数据科学 ， 意见 (Data Science, Opinion)

We’ve all seen the ‘controversial’ Medium articles that say that MOOC’s don’t make you a Data Scientist, and also the ones that say you should get Certificates accredited by various organizations to prove that you’re actually worthy of calling yourself a Data Scientist.

我们都看过“有争议的”中型文章，这些文章说MOOC不能使您成为数据科学家，还有一些人说您应该获得各个组织的认证证书，以证明您确实值得称自己为“数据科学家”。数据科学家。

Whilst it may be true that these various pathways alter the chances of success, are they really sufficient in being able to call yourself a successful Data Scientist?

虽然这些途径确实改变了成功的机会，但它们真的足以称自己为成功的数据科学家吗？

To call yourself a success in any field is a subjective matter so it’s important to first know what you want out of a journey. Do you want Money? Freedom? Do you want to just have a casual understanding or do you want to go toe-to-toe with the world's best academics?

在任何领域都称自己为success是一个主观的问题，因此，首先要了解旅途中想要的东西很重要。 你要钱吗？ 自由？ 您是想只是一个随意的了解，还是想与世界上最好的学者一较高下？

To be a success , or even world class is something that I think is hugely subject and changes over time, but from my experience of working under and with some of the best minds in the world: the following are simply perquisites:

我认为，要success甚至是world class是一个巨大的课题，并且会随着时间的推移而变化，但是根据我在世界上一些顶尖人才的领导下工作的经验，以下内容很简单：

知道你不知道的 (Know what you don’t know)

What’s your goal and where do you see yourself 10 years from now? When I started on my journey, I was 19 and had a vague picture of where I wanted to go and like many of my contemporaries, serendipity played a big hand on the path I took.

您的目标是什么？十年后您会在哪里看到自己？ 当我开始旅行时，我只有19岁，对我想去的地方含糊不清，与许多当代人一样，机缘巧合在我走的道路上起了很大的作用。

A good teacher and a good guide is a fantastic starting point. Speak to your role model and if you don’t have one: find one. Then you need to make a list of everything you need to know and cross-check your current knowledge set against where you stand.

一个好的老师和一个好的向导是一个奇妙的起点。 与您的榜样交谈，如果您没有，请找一个。 然后，您需要列出所需了解的所有内容，并对照自己的立场对当前知识进行交叉检查。

了解专家在做什么 (Learn about who the experts are in what they’re doing)

Keep an eye on the top 3 or 4 people or institutions in the field. Regardless of which discipline you’re in, it’s always important to know where the bar is and where you stand on the totem pole.

密切关注该领域中排名前3或4的人员或机构。 无论您从事哪种学科，了解酒吧的位置以及您在图腾柱上的位置始终很重要。

Benchmarking is central to the theory of Data Science and likewise, it should be central to your perspective in your discipline.

基准测试是数据科学理论的核心，同样，它也应该是您学科知识的核心。

We’re in an era of perfect information and in an era of openness, use that to your advantage. Watch their youtube videos and read their academic papers, and most importantly, try to get in touch and have a chat!

我们正处于完美信息时代和开放时代，请利用它为您带来优势。 观看他们的youtube视频并阅读他们的学术论文，最重要的是，尝试保持联系并聊天！

A few times in my career, I overlooked this point. Missed opportunities or paths I should have trodden further down are now just memories, but, we learn something from these mistakes. Get good at spotting what you like and don’t like.

在我的职业生涯中，有几次我忽略了这一点。 错失的机会或本该走的路现在只是回忆，但是，我们从这些错误中学到了一些东西。 善于发现自己喜欢和不喜欢的东西。

使您的基础强大 (Make your foundation strong)

When I say learn, I mean you really have to learn. Data Science and Machine Learning require top of the line Mathematics and Statistics. I’d recommend not using sklearn or other out of the box tools (yes, that includes Tensorflow) until you can stand for yourself and take on the big guys in advanced discussions.

当我说学习时，我的意思是你真的必须学习。 数据科学和机器学习需要最高级的数学和统计学。 我建议您不要使用sklearn或其他开箱即用的工具(是的，其中包括Tensorflow )，直到您能站出来并在进阶讨论中与大人物们Tensorflow 。

I’m really sorry but this is debatably the most important part of the whole story. You need to work your ass off and make sure your maths and stats are up to scratch. You want to watch youtube videos, download pdfs, or even learn whilst making a project — but please, know your weakness and keep fighting it!

