數據結構兩個月學完

It has been 2 years ever since I started my data science journey. Boy, that was one heck of a roller coaster ride!

自從我開始數據科學之旅以來已經有兩年了 。 男孩 ，那可真是坐過山車！

There were many highs and lows, and of course, countless cups of coffee and sleepless nights.

有很多高峰和低谷，當然還有無數杯咖啡和不眠之夜。

I failed a lot, learned a lot, and of course, grew a lot as a data scientist along the journey.

作為一個數據科學家，我經歷了很多失敗，學到了很多東西，當然，成長了很多。

Throughout my journey in these 2 years, from writing on Medium, speaking at meetups and workshops, sharing my experience on LinkedIn, consulting clients on data science projects, to the current stage of teaching data science in education, I find joy and fulfilment in sharing and teaching to help others in data science and make an impact.

在這兩年的旅程中，從撰寫中型文章 ， 在聚會和研討會 上 發表演講， 在LinkedIn上分享我的經驗 ， 就數據科學項目向客戶提供咨詢 ，到目前在教育中教授數據科學的階段，我在分享中都感到快樂和成就并進行教學以幫助他人在數據科學中產生影響 。

At the end of the day, it all boils down to one simple fact — that I’m moving towards my mission — Making data science accessible to everyone.

歸根結底，這都歸結為一個簡單的事實-我正在朝著自己的使命邁進- 使所有人都能使用數據科學 。

If you’re interested, feel free to check my previous LinkedIn post on why I decided to transition from a data scientist to becoming a data science instructor — a.k.a teacher.

如果您有興趣，請隨時查看我以前在LinkedIn上發布的帖子，以了解為什么我決定從數據科學家過渡到成為數據科學老師(又名老師)。

In this article, for the first time, I’ll consolidate everything that I’ve learned and condense all of these into 5 lessons that I’ve learned in 2 years as a data scientist.

在本文中，我將第一次將自己學到的所有知識整合在一起，并將所有這些知識匯總為我在兩年內作為數據科學家學到的5課 。

If you’re just starting out in data science and wondering what to learn…

如果您只是剛開始從事數據科學，并想知道該學習什么……

Or you’re looking for a job in data science…

或者您正在尋找數據科學領域的工作...

Or you’re already working in data science space…

或者您已經在數據科學領域工作了……

I hope you’ll find these 5 lessons helpful to you as a data scientist!

希望您會發現這5堂課程對數據科學家有幫助！

Enough of talking… Let’s get started!

足夠多的談話……讓我們開始吧！

我兩年來作為數據科學家學到的5課 (5 Lessons I’ve Learned in 2 Years as a Data Scientist)

(Source)(資源)

1.講故事，而不是陳述。 (1. Storytelling, NOT Presentation.)

One of the most profound questions that I’ve ever been asked by one of the great senior data scientists during my data science career:

在我的數據科學職業生涯中，一位偉大的高級數據科學家曾經問過我最深刻的問題之一：

“Admond, what’s the story that we are gonna tell in the meeting later?”

“阿德蒙德，我們稍后在會議上要講的故事是什么？”

The first time I heard this question, I was stunned for a second.

第一次聽到這個問題時，我驚呆了一秒鐘。

He didn’t ask what slides I’d prepared.

他沒有問我準備了哪些幻燈片。

He didn’t ask what I was gonna share.

他沒有問我要分享什么。

He didn’t ask what results that I was gonna tell.

他沒有問我要告訴什么結果。

NONE.

沒有。

To be honest with you, I didn’t even understand why he emphasized so much on telling stories, instead of telling facts that we already had.

老實說，我什至不明白他為什么這么講講故事，而不是講我們已經掌握的事實。

Before I began to appreciate the importance of telling stories, I made tons of mistakes.

在我開始欣賞講故事的重要性之前，我犯了很多錯誤。

Either stakeholders didn’t understand what I was saying. Or the insights couldn’t convince and motivate them to take action.

任何一個利益相關者都不理解我在說什么。 否則這些見解無法說服和激勵他們采取行動。

Once I decided to improve my storytelling skills…

一旦我決定提高敘事技巧，…

Once I started focusing on telling stories…

一旦我開始專注于講故事...

Things changed, for real.

事情變了，真的。

Stakeholders or non-technical bosses began to understand what I was delivering without bombarding them with technical jargons and results. They took action.

利益相關者或非技術老板開始理解我所提供的內容，而沒有用技術術語和結果轟炸他們。 他們采取了行動。

Facts tell, but stories sell.

