数据库代码编写_如何将您的职业转变为数据科学-即使您今天不编写代码。

阅读量：2520 次

发布时间：2019-05-11

本文共 14311 字，大约阅读时间需要 47 分钟。

数据库代码编写

by Sam Chow, PhD

由周星驰博士

如何将您的职业转变为数据科学-即使您今天不编写代码。 (How to transition your career into Data Science — even if you don’t code today.)

(Don’t have time to read it all? I’ve summarized the entire article in the conclusion section!)

(没有时间阅读所有内容吗？我在结论部分总结了整篇文章！)

During my PhD in Organizational Psychology I always found myself reading about Data Science — thinking, it’d be really cool to be a data scientist... But, at the time, I couldn’t write a lick of code, so I chalked it up as a dream I’d achieve in another lifetime.

在我的组织心理学博士学位我经常发现自己读过数据科学-思考，成为一名数据科学家真的很酷...但是，当时，我无法编写一小段代码，所以我将其写成粉笔作为一个梦想，我会在另一生中实现。

My worries about learning data science came in two forms: 1) Data Scientists code, I don’t, I’ll never become one, and 2) I’m doing a PhD in an unrelated topic, why throw it away?

我对学习数据科学的担心有两种形式：1)数据科学家编码，我没有，我永远也不会成为一名； 2)我正在一个不相关的主题上攻读博士学位，为什么要扔掉它？

It was around this time I was perusing LinkedIn and noticed an update, my friend who had been working as a recruiter for 5 years took a position at Google.

大约在这个时候，我正在仔细阅读LinkedIn，并注意到了一个更新，我的朋友已经担任了5年的招聘人员 ，就职于Google 。

Curious to learn the secrets of scoring a job as a software developer at Google without having a related degree from Stanford or MIT, I sat down with him for a coffee.

我很想知道没有在斯坦福大学或麻省理工学院获得相关学位的情况下获得在Google担任软件开发人员的工作的秘密，所以我和他坐下来喝咖啡。

I was excited.

我很兴奋。

Maybe this is where I could learn the secrets to a career transition into Data Science?!

也许在这里我可以学习到向数据科学职业过渡的秘密？

I brought along my favorite pen and notebook.

我带来了我最喜欢的笔和笔记本。

I expected to hear the equivalent of the “5 easy ways to become a Data Scientist at Google” and have a come to Jesus moment.

我希望能听到与“成为Google数据科学家的5种简便方法”等效的信息，并且有机会来到耶稣的时刻。

I heard no such thing.

我没听到这样的话。

His secret?

他的秘密？

He engaged with communities at freeCodeCamp and started with basic tutorials on how to code. He learned the basics of coding and then moved to building small projects. He further immersed himself in the community and began to build bigger and badder products. Two years later, and after committing a few hours a week, he was able to land his dream job at Google.

他在freeCodeCamp与社区互动，并从如何编码的基本教程开始。他学习了编码的基础知识 ，然后开始构建小型项目。 他进一步使自己沉浸在社区中，并开始制造更大或更差的产品 。两年后，在每周投入几个小时后，他得以在Google找到理想的工作。

I digress.

我离题了。

Instead of feeling defeated because there was no secret to transitioning my career, I felt inspired.

我没有因为没有过渡职业的秘密而感到失败，反而感到鼓舞。

So I made a contract to myself.

所以我给自己订了合同。

I’d commit 5 hours of my time a week to learn to code, to read about data science and to shut down the self-doubt I had about doing all this.

我每周将有5个小时的时间用于学习编码，阅读有关数据科学的知识，并关闭我所做的所有自我怀疑。

综上所述，以下是我成为数据科学家的过程中最有用的5个技巧。 (With all that being said, here are the 5 tips I found most helpful along my journey to becoming a Data Scientist.)

提示1：学会像数据科学家一样说话和思考 (Tip 1: Learn to speak and think like a Data Scientist)

The most important first step is to speak and think like a Data Scientist. What does that mean? First, learn how data scientists speak. What terms do they throw around frequently (e.g., scikitlearn, matrix-factorization, eigenvectors)? Don’t be afraid, just take notes on the words you don’t understand. Why? Learning the vocabulary is the first step in learning and communicating data science.

