Since Data Science became the “Sexiest Job of the 21st Century” the interest in the field has grown tremendously. With it so have the courses available to gain the necessary knowledge. As great as this is, the downside is a field marketed as something that can be mastered within weeks and you’ll be fine from there. Yes, it can be learned fast but that’s not the end, data science is a field where you will always continue to learn and have plenty of challenges. Having this in mind, I started thinking of Data Science similar to being an athlete where you train (practice), win or lose, and repeat over again. I started applying an athletic work ethic to my career and grad school and my good/bad moments have changed drastically.

自从数据科学成为“ 21世纪最勤奋的工作”以来，对该领域的兴趣已大大增加。 有了它，课程就可以获取必要的知识。 尽管如此，但不利的一面是，它可以在几周内掌握，因此您可以从中受益。 是的，可以快速学习，但这还不是终点，数据科学是一个您将继续学习并且面临很多挑战的领域。 考虑到这一点，我开始思考数据科学，就像成为一名训练(练习)，赢或输，然后重做的运动员一样。 我开始将体育职业道德应用到我的职业和研究生院，我的好/不好时光已经发生了巨大变化。

Note: This isn’t a guide to just copy someone’s routine and force yourself to follow it. On the contrary, I wanted to provide a practical list of what has helped me that anyone could apply and modify for themselves.

注意 ：这不是仅复制某人的例程并强迫自己遵循该例程的指南。 相反，我想提供一份实用的清单，列出可以帮助任何人自己申请和修改的内容。

练习，练习，我说练习吗？ (Practice, practice and did I say practice?)

This is a reminder I constantly need as well. I’m sure you read about how we need to practice (insert topic) to improve (insert what applies). As an aspiring data scientist I started thinking of practice as not just an inconvenient task, but as part of the process like athletes do. For every game played, there are multiple practice sessions. The goal is to make practice as routine as possible to build a solid foundation and get to the desired level.

这也是我经常需要的提醒。 我确定您已阅读有关如何练习(插入主题)以改进(插入适用内容)的信息。 作为一名有抱负的数据科学家，我开始将练习不仅视为一项艰巨的任务，而且还像运动员一样将其视为过程的一部分。 对于每场比赛，都有多个练习课。 目的是使例行练习尽可能地常规，以建立坚实的基础并达到所需的水平。

As data scientists, we need to start looking as practice as part of what helps us achieve the desired goal and reaching for the next level in our craft. During practice is where our skills develop and we need to treat it as an athletes, never missing practice.

作为数据科学家，我们需要从实践中入手，这是帮助我们实现预期目标并达到更高技术水平的一部分。 在练习中，我们的技能得到了发展，我们需要将其视为运动员，永远不要错过练习。

To practice here are some of my favorite resources:

在这里练习是我最喜欢的一些资源：

For Python and R: Dataquest (free) courses

对于Python和R： Dataquest(免费)课程

Udemy Python Course (there is a fee for this one, but look for discount codes!)

Udemy Python课程 (此课程需要付费，但是请寻找折扣码！)

失败是过程的一部分。 (Failure is part of the process.)

Failure is as much a part of the process as winning. I started viewing failure as a necessary step to my overall win. Once I started applying this mentality to my job hunt, dealing with rejection became bearable and less time spent on dwelling.

失败与获胜一样，是过程的一部分。 我开始将失败视为实现整体胜利的必要步骤。 一旦我开始将这种心态应用到求职中，应对拒绝就变得可以忍受，并且减少了在住宅上的时间。

We need to start thinking, I’m going to get 10 rejections before one ‘Yes’. If the yes comes in just the 5th time around your even better off. If not, your motivation won’t diminish with each one. Getting rejected is just part of the process and with each no, you’ll learn something new that can be applied for the next time. Rest when you need to and continue. It’ll be better to get rejected and keep trying than always wonder what could have been.

我们需要开始思考，我将在“是”之前得到10次拒绝。 如果是的话，您的富裕状况就只是第5次出现。 如果不是这样，您的动力不会随着每个人而降低。 被拒绝只是过程的一部分，每一个都不是，您将学到一些新知识，可以在下一次应用。 在需要时休息，然后继续。 被拒绝并继续尝试比总想知道会发生什么更好。

Sometimes we don’t even notice how much we are letting the fear of failure to consume the work we do. When I first started learning Python I would write my code and make sure everything looked good before running it because I was scared of not getting it correct. Even though it’s just running the program to see how it’s going so far, I was nervous about not getting it correct my first try when it didn’t even matter. But seeing my ‘failure’ for getting it wrong lead to more time wasted because I wasn’t paying attention to what I should.

有时，我们甚至没有注意到我们有多少让我们担心无法消耗我们所做的工作。 刚开始学习Python时，我会写我的代码，并确保在运行它之前一切正常，因为我担心它不会正确。 即使它只是运行程序以查看到目前为止的进展，我还是担心即使在无关紧要的情况下也无法正确地进行第一次尝试。 但是看到我的“失败”是错误的，这会导致更多的时间浪费，因为我没有注意应该做的事情。

Failing is part of the process, it’s just as important as when we get it right. It gets you closer and closer to what you want to achieve and it will happen if we like it or not.

失败是该过程的一部分，与我们正确解决问题同样重要。 它使您越来越接近要实现的目标，无论我们是否愿意，它都会发生。

多功能性 (Versatility)

We have this notion that only talented people can take on multiple roles in their careers. But this idea doesn’t apply to our evolving society, especially in our current pandemic world.

我们的观念是，只有才华横溢的人才能在其职业生涯中扮演多种角色。 但是这种想法不适用于我们不断发展的社会，尤其是在当前的大流行世界中。

It’s important to develop multiple skill sets and start exploring new interests and see where those roads lead. Sticking to only one thing you know is a rapid way to extinction. Technology allows people to put on multiple hats and go look for a one-stop-shop in most of our services. Plus, people already have interests in multiple arenas, cherish those, and apply them. Even Micheal Jordan dabbed with baseball and he was the greatest basketball player of his time.

重要的是要发展多种技能，并开始探索新的兴趣，看看这些路在何处。 坚持只知道一件事是一种快速灭绝的方法。 技术使人们可以戴上许多帽子，并在我们的大多数服务中寻找一站式服务。 另外，人们已经对多个领域产生了兴趣，珍惜并运用了它们。 甚至乔丹(Micheal Jordan)都打棒球，他是当时最伟大的篮球运动员。

In the ever-changing field of data science, having versatility and knowledge in multiple areas is key because only focusing on one topic won’t be sustainable. For example, I’ve taken an interest in Environmentally and used programming and storytelling to showcase my skills on topics not often discussed such as our current waste management system. Most of our interests can be applied in most areas allowing us to expand into new fields.

在不断变化的数据科学领域中，在多个领域拥有多功能性和知识是关键，因为仅关注一个主题将无法持续。 例如，我对“环境”感兴趣，并通过编程和讲故事来展示我的技能，例如在我们当前的废物管理系统等不经常讨论的主题上。 我们的大多数兴趣都可以应用到大多数领域，从而使我们能够扩展到新的领域。

I hope some of these career applications will help you learn and grow as Data Scientists and in your careers. Reach out if you have any other career/work applications. Remember, apply yourself wisely!

我希望其中一些职业应用程序将帮助您作为数据科学家和您的职业学习并成长。 如果您还有其他职业/工作申请，请与他们联系。 记住，明智地运用自己！