Finding great mentors are hard to come by these days. With so much information and so many opinions flooding the internet, finding an authority in a specific field can be quite tough.

這些天很難找到優秀的導師。 互聯網上充斥著如此眾多的信息和眾多見解，因此在特定領域尋找權威可能非常困難。

This feat does not devalue the importance of finding mentors in different stages and areas of our lives. Mentorship has long been considered the most effective way to learn and cut your learning curve in half. Heck, there’s even a Bible scripture on it – Proverbs 19:20-NLT “Get all the advice and instruction you can, so you will be wise the rest of your life”.

這一壯舉并沒有降低在我們生活的不同階段和領域尋找導師的重要性。 長期以來，導師制一直是學習和將學習曲線減少一半的最有效方法。 哎呀，甚至還有一部圣經經文– 箴言19：20-NLT “ 盡一切可能的建議和指示，這樣一生便會很明智 ”。

“If I have seen further it is by standing on the shoulders of Giants” – Isaac Newton

“如果我能進一步看到，那就是站在巨人的肩膀上” –艾薩克·牛頓

With that being said, I thought it necessary to curate a list of effective Data Science professionals that we should all be following, specifically on LinkedIn.

話雖這么說，我認為有必要整理一份我們都應該關注的有效數據科學專業人員的名單，尤其是在LinkedIn上。

Coming up with this list was very difficult and there were so many names I could have added, such as Dat Tran, Kevin Tran, and Steve Nouri to name a few. But I thought “Nah” these names come up so frequently — people should know and be following them by now. I wanted new blood, names that I don’t see thrown about as much but are doing amazing things for the community.

列出此列表非常困難，我可以添加太多名稱，例如Dat Tran ， Kevin Tran和Steve Nouri等等。 但是我認為這些名字經常出現“ Nah”(不)，人們現在應該知道并關注它們。 我想要新的血液，雖然我認為名字很少，但是正在為社區做著很棒的事情。

Note: I must also consider that I do not know the whole population of Data Scientist doing amazing work on LinkedIn. If you wish, feel free to comment some names and add their LinkedIn profiles so that we can give them a follow.

注意 ：我還必須考慮，我不知道整個數據科學家在LinkedIn上所做的出色工作。 如果您愿意，請隨時評論一些名稱并添加其LinkedIn個人資料，以便我們為他們提供關注。

＃1 — Abhishek Thakur (#1 — Abhishek Thakur)

He is the world’s first 4x Kaggle Grandmaster, an Author of one of the most exciting Machine Learning books this year, a Youtuber and Chief Data Scientist at Boost.AI.

他是全球首位4x Kaggle Grandmaster，是今年最激動人心的機器學習書籍之一的作者，還是Boost.AI的Youtuber和首席數據科學家。

If you follow me on LinkedIn, you probably knew that this was coming since I am constantly sharing his post. I personally take tons of inspiration from Abhishek because of how practical he is — everything is applied. I don’t think I’ve ever seen him share something without giving a real world example that is relatable to.

如果您在LinkedIn上關注我，您可能會知道這是即將到來的，因為我一直在分享他的帖子。 我個人從Abhishek那里汲取了很多靈感，因為他的實踐能力強-一切都可以運用。 我認為我從未見過他在沒有提供與之相關的真實示例的情況下分享某些東西。

Much of his work is definitely targeted towards people with Machine Learning experience, but of late he has been posting many videos surrounding breaking into Data Science — here are some examples:

他的大部分工作肯定是針對具有機器學習經驗的人，但最近他發布了許多有關闖入數據科學的視頻-以下是一些示例：

• How to Become A Data Scientist in 1 Year (Learn from a Real World Example)

  如何在1年內成為一名數據科學家(從真實示例中學習)

• How do I start My Career In Data Science?

  如何開始我的數據科學職業？

• My Journey: How I Became The World’s First 4x (and 3x) Grand Master on Kaggle

  我的旅程：我如何成為Kaggle上世界上第一個4x(和3x)大師

Disclaimer: His hair changes a lot but I can verify that it is still him!

免責聲明 ：他的頭發變化很大，但我可以確定它仍然是他！

＃2 — Angshuman Ghosh博士(博士學位，MBA，MBE) (#2 — Dr. Angshuman Ghosh (PhD, MBA, MBE))

Dr. Angshuman shares extremely thought provoking, educational and motivational post surrounding Data Science. I often find myself bookmarking useful resources that he post so I can refer back to it at a later data, for example this 47 page book on Maths For Machine Learning.

昂斯曼(Angshuman)博士在數據科學領域分享了令人發指的啟發性，教育性和激勵性的文章。 我經常發現自己為他發布的有用資源添加了書簽，因此我可以在以后的數據中引用它，例如，這本47頁的關于“機器學習的數學”的書。

He was the Lead Data Scientist at Target and is now the Senior Manager at Grab, as well as a Visiting Professor at the Indian Institute of Management. I definitely would advise giving Dr. Angshu a follow and interacting with his post.

他曾是Target的首席數據科學家 ，現在是Grab的高級經理 ，以及印度管理學院的客座教授。 我絕對建議您給Angshu博士一個關注并與他的帖子互動。

Note: For some weird reason Medium does not make a block when I share the Link to his page. Follow Dr. Angshu on LinkedIn

注意 ：由于某些奇怪的原因，當我共享指向他的頁面的鏈接時，Medium沒有阻止。 按照醫生Angshu上LinkedIn

＃3 — 菲利普·沃爾特 (#3 — Phillip Vollet)

Phillip is a Senior Data Engineer and a radical Natural Language Processing evangelist — I mean literally, radical!