F 言行舉止，但故事卻賣。

If you want to be a good data scientist, focus on technical skills.

如果您想成為一名優秀的數據科學家，請專注于技術技能。

If you want to be a great data scientist, focus on storytelling skills.

如果您想成為一名出色的數據科學家，請專注于講故事的技能。

所以……如何學習講故事的技巧？ (So… How To Learn Storytelling Skills?)

Want to learn storytelling skills? Learn from Vox.

想學習講故事的技巧嗎？ 向Vox學習。

Because they are the master of storytelling, like seriously.

因為他們是講故事的主人，所以很認真。

They have always been able to explain complex issues or ideas in an engaging and understandable way.

他們始終能夠以一種引人入勝且易于理解的方式解釋復雜的問題或想法。

If this is the first time you’ve heard of Vox, check out their YouTube video below.

如果這是您第一次聽說Vox，請在下面查看他們的YouTube視頻。

Just observe how they explained societal phenomena and issues in the most intuitive storytelling way possible to understand.

只需觀察他們如何以最直觀的講故事的方式解釋社會現象和問題，就可以理解。

And this is very important when it comes to presenting insights or delivering core message to your audience with great storytelling skills.

當談到具有深刻的講故事技巧的見解或向您的聽眾傳達核心信息時，這一點非常重要。

演示地址

Vox — How wildlife trade is linked to coronavirusVox —野生生物貿易與冠狀病毒之間的聯系

2.數據混亂，擁抱它。 (2. Data Is Messy, Embrace It.)

Forget about having Kaggle-like data in your real working environment, because most of the time you won’t have clean data.

忘記在實際的工作環境中擁有類似Kaggle的數據，因為大多數時候您將沒有干凈的數據。

Or worse, sometimes you don’t even have data to begin with, or perhaps you’re just not sure where to get or query data because they are scattered everywhere.

或更糟糕的是，有時您甚至沒有開始使用的數據，或者您只是不確定要從哪里獲取或查詢數據，因為它們分散在各處。

Data collection and data integrity are one of the most important steps in any data science projects, yet a lot of junior data scientists might be oblivious to that.

數據采集 和數據完整性 這是任何數據科學項目中最重要的步驟之一，但是許多初級數據科學家可能會忽略這一點。

The reality is that you need to know where to get your data based on business requirements and the existing data architecture.

現實情況是，您需要根據業務需求和現有數據架構來了解從何處獲取數據。

You might breathe a sigh of relief after you’ve got the data, but this is where the hard part begins — data integrity.

擁有數據后，您可能會松一口氣，但這就是最困難的部分-數據完整性。

You need to perform a thorough check on the data collected by asking hard questions and understanding from different stakeholders to see if the data collected makes any sense.

您需要通過提出難題和不同利益相關者的理解對收集的數據進行徹底檢查，以查看收集的數據是否有意義。

Without having right and accurate data in place at the first place, all of our data cleaning, EDA, machine learning models building, and deployment are simply a luxury.

如果沒有首先放置正確且準確的數據，那么我們所有的數據清理 ， EDA ，機器學習模型的建立和部署都是一種奢侈。

3.軟技能>技術技能 (3. Soft Skills > Technical Skills)

One of the most common questions for beginners in data science is this:

數據科學初學者最常見的問題之一是：

“What are the skills that I need to learn when starting out in data science?”

“從數據科學開始我需要學習哪些技能？”

In my opinion, I think learning technical skills (programming, statistics etc.) should be the priority when first starting out in data science.

在我看來，我認為學習技術技能 (編程，統計學等)應該是首次進入數據科學時的優先事項。

Once we’ve a solid foundation in technical skills, we should focus more on building and improving our soft skills (communication, storytelling etc.).

一旦我們在技術技能上建立了堅實的基礎，我們就應該更加專注于建立和改進我們的軟技能 (溝通，講故事等)。

While this might seem a bit counter-intuitive to the normal ways of learning data science skills, I truly believe in this approach.

盡管這似乎與學習數據科學技能的常規方法有點反常理，但我確實相信這種方法。

WHY?

為什么？

You see. Data scientists are problem solvers.

你看。 數據科學家是解決問題的人。

We don’t just write some code, build some fancy machine learning models and call it a day.