最重要的第一步是像数据科学家一样说话和思考。那是什么意思？首先，了解数据科学家如何说话。它们经常抛出哪些术语(例如scikitlearn，矩阵分解，特征向量)？不要害怕，只要对不懂的单词做笔记。为什么？学习词汇是学习和交流数据科学的第一步。

Learning data science vocabulary will optimize your learning rate (..get the pun..? ha..ha?)

学习数据科学词汇将优化您的学习率(..得到双关语..？ha..ha？)

By knowing the vocabulary, you’ll be able to utilize Google to its fullest extent. For example, if you’re learning about Principal Component Analysis (PCA)and you become frustrated because all the articles you’re reading are too technical, knowing that PCA is a dimension reduction technique and running a new Google search will net you entirely new results on a Google search.

通过了解这些词汇，您将可以充分利用Google。例如，如果您正在学习主成分分析(PCA)，并且由于阅读的所有文章过于技术性而感到沮丧，那么知道PCA是一种降维技术并运行新的Google搜索将使您完全不熟悉Google搜索结果。

Often times, your ability to learn is limited by what you don’t know (…confusing eh?).

通常，您的学习能力受到您不知道的知识的限制(……令人困惑吗？)。

Finding an article that is one level lower and less abstract is key to resolving the gaps in your knowledge.

找到低一级且抽象程度较低的文章是解决知识差距的关键。

Knowing multiple terms for the same technique/idea is key to developing a broad understanding of the topic.

了解相同技术/思想的多个术语是发展对该主题的广泛理解的关键。

Here’s a list of resources I used to learn how to speak like a Data Scientist:

这是我用来学习如何像数据科学家一样说话的资源列表：

: This was my most inspirational resource to becoming a Data Scientist. Redditors post cool apps they’ve built and talk about how they did it. Sometimes, they post their Github!
：这是我成为数据科学家时最有启发性的资源。 Redditor会发布他们构建的很棒的应用程序，并谈论它们的执行方式。有时，他们发布自己的Github！

: If you’ve spent anytime reading about Data Science, then you’ve most likely heard about Kaggle. The most important part of Kaggle to an aspiring Data Scientist is the “Kernals” section. Here, fellow Kaggler’s post their solutions to the problems posed by the competition. Spend at least an hour of your time, TYPING and CODING out their solution — practice typing each line, line-by-line in your own Jupyter Notebook. Run the code and see what happens (e.g., you’ll run into errors because you won’t have certain libraries or dependencies installed, which brings me to my third bullet).
：如果您花了任何时间阅读有关Data Science的内容，那么您很可能听说过Kaggle。对于一位有抱负的数据科学家而言，Kaggle最重要的部分是“内核”部分。在这里，Kaggler的同仁发布了解决竞赛所带来问题的解决方案。花费至少一个小时的时间，键入和编码他们的解决方案-练习在自己的Jupyter Notebook中逐行键入每一行。运行代码，看看会发生什么(例如，您将遇到错误，因为您没有安装某些库或依赖项，这使我想到了第三点)。

: Anytime you have a question about Data Science related questions, head to Stackoverflow and run a search. Learn how people ask questions about Data Science related questions. At this level, most likely all your questions have answers, so running a search should be sufficient.
：每当您对数据科学相关问题有疑问时，请前往Stackoverflow并运行搜索。了解人们如何提出有关数据科学相关问题的问题。在此级别上，很可能您所有的问题都有答案，因此运行搜索就足够了。

提示2：参与Kaggle挑战 (Tip 2: Engage in Kaggle Challenges)

I eluded to this a bit earlier but, learning by doing is ultimately the best way to learn. Spend time looking at the kernals in Kaggle competitions to learn from how other Kaggler’s approached the competition. At first, this will be extremely daunting, you won’t understand 95% of the code you’re reading, let alone, you probably won’t be able to run the code on your own computer even after you’ve cloned it.

我稍早不了解这一点，但是边做边学最终是最好的学习方法。花时间研究Kaggle比赛的核心，以了解其他Kaggler的比赛方法。刚开始，这将是非常艰巨的 ，您将无法理解正在阅读的代码的95％ ，更不用说， 即使克隆了代码 ， 也可能无法在自己的计算机上运行代码。

This is where you need to be persistent.