Phillip是一位高級數據工程師，也是一位激進的自然語言處理布道者-我的意思是，太激進了！

He also posts very useful content regarding Data Visualization, Deep Learning and Machine Learning, hence making him an all-rounder to some extent, but in general, he’s definitely going to be talking about NLP.

他還發布了關于數據可視化，深度學習和機器學習的非常有用的內容，因此在某種程度上使他成為多面手，但總的來說，他肯定會談論NLP。

Give him a follow…

跟著他...

＃4 — 埃里克·韋伯 (#4 — Eric Weber)

I recently started following Eric and since that day it has been non-stop gems on my LinkedIn feed. His post major around advice for Data Professionals, but every so often he will drop a sprinkle of resources that are useful for breaking into the Data field.

我最近開始關注埃里克(Eric)，從那天開始，它一直是我的LinkedIn訂閱源中的不停寶石。 他的專業主要是為數據專業人員提供建議，但他經常會浪費大量資源，這些資源對于打入數據領域很有用。

Eric works as the Head of Experimentation and a Data Science Leader at Yelp and I’d definitely put him down as one of the first people to follow.

埃里克(Eric)是Yelp 的實驗負責人和數據科學負責人 ，我肯定會把他當成最早跟隨他的人之一。

＃5 — 馬丹尼 (#5 — Danny Ma)

I feel as though I draw plenty of similarities to Danny so I can relate to his post. Danny is a self-taught Data Scientist and Machine Learning Engineer without a Science, Technology, Engineering or Maths (STEM) degree, Masters or PhD — He even expands on that to say that he has no certifications and struggled to finish 3 online courses. I’m sure many of us can relate.

我覺得自己與Danny有很多相似之處，因此可以參考他的職位。 Danny是一位自學成才的數據科學家和機器學習工程師，沒有科學，技術，工程或數學(STEM)學位，碩士學位或博士學位—他甚至在此基礎上進一步擴大，說自己沒有認證，并且很難完成3項在線課程。 我敢肯定我們很多人都可以聯系。

The way Danny breaks down what it is like to work in the field takes away the fright of the massive salaries being attached to roles. Look, I haven’t cooked a meal since I dropped Food Technology in school, but look how talks about the Data Science diet.

丹尼(Danny)打破在野外工作的感覺的方式消除了與角色相關的巨額薪水的恐懼。 看，自從學校放棄食品技術以來，我還沒有煮過飯，但請看如何談論數據科學飲食。

＃6— 凱爾·麥基歐(Kyle McKiou) (#6— Kyle McKiou)

Introducing Kyle is easy — I will just read his headline. I Teach Data Scientist How to Get Jobs, plain and simple. Kyle post when he has something to say and often it’s quite valuable.

介紹Kyle很容易-我會讀他的標題。 我教數據科學家如何簡單而簡單地找到工作。 凱爾(Kyle)在有話要說時發帖，而這通常很有價值。

I know… controversial!

我知道……有爭議！

There are various ways to connect with Kyle:

與Kyle建立聯系的方式有多種：

• Youtube

  優酷

• Instagram

  Instagram

But this post is about LinkedIn Veterans!

但是這篇文章是關于領英退伍軍人的！

＃7 —萊克斯·弗里德曼 (#7 — Lex Fridman)

Lex Fridman is one of a kind, but his posts do remind me that he is truly human — I can prove it because I saw him sweating. He works at MIT doing Research in human-centered AI, Autonomous Vehicles, and Deep Learning.

萊克斯·弗里德曼(Lex Fridman)是其中的一種，但他的帖子確實使我想起他是真正的人-我可以證明這一點，因為我看到他出汗。 他在麻省理工學院工作，從事以人為中心的人工智能，自動駕駛汽車和深度學習的研究。

Ever heard of how Elon Musk manages his time? Well, Elon Musk found enough time (36 minutes to be precise) to sit with the “Russian Hitman” — he said it not me — Oh… I forgot to add, Joe Rogan thought it necessary also.

是否聽說過埃隆·馬斯克(Elon Musk)如何管理自己的時間？ 好吧，埃隆·馬斯克(Elon Musk)找到了足夠的時間(準確地說是36分鐘)和“俄羅斯殺手”坐在一起-他說不是我-哦……我忘了補充，喬·羅根(Joe Rogan)認為也有必要。

結語 (Wrap Up)

You may be surprised after reading this article but the answer to your question is “YES! There are tons of outstanding Women doing Data Science” and I will be dedicating my next post the 7 women you should be following on LinkedIn — If you’d like to be notified of that post, follow me on Medium.

閱讀本文后，您可能會感到驚訝，但問題的答案是“是！ 有大量杰出的女性從事數據科學研究”，我將在下一篇文章中奉獻您應該在LinkedIn上關注的7位女性-如果您希望收到有關該職位的通知，請在Medium上關注我。

The list I have provided is in no particular order and is most definitely not the only people in the world doing great stuff in the community. As I’ve mentioned earlier, I want to meet more authorities so if you have some that I haven’t named then definitely link them to me!

我提供的列表沒有特別的順序，并且絕對不是世界上唯一在社區中做得很好的人。 正如我前面提到的，我想遇到更多的權威人士，所以如果您有一些我沒有命名的權威人士，那么可以肯定地將它們鏈接到我身上！

Let’s continue the conversation on LinkedIn…

讓我們繼續在LinkedIn上進行對話…