我們不只是編寫一些代碼，構建一些高級的機器學習模型，然后再稱之為一天。

From understanding a business problem, collecting and visualizing data, to the stage of prototyping, fine-tuning and deploying models to real world applications, all these steps require teamwork, communication and storytelling skills to work with team members, manage expectation with stakeholders and ultimately to drive business decisions and actions.

從了解業務問題，收集和可視化數據到原型設計，微調和將模型部署到現實世界應用程序的階段，所有這些步驟都需要團隊合作，溝通和講故事的技巧，才能與團隊成員一起工作，與利益相關者一起管理期望并最終推動業務決策和行動。

There is a famous quote:

有句名言：

“ Without data you’re just another person with an opinion ”

“沒有數據，您就是另一個有意見的人”

— W. Edwards Deming

—愛德華茲·戴明(W. Edwards Deming)

To me, getting data is only the first step. What’s more important is how you can use data to drive business decisions and actions to make a real impact. Here is a slightly modified quote from me:

對我來說，獲取數據只是第一步。 更重要的是如何使用數據來推動業務決策和行動以產生真正的影響。 這是我的引用語：

“ Without storytelling skills you’re just another person with data ”

“沒有講故事的技巧，您就是另一個擁有數據的人”

You can perform the best data analytics in the world.

您可以執行世界上最好的數據分析。

You can build the best machine learning model in the world.

您可以構建世界上最好的機器學習模型。

You can also write the cleanest code in the world.

您還可以編寫世界上最干凈的代碼。

But if you can’t use your results to drive business decisions and actions to convince people to use what you’ve got, your results would only be residing in your PowerPoint slides without having any real impact.

但是，如果您不能使用結果來推動業務決策和采取行動來說服人們使用您所擁有的功能，那么結果將只會駐留在PowerPoint幻燈片中而不會產生任何實際影響。

Sad, but true.

傷心，但真實。

4.可解釋的模型很重要。 (4. Interpretable Models Matter, A Lot.)

For most businesses — unless you’re working at some cutting-edge technology companies — fancy or complex models typically are not the first choice for analytics or predictions.

對于大多數企業而言-除非您在某些尖端科技公司工作-否則，花哨或復雜的模型通常不是分析或預測的首選。

Your boss and stakeholders want to understand what’s going on behind your results.

您的老板和利益相關者希望了解結果背后的情況。

Therefore, you need to be able to explain what’s going on behind your results.

因此，您需要能夠解釋結果背后的原因。

For instance, what caused this anomaly to be detected? And why is that so? Does it make sense in the business context? Why is the prediction the way it is? What are the contributing factors to the prediction? Are our assumptions correct?

例如，什么原因導致此異常被檢測到？ 為什么會這樣呢？ 在商業環境中有意義嗎？ 為什么預測是這樣？ 預測的影響因素是什么？ 我們的假設正確嗎？

From all those questions asked above, it essentially boils down to one simple question:

從以上所有這些問題中，它基本上可以歸結為一個簡單的問題：

“ What’s the pattern observed behind? ”

“觀察到的模式是什么？ ”

Being able to understand what’s going on behind our models and results is crucial to drive business decisions by convincing stakeholders to take actions.

通過說服利益相關者采取行動，能夠了解我們的模型和結果背后發生的事情，對于推動業務決策至關重要。

Huge enterprises simply can’t afford to deploy a blackbox model in the real world and let it run wild on the ground without understanding how it works or when it fails.

巨大的企業根本無力在現實世界中部署黑盒模型，而讓它在不了解其工作原理或失效時間的情況下在野外瘋狂運行。

And this is exactly why we’re still seeing simple models are still being utilized in the current industry like decision trees and logistic regression models.

這就是為什么我們仍然看到諸如決策樹和邏輯回歸模型之類的簡單模型在當前行業中仍在使用的原因。

5.總是看到大圖景 (5. Always See The Big Picture)

(Source)(資源)

I made this huge mistake when I was first starting out in data science.

當我剛開始從事數據科學時，我犯了一個巨大的錯誤。

I focused too much on code and errors but somehow lost sight of the big picture that was truly important — end-to-end pipeline integration in production and how the solution performed in real world.

我過多地專注于代碼和錯誤，但是卻以某種方式忽略了真正重要的全局- 生產中的端到端管道集成以及解決方案在現實世界中的執行情況 。

In other words, I was too fixated with the technical part to the extent of over-optimizing my code and models without having a real impact in the overall project or business.

換句話說，我過于專注于技術部分，以至于過度優化了我的代碼和模型，而對整個項目或業務沒有真正的影響。

Unfortunately, I learned this the hard way.