我真的很抱歉，但这无疑是整个故事中最重要的部分。 您需要精打细算，并确保您的数学和统计数据正确无误。 您想观看youtube视频，下载pdf甚至在制作项目时学习，但请知道您的弱点并继续与之抗争！

Seriously, the best data scientists aren’t geniuses: they just work harder than everyone else.

认真地讲，最好的数据科学家不是天才：他们只是比其他人更努力。

与他人一起进行项目-获得反馈 (Make projects with others — get feedback)

This is really underrated. Working with others on Data Science or Machine Learning projects gives you a deeper understanding of the problem you face because your partner will generally have a different perspective.

这真的被低估了。 与他人一起进行数据科学或机器学习项目可以使您对遇到的问题有更深入的了解，因为您的伴侣通常会有不同的看法。

Everyone has their own story and background so it helps to really diversify your view. Mathematical techniques are gold-dust in Data Science and whether you’re confused on why you should choose L1 over L2 regularisation on a simple regression problem, or debating on how many layers to have in your DNN , these kinds of questions only come to you when you have to justify your actions with other people.

每个人都有自己的故事和背景，因此有助于真正丰富您的观点。 数学技术是数据科学中的金粉尘，无论您是对为什么在一个简单的回归问题上为什么应该选择L1不是L2正则化感到困惑，还是对DNN要包含多少层的争论不休，这些问题只会DNN 。当您必须与他人证明自己的行为合理时。

建立一些可以解决问题的东西 (Build something that fixes a problem)

Think about your own life and think of something that makes you more than 10 minutes to do. Turn this into a list and go through them one by one. Can you use Machine Learning or Data Science to tackle them? I assure you — you can.

考虑一下自己的生活，并考虑一些会让您超过10分钟的事情。 将其转换为列表，然后逐一浏览。 您可以使用机器学习或数据科学来解决它们吗？ 我向您保证-您可以。

Fixing problems isn’t easy and a lot of the time searching for problems often gets you into a convoluted mess. However, understanding the lifecycle of problems get you familiar with the whole ecosystem that we live in.

解决问题并非易事，而且很多时候寻找问题常常会使您陷入混乱。 但是，了解问题的生命周期可以使您熟悉我们所生活的整个生态系统。

We’re in a time where our lives feel symbiotic: half of it is online and the other half is idly walking around the screen to screen. There are so many problems around us: often, you just need to look with a different lens for them all to be clear.

我们正处在一种共生的时代：一半生活在网络上，另一半闲逛在屏幕上。 我们周围有很多问题：通常，您只需要换一个角度来看所有问题即可。

回到第1方块 (Go back to square 1)

So you learned a bit, you taught yourself a bit, you solved a problem and now you’ve accomplished your mission. Now what? Well, this comes back to my first point: know your end goal.

因此，您学到了一点，学到了一些，解决了一个问题，现在您已经完成了自己的任务。 怎么办？ 好吧，这回到我的第一点：知道您的最终目标。

If you’re still not satisfied, re-group and speak to an expert again or reach out to who you look up to. It’s not easy, but this perpetual loop of taking a look at how your career is shaping is a key way to ensure that you are on the right path.

如果您仍然不满意，请重新分组并再次与专家交谈，或者联系您希望找的人。 这并非易事，但是，这种永无休止的循环来看看您的职业生涯是如何形成的，是确保您走上正确道路的关键方法。

I assure you if you keep repeating this loop, you’ll make it to the top. If you don’t believe me, comment below and I can give 101 examples of friends who’ve succeeded far beyond belief by following the top pattern.

我向您保证，如果您继续重复此循环，将使其升到最高位置。 如果您不相信我，请在下方评论，然后我将举101个通过遵循最佳模式成功取得令人难以置信的成功的朋友的例子。

At times the path is steeper than others, so make sure to stay focused and stay cognizant of your current position. You can never be too sure when your next break will be.

有时路径比其他路径陡峭，因此请确保集中注意力并保持对当前位置的了解。 您永远不能确定下一次休息的时间。

Thanks for reading! If you have any messages, please let me know!

谢谢阅读！ 如果您有任何留言，请告诉我！

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