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.

自從數據科學成為“ 21世紀最勤奮的工作”以來，對該領域的興趣已大大增加。 有了它，課程就可以獲取必要的知識。 盡管如此，但不利的一面是，它可以在幾周內掌握，因此您可以從中受益。 是的，可以快速學習，但這還不是終點，數據科學是一個您將繼續學習并且面臨很多挑戰的領域。 考慮到這一點，我開始思考數據科學，就像成為一名訓練(練習)，贏或輸，然后重做的運動員一樣。 我開始將體育職業道德應用到我的職業和研究生院，我的好/不好時光已經發生了巨大變化。

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 ：這不是僅復制某人的例程并強迫自己遵循該例程的指南。 相反，我想提供一份實用的清單，列出可以幫助任何人自己申請和修改的內容。

練習，練習，我說練習嗎？ (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.

這也是我經常需要的提醒。 我確定您已閱讀有關如何練習(插入主題)以改進(插入適用內容)的信息。 作為一名有抱負的數據科學家，我開始將練習不僅視為一項艱巨的任務，而且還像運動員一樣將其視為過程的一部分。 對于每場比賽，都有多個練習課。 目的是使例行練習盡可能地常規，以建立堅實的基礎并達到所需的水平。

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.

作為數據科學家，我們需要從實踐中入手，這是幫助我們實現預期目標并達到更高技術水平的一部分。 在練習中，我們的技能得到了發展，我們需要將其視為運動員，永遠不要錯過練習。

To practice here are some of my favorite resources:

在這里練習是我最喜歡的一些資源：

For Python and R: Dataquest (free) courses

對于Python和R： Dataquest(免費)課程

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

Udemy Python課程 (此課程需要付費，但是請尋找折扣碼！)

失敗是過程的一部分。 (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.

失敗與獲勝一樣，是過程的一部分。 我開始將失敗視為實現整體勝利的必要步驟。 一旦我開始將這種心態應用到求職中，應對拒絕就變得可以忍受，并且減少了在住宅上的時間。

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.

我們需要開始思考，我將在“是”之前得到10次拒絕。 如果是的話，您的富裕狀況就只是第5次出現。 如果不是這樣，您的動力不會隨著每個人而降低。 被拒絕只是過程的一部分，每一個都不是，您將學到一些新知識，可以在下一次應用。 在需要時休息，然后繼續。 被拒絕并繼續嘗試比總想知道會發生什么更好。

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.

有時，我們甚至沒有注意到我們有多少讓我們擔心無法消耗我們所做的工作。 剛開始學習Python時，我會寫我的代碼，并確保在運行它之前一切正常，因為我擔心它不會正確。 即使它只是運行程序以查看到目前為止的進展，我還是擔心即使在無關緊要的情況下也無法正確地進行第一次嘗試。 但是看到我的“失敗”是錯誤的，這會導致更多的時間浪費，因為我沒有注意應該做的事情。

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.

失敗是該過程的一部分，與我們正確解決問題同樣重要。 它使您越來越接近要實現的目標，無論我們是否愿意，它都會發生。

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.

我們的觀念是，只有才華橫溢的人才能在其職業生涯中扮演多種角色。 但是這種想法不適用于我們不斷發展的社會，尤其是在當前的大流行世界中。

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.

重要的是要發展多種技能，并開始探索新的興趣，看看這些路在何處。 堅持只知道一件事是一種快速滅絕的方法。 技術使人們可以戴上許多帽子，并在我們的大多數服務中尋找一站式服務。 另外，人們已經對多個領域產生了興趣，珍惜并運用了它們。 甚至喬丹(Micheal Jordan)都打棒球，他是當時最偉大的籃球運動員。

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.

在不斷變化的數據科學領域中，在多個領域擁有多功能性和知識是關鍵，因為僅關注一個主題將無法持續。 例如，我對“環境”感興趣，并通過編程和講故事來展示我的技能，例如在我們當前的廢物管理系統等不經常討論的主題上。 我們的大多數興趣都可以應用到大多數領域，從而使我們能夠擴展到新的領域。

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!

我希望其中一些職業應用程序將幫助您作為數據科學家和您的職業學習并成長。 如果您還有其他職業/工作申請，請與他們聯系。 記住，明智地運用自己！