这是您需要坚持不懈的地方。

You aren’t going to learn anything if you get frustrated, so ease yourself into engaging with these challenges and soon enough you’ll be able to understand the kernals you read.

如果您感到沮丧，您将不会学任何东西，因此可以轻松应对这些挑战，并且您很快就能了解所阅读的核心内容。

Remember, when setting goals, be realistic about them (e.g., SMART goals): Specific, Measurable, Attainable, Realistic, Time-Bound (SMART).

记住，在设定目标时，要对它们现实(例如，SMART目标)：具体，可衡量，可实现，现实，有时间限制(SMART)。

In other words, don’t think you’ll be reading Kaggle kernals within a week. Give yourself a specific, realistic and time-bound goal —

换句话说，不要以为您会在一周内阅读Kaggle内核。给自己一个具体，现实和有时限的目标-

“I’ll be able to understand how train/test/split works by the end of this week”.

“到本周末，我将能够理解火车/测试/拆分的工作方式”。

Set small goals, write them down and check them off when you achieve them. When you feel frustrated, go back to these checkmarks and see how far you’ve come since yesterday.

设定小目标，写下来，并在达到目标时予以核对。当您感到沮丧时，请回到这些检查标记，看看您从昨天开始走了多远。

提示3：找到您自己的数据科学项目 (Tip 3: Find your own Data Science Project)

Find a project you’re passionate about, whether it be a problem you’d like to solve or a library you’d like to learn — turn this into a project that you’ll put onto your github as a portfolio piece.

查找一个您热衷的项目，无论是要解决的问题还是要学习的库，都可以将其变成一个项目，并将其作为项目组合放到github上。

Finding a problem is best done through conversations. Engage with your community, your friends or… Even strangers. Find out what bothers them, or talk to them about ideas you’ve always had.

找到问题最好通过对话来完成。与您的社区，朋友或……甚至是陌生人互动。找出困扰他们的原因，或与他们讨论您一直以来的想法。

Hash out your idea, make it simple. Your project isn’t going to change the world. The most important part here is to start on one.

提出您的想法，使其变得简单。您的项目不会改变世界。这里最重要的部分是从一个开始。

Once you find your idea you’d like to build, tell a friend or make an open commitment to your community (e.g., Medium blog) that you’ll be building it. Most importantly, highlight the features of your app and the time it’ll take for you to have it done (e.g., 1 month to build this app out).

一旦找到您的想法，您将要建立，告诉朋友或对您的社区(例如，Medium博客)做出公开承诺，将要建立它。最重要的是，突出显示应用程序的功能以及完成该过程所需的时间(例如，开发此应用程序需要1个月的时间)。

I relied on my commitment to my peer group to build this that generates revenue today.

我依靠对同龄人小组的承诺来构建这个，该今天可以产生收入。

Read about how I built it on Medium here:

在这里阅读有关我如何在Medium上：

If you’re looking for a project, I come across companies looking for pro-bono work all the time. Connect with me on and I’ll find the best project for your goals!

如果您正在寻找一个项目，我会遇到很多一直在寻找无偿工作的公司。在与我联系，我将为您的目标找到最好的项目！

提示4：申请数据科学工作 (Tip 4: Apply for Data Science Jobs)

Wait… wait... what?!

等待...等等...什么？！

You’re probably thinking that you just started learning Data Science and now I’m telling you to apply to jobs. What the…?!

您可能以为您刚刚开始学习数据科学，现在我告诉您申请工作。什么...？！

If you’re looking to score a Data Science job, you need to learn how to interview as a Data Scientist.

如果您想为数据科学工作评分，则需要学习如何以数据科学家的身份进行面试。

Yes… It’s a skill. The recruitment and selection of Data Scientists today isn’t great, and you’ll have to learn how get good at interviewing before you can land a job, so begin ASAP.

是的，这是一种技能。如今，数据科学家的招聘和选拔情况并不理想， 您必须先学习如何擅长面试才能找到工作，因此请尽快开始。

IMO, you’re ready to interview when you can speak like a data scientist.

IMO，当您像数据科学家一样说话时，您就准备好面试了。

Don’t worry about live coding assessments or Data Science related questions just yet, you’re 99% likely to fail these interviews at this point

暂时不必担心实时编码评估或与数据科学相关的问题，此时您有99％的可能性不及格这些面试

You want to fail.