不幸的是，我很難學到這一點。

Fortunately, I’m currently using what I’ve learned to always remind myself to see the big picture.

幸運的是，我目前正在使用自己學到的知識來提醒自己看大圖。

Hopefully, you’ll begin to realize the importance of seeing the big picture in your day-to-day work as a data scientist.

希望您會開始意識到在作為數據科學家的日常工作中看到全局的重要性。

And the first step to do this is to first understand the business domain and the problems that you’re solving.

第一步是首先了解業務領域和您要解決的問題。

Be clear of what you or your team aims to achieve in a project and understand how your role could be a part of the big picture and how different small pieces of picture can work together as a whole for the common goals.

清楚您或您的團隊在項目中要實現的目標，并了解您的角色如何成為整體的一部分，以及不同的小片段如何共同為共同的目標而協同工作。

最后的想法 (Final Thoughts)

(Source)(資源)

Thank you for reading.

感謝您的閱讀。

My data science journey definitely has been a tough one, but I enjoyed the ride and learned a lot along the way.

我的數據科學之旅當然是艱難的，但是我很喜歡這次旅程，并且在此過程中學到了很多東西。

And I’m still learning each and every day.

而且我仍在每天學習。

I hope you found this article helpful to you in some ways and will apply the lessons here in your work as a data scientist.

我希望您發現本文在某些方面對您有所幫助，并將本文中的課程應用于您作為數據科學家的工作。

Now that I’ve moved to become a data science instructor, you’d also expect more data science content from me in future to help you learn and get into this field.

既然我已經成為一名數據科學講師，那么您也希望以后我會提供更多的數據科學內容，以幫助您學習和進入這一領域。

Check out my other articles if you want to learn more about data science.

如果您想了解有關數據科學的更多信息，請查看我的其他文章 。

If you’re interested in learning how to go into data science, feel free to check out this article — How To Go Into Data Science — where I compiled and answered a list of common questions (or challenges) faced by beginners in data science with guidance.

如果您有興趣學習如何進入數據科學領域，請隨時閱讀本文— 如何進入數據科學領域。 在這里，我整理并回答了數據科學初學者在指導下遇到的常見問題(或挑戰)列表。

I hope you enjoyed reading this article and I look forward to having you as part of the data science community.

希望您喜歡閱讀本文，并希望您成為數據科學界的一員。

Remember, keep learning and never stop improving.

記住，繼續學習，永遠不要停止改進。

As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn. Till then, see you in the next post! 😄

與往常一樣，如果您有任何疑問或意見，請隨時在下面留下您的反饋，或者隨時可以通過LinkedIn與我聯系。 到那時，在下一篇文章中見！ 😄

關于作者 (About the Author)

As a data scientist and data science instructor, Admond Lee is on a mission to make data science accessible to everyone. He is helping companies and digital marketing agencies track and achieve marketing ROI with actionable insights through innovative attribution and data-driven approach.

作為數據科學家和數據科學講師， Admond Lee的使命是使每個人都可以訪問數據科學。 他正在通過創新的歸因和數據驅動方法，以切實可行的見解，幫助公司和數字營銷機構跟蹤并實現營銷投資回報。

His story and data science work have been featured by various publications, including KDnuggets, Medium, Tech in Asia, AI Time Journal and business magazines. Besides, he has been invited to speak at various workshops and meetups.

他的故事和數據科學工作在KDnuggets ， Medium ， Asia in Tech ， AI Time Journal和商業雜志等各種出版物中都有報道。 此外，他還應邀在各種研討會和聚會上演講 。

With his expertise in advanced social analytics and machine learning, Admond aims to bridge the gaps between digital marketing and data science.

憑借在高級社交分析和機器學習方面的專業知識，Admond致力于彌合數字營銷與數據科學之間的鴻溝。

Check out his website if you want to understand more about Admond’s story, data science services, and how he can help you in marketing space using data science.

如果您想了解有關Admond的故事，數據科學服務以及他如何使用數據科學幫助您進行市場營銷的更多信息，請訪問他的網站 。

You can connect with him on LinkedIn, Medium, Twitter, and Facebook.

您可以在LinkedIn ， Medium ， Twitter和Facebook上與他聯系。

翻譯自: https://towardsdatascience.com/here-is-what-ive-learned-in-2-years-as-a-data-scientist-e13a24a74a72

數據結構兩個月學完

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