vue取數(shù)據(jù)第一個(gè)數(shù)據(jù)

A lot.

很多。

I landed my first job as a Data Scientist at the beginning of August, and like any new job, there’s a lot of information to take in at once.

我于8月初找到了數(shù)據(jù)科學(xué)家的第一份工作，并且像任何新工作一樣，一次有很多信息需要接受。

By documenting and sharing my own thoughts, hopefully those that are aspiring to work as a Data Scientist (or in anything data-related) can find this helpful in the future. Of course, each company and workplace is different, but I’d like to think that these tips can be useful to many people in general.

通過記錄和分享我自己的想法，希望那些希望成為數(shù)據(jù)科學(xué)家(或從事與數(shù)據(jù)相關(guān)的工作)的人將來能對(duì)您有所幫助。 當(dāng)然，每個(gè)公司和工作場(chǎng)所都是不同的，但是我想這些技巧通常對(duì)許多人有用。

遇見盡可能多的人 (Meet as many people as possible)

Photo by bantersnaps on Unsplash照片由bantersnaps在Unsplash上拍攝

This applies to a lot of other roles, but I feel like this is particularly important when working with data.

這也適用于許多其他角色，但是我覺得這在處理數(shù)據(jù)時(shí)特別重要。

The more people you know, the easier it is for you to do your job.

您認(rèn)識(shí)的人越多，就越容易完成工作。

There’s no better time to meet people than at the start where you have the excuse of introducing yourself. By expanding your reach within the company, there’s more potential for you to find the data that you might need for analysis in the future.

沒有比在開始時(shí)介紹自己的借口更好的時(shí)間與人見面了。 通過擴(kuò)大公司的業(yè)務(wù)范圍，您就有更多的潛力來查找將來可能需要進(jìn)行分析的數(shù)據(jù)。

This is especially true if the data is not well-managed. Even if your team has a clean and dedicated data warehouse, there’s bound to be a moment where you’ll need something but not be able to find it without the help of someone more familiar with the data than you are.

如果數(shù)據(jù)管理不當(dāng)，尤其如此。 即使您的團(tuán)隊(duì)有干凈整潔的數(shù)據(jù)倉庫，也一定會(huì)有一會(huì)兒您需要一些東西，但是如果沒有比您更熟悉數(shù)據(jù)的人的幫助，便無法找到它們。

定期記筆記 (Take notes regularly)

Photo by JESHOOTS.COM on Unsplash JESHOOTS.COM在Unsplash上的照片

Personally, I think this is a habit that’s worth having throughout your career.

就個(gè)人而言，我認(rèn)為這是一個(gè)在整個(gè)職業(yè)生涯中都值得擁有的習(xí)慣。

By regularly taking notes, you’ll have something to refer back to in the future if you forget something — and at the beginning, you will end up forgetting things.

通過定期記筆記，如果您忘記了某些內(nèi)容，將來您將有一些需要參考的地方–開始時(shí)，您最終會(huì)忘記一些東西。

Developing this habit early means that you won’t have to awkwardly ask for something in the future when you know you should have remembered it by then.

早日養(yǎng)成這種習(xí)慣，意味著當(dāng)您知道屆時(shí)應(yīng)該已經(jīng)記住它時(shí)，您將來就不必笨拙地要求一些東西。

It’s also a good way to keep track of what people are currently doing or using (e.g. what data do they use etc.) and lets you document the location of things that might potentially be useful to you in the future.

這也是跟蹤人們當(dāng)前在做什么或正在使用的好方法(例如，他們使用什么數(shù)據(jù)等)，并讓您記錄將來可能對(duì)您有用的事物的位置。

Speaking of note-taking, I’d recommend using Notion. It’s served me well during my student days for documenting my own projects and ideas, and has transitioned easily over to my working career.

說到筆記，我建議使用Notion 。 在學(xué)生時(shí)期記錄自己的項(xiàng)目和想法對(duì)我很有幫助，并且可以輕松地過渡到我的工作生涯。

提前集思廣益 (Brainstorm ideas ahead of time)

Per L??v on PerL??v攝于UnsplashUnsplash

This follows on from the previous section: start jotting down ideas as you’re getting more familiar with the data — even if they might seem unreasonable for now.

這是從上一節(jié)開始的：隨著對(duì)數(shù)據(jù)的熟悉程度的增加，開始記下想法，即使目前看來這些想法并不合理。

There have been times where I’ve had an idea about solving a particular problem but then forget about it later because I didn’t write it down. If you’re finally tasked to solve that same problem, you’d have to spend time coming up with the same idea again!

有時(shí)候我對(duì)解決一個(gè)特定的問題有個(gè)主意，但是后來我忘了，因?yàn)槲覜]有寫下來。 如果您最終被要求解決相同的問題，那么您將不得不花費(fèi)時(shí)間再次提出相同的想法！

Documenting your ideas also lets you improve on them over time as you become more familiar with everything. When someone presents to you a new problem to solve, you might already have a good idea on how to solve it, thus making your job easier in the long run.

記錄您的想法還可以使您隨著時(shí)間的流逝對(duì)它們的熟悉程度不斷提高。 當(dāng)有人向您提出要解決的新問題時(shí)，您可能已經(jīng)對(duì)如何解決有個(gè)好主意，從長(zhǎng)遠(yuǎn)來看，這使您的工作變得更輕松。

不要過于復(fù)雜 (Don’t overcomplicate things)

Photo by Antoine Dautry on Unsplash Antoine Dautry在Unsplash上的照片

With the hype surrounding machine learning these days, it’s quite easy to fall into the trap of overcomplicating a problem that could be solved with a simple linear or logistic regression.

