益相關者。 特別是對于后者，如果您表現出某種講故事的技巧，則可能會更有效，例如，您的作品以引人注目的且易于遵循的方式傳達清晰的信息(例如，通過可視化，說明性示例等)。

So read carefully who the case study presentation is for and tailor your presentation to this audience. What is the right level of abstraction? Are you prepared to give more details of your solution and your data when needed?4 Can you explain the limitations and the trade-offs of your solution? Does your solution present clear action points / key takeaways? Can you maybe even inspire your audience with a future vision of the solution?

因此，請仔細閱讀該案例研究的演講對象，然后針對此受眾量身定制您的演示文稿。 什么是正確的抽象級別？ 您是否準備在需要時提供解決方案和數據的更多詳細信息？ 4您能解釋一下解決方案的局限性和權衡嗎？ 您的解決方案是否提供清晰的操作要點/關鍵要點？ 您甚至可以用該解決方案的未來愿景來激發您的聽眾嗎？

Don’t make the mistake of thinking that it’s enough to just build an unexplainable model that has high accuracy. Don’t underestimate the importance of collaboration skills.

不要誤以為僅僅建立一個無法解釋的高精度模型就足夠了。 不要低估協作技能的重要性。

結論 (Conclusion)

In this article, I have tried to convince you that data science case studies provide an opportunity for candidates to learn a lot about the company and the role. I also shared a common set of expectations that hiring managers have towards case studies, and derived some tips for candidates. I hope these tips help you to decide what to focus on in your next case study.

在本文中，我試圖說服您，數據科學案例研究為應聘者提供了一個學習有關公司及其角色的很多知識的機會。 我還對招聘經理對案例研究抱有共同的期望，并為求職者提供了一些技巧。 我希望這些技巧可以幫助您決定下一個案例研究的重點。

However, there might always be some more specific requirements that only the job you’re applying for has. Try out the exercise of putting yourself into the hiring manager’s shoes! If you feel you are not able to because you lack information about the hiring manager’s expectations, maybe it’s a good time to ask for a chat with the hiring manager in order to understand them better.

但是，可能總會有一些更具體的要求，只有您要申請的工作才有。 嘗試鍛煉自己，使自己適應招聘經理的職責！ 如果您由于缺乏有關招聘經理期望的信息而感到無法做到，也許是時候與招聘經理進行聊天以更好地了解他們了。

Best of luck!

祝你好運！

This article was first published on https://mins.space.

該文章最初在https://mins.space上發布。

翻譯自: https://towardsdatascience.com/master-data-science-case-studies-a-hiring-managers-perspective-49e508263280

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