你想失败。

You want to take notes on the questions you get.

您想记下所遇到的问题。

You want to learn how interviews are conducted.

您想了解采访的进行方式。

Your goal here is to get good at interviewing, because you’re still far out from landing a job given your competencies in Data Science.

您在这里的目标是要擅长于面试，因为鉴于您在数据科学方面的能力，您离找到一份工作还很遥远。

Before you comment about this tip, hear me out!

在您对本技巧发表评论之前，请先听我说！

You should be speaking like a data scientist within 6 months of spending 5 hours a week. You’re still probably 1.5 years away from landing a job as a data scientist. Applying to jobs now gives you an understanding of how the entire process works. You’ll learn what you need to learn to interview well later on. Further, you aren’t burning any bridges.

您应该在每周花费5个小时的6个月内像数据科学家一样说话。您距离从事数据科学家的工作还差1.5年。现在，应用到作业可以使您了解整个过程的工作方式。您将学到以后面试所需的知识。此外，您不会烧毁任何桥梁。

Re-applying to a job where you failed an interview is often flattering to an interviewer especially if you come back and are 100x better than you were the year before.

重新面试失败的工作通常会使面试官感到受宠若惊，特别是如果您回来并比前一年好100倍。

For instance, during one of my first interviews for a data science position, I was asked “What is a negative R-Squared?”. I thought… Wtf? R-Squared can’t be negative. So I proceeded to ramble to the interviewer about what I thought it was.

例如，在我第一次访问数据科学职位时，有人问我“什么是负R平方？” 。我以为... WTF？ R平方不能为负。因此，我开始向面试官说说我的想法。

Fast forward 2 years later, I received the same question from another interviewer.

快进2年后，我从另一个面试官那里收到了同样的问题。

Because I had looked up the answer 2 years back, I was able to smirk and provide an answer.

因为两年前我一直在寻找答案，所以我可以假笑并提供答案。

The interviewer later told me that he’d never heard an answer that was so succinctly and clearly communicated.

采访者后来告诉我，他从未听过如此简洁明了的答案。

Having your own project will motivate you to complete it as it can sit in your portfolio and show potential companies your competencies.

拥有自己的项目将激励您完成它，因为它可以放在您的投资组合中并向潜在的公司展示您的能力。

提示5：网络…网络…以及网络 (Tip 5: Network…Network… And Network)

Your chances of scoring a job as a data scientist improves exponentially based on your network size.

根据您的网络规模，您获得数据科学家职位的机会成倍增加。

In-person networking is the best way to expand your network in a meaningful way, however, it’s not always possible to make it out to networking events.

面对面的联网是以有意义的方式扩展网络的最佳方法，但是，并非总是可以将其用于联网事件。

The second best scenario is LinkedIn.

第二好的场景是LinkedIn。

Creating meaningful connections on LinkedIn is as simple as finding people in your industry, sending them a message and keeping up to date with their happenings.

在LinkedIn上创建有意义的联系就像在您的行业中找到人，向他们发送消息并及时了解他们的最新动态一样简单。

The crucial piece people miss about LinkedIn opportunities is that they don’t let others know they are open to opportunities.

人们错过LinkedIn机会的关键在于，他们不会让其他人知道他们对机会持开放态度。

I found great success by adding: “Open to new opportunities” on my LinkedIn title.

通过在LinkedIn标题上添加“打开新机遇”，我获得了巨大的成功。

Further, Medium supports a great network of Data Scientists that, I’m sure would be happy to connect. Read a cool article? Find the author on LinkedIn and chat with them about prospective opportunities!

此外，Medium支持庞大的数据科学家网络，我相信很高兴能与他们联系。阅读很酷的文章？在LinkedIn上找到作者，并与他们聊天，探讨潜在机会！

I work as a Data Science consultant and come across many opportunities (mostly in Toronto). Let me know who you are on and I’d be happy to connect you to a company that is interested!

我是一名数据科学顾问，并且遇到很多机会(大部分在多伦多)。让我知道您在上的身份，很高兴将您与感兴趣的公司联系起来！

Find me here:

在这里找到我：