如今隨著圍繞機(jī)器學(xué)習(xí)的炒作，很容易陷入使問題復(fù)雜化的陷阱，而該問題可以通過簡(jiǎn)單的線性或邏輯回歸來解決。

In some cases, the required infrastructure for a complex machine learning pipeline might not even be available.

在某些情況下，復(fù)雜的機(jī)器學(xué)習(xí)管道所需的基礎(chǔ)架構(gòu)甚至可能不可用。

Most data science problems are statistical ones that require you to think more like a statistician than a machine learning engineer.

大多數(shù)數(shù)據(jù)科學(xué)問題都是統(tǒng)計(jì)問題，需要您像統(tǒng)計(jì)學(xué)家一樣思考而不是機(jī)器學(xué)習(xí)工程師。

That means starting with the usual: What does the distribution of the data look like? What sort of model would best fit this kind of distribution? And if so, does the data satisfy the statistical assumptions of the model? Do I need to remove any data if it doesn’t satisfy my assumptions? (e.g. multicollinearity).

這意味著從通常的情況開始：數(shù)據(jù)的分布是什么樣的？ 哪種模型最適合這種分布？ 如果是這樣，數(shù)據(jù)是否滿足模型的統(tǒng)計(jì)假設(shè)？ 如果數(shù)據(jù)不符合我的假設(shè)，是否需要?jiǎng)h除？ (例如多重共線性)。

From here, if it seems reasonable, a machine learning algorithm and/or pipeline could be considered. However, the more complicated the solution becomes, the harder it is to explain and justify your results to the decision makers. Try explaining how neural networks work to a non-mathematical audience, and you’ll find that it’s a very difficult thing to do.

從這里開始，如果看起來合理，則可以考慮使用機(jī)器學(xué)習(xí)算法和/或管道。 但是，解決方案越復(fù)雜，就很難向決策者解釋和證明您的結(jié)果。 嘗試向非數(shù)學(xué)對(duì)象解釋神經(jīng)網(wǎng)絡(luò)的工作原理，您會(huì)發(fā)現(xiàn)這是一件非常困難的事情。

If it provides actionable insight and the evidence can be communicated clearly to the audience, then I think that’s a job well done.

如果它提供了可行的見解并且可以將證據(jù)清楚地傳達(dá)給聽眾，那么我認(rèn)為這是一項(xiàng)出色的工作。

不要為解決一切感到壓力 (Don’t feel pressured to solve everything)

Photo by Christian Erfurt on Unsplash 克里斯蒂安·愛爾福特在Unsplash上的照片

Although we’re hired to solve problems, there will always be times where it simply isn’t possible to go any further. It could be due to a lack of (usable) data, or that the solution takes too long to implement.

盡管我們被雇用來解決問題，但總有一些時(shí)候根本無法進(jìn)一步解決問題。 可能是由于缺少(可用)數(shù)據(jù)，或者解決方案實(shí)施時(shí)間過長(zhǎng)。

Whatever the reason is, it’s sometimes better to put it in the backburner and move on to something that can be solved. Most of the time, completing a single task is better than not completing any tasks at all.

不管是什么原因，有時(shí)最好將其放回爐中，然后繼續(xù)進(jìn)行可以解決的問題。 在大多數(shù)情況下，完成一項(xiàng)任務(wù)比根本不完成任何任務(wù)要好。

最后-犯錯(cuò)誤并從中學(xué)到快樂！ (And lastly — make mistakes and have fun learning!)

Photo by Doran Erickson on Unsplash 多蘭·埃里克森 ( Doran Erickson)在Unsplash上拍攝的照片

Imposter syndrome is real, and it can sometimes feel a bit overwhelming when expectations are high.

冒名頂替綜合癥是真實(shí)的，當(dāng)期望值很高時(shí)，有時(shí)會(huì)感到有些不知所措。

Don’t be afraid to make mistakes, especially at the beginning of your career. Instead, focus on making fewer mistakes over time. It’s only natural that as you progress, fewer and fewer mistakes will be tolerated, so make the most of it at the beginning where you have an excuse to.

不要害怕犯錯(cuò)誤，尤其是在您的職業(yè)生涯初期。 相反，應(yīng)著重于隨著時(shí)間的流逝減少錯(cuò)誤。 很自然，隨著您的進(jìn)步，越來越少的錯(cuò)誤會(huì)被容忍，因此在您有借口的一開始就充分利用它。

And finally —you might feel like you should know how to solve every problem and provide amazing insights at the beginning; however, now’s the perfect opportunity to learn more about the industry instead.

最后，您可能會(huì)覺得自己應(yīng)該知道如何解決每個(gè)問題并在一開始就提供驚人的見解； 但是，現(xiàn)在是了解該行業(yè)的絕佳機(jī)會(huì)。

Take the time to explore how certain data science techniques could be applied to solving your own business problems. I’ve noticed that I’m more motivated to read and explore other potential solutions since I now have a good reason to. The biggest motivator for me though, is realising that after all these years of hard studying, I’m finally getting paid for it!

花時(shí)間探索如何將某些數(shù)據(jù)科學(xué)技術(shù)應(yīng)用于解決您自己的業(yè)務(wù)問題。 我注意到，由于我現(xiàn)在有充分的理由，因此我更加有動(dòng)力去閱讀和探索其他潛在的解決方案。 但是，對(duì)我而言，最大的動(dòng)力是意識(shí)到經(jīng)過多年的努力學(xué)習(xí)，我終于為此獲得了報(bào)酬！

翻譯自: https://towardsdatascience.com/my-first-month-as-a-data-scientist-454b44aaef91

vue取數(shù)據(jù)第一個(gè)數(shù)據(jù)

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