数据科学家美国工作待遇_如何获得数据科学工作

数据科学家美国工作待遇

You’ve done it. You just spent months learning how to analyze data and make predictions. You’re now able to go from raw data to well structured insights in a matter of hours. After all that effort, you feel like it’s time to take the next step, and get your first data science job.

您已经完成了。 您只花了几个月的时间学习如何分析数据并做出预测。 现在，您只需几个小时即可从原始数据获取结构合理的见解。 经过所有这些努力，您感觉是时候采取下一步并获得第一份数据科学工作了。

Unfortunately for you, this is where the process starts to get much harder. There’s no clear path to go from having data science skills to getting a data science job. You’ll need to put in a lot of hard work to forge your own.

对于您来说不幸的是，这是过程开始变得困难得多的地方。 从拥有数据科学技能到获得数据科学工作没有明确的道路。 您需要付出很多努力才能打造自己的作品。

But don’t give up hope! Getting a data science job after learning on your own is very possible. In this post, we’ll discuss the things you should be doing to put yourself in position to start getting data science interviews. In a subsequent post, we’ll cover the interview process itself, and how to prepare.

但是不要放弃希望！ 自己学习后就可以从事数据科学工作。 在这篇文章中，我们将讨论您应该做的事情以使自己处于开始接受数据科学面试的位置。 在后续文章中，我们将介绍面试过程本身以及准备方法。

If you feel like your data science skills aren’t yet well developed enough to start looking for a job, you might want to check out our post on how to learn data science, or go to Dataquest and start one of our data science learning paths.

如果您觉得自己的数据科学技能还不够完善，无法开始寻找工作，那么您可能想看看我们关于如何学习数据科学的文章 ，或者去Dataquest并开始我们的数据科学学习道路之一。

1.通过建设项目不断学习 (1. Keep Learning By Building Projects)

This may seem counterintuitive, as you only have so many hours per day, and you probably want to spend all of them on looking for a job. But think about it this way instead – every data scientist’s primary responsiblity is to learn. New tools are constantly coming out, and what skills are defined as “data science skills” is constantly changing. By learning, you stay on top of these skills, and improve your desirability to employers.

这可能似乎违反直觉，因为您每天只有几个小时，并且您可能想将所有时间都花在找工作上。 但是，请以这种方式进行思考-每个数据科学家的主要职责是学习。 新工具不断涌现，定义为“数据科学技能”的技能也在不断变化。 通过学习，您可以掌握这些技能，并提高对雇主的期望。

I’d highly suggest spending a majority of your time on learning. In order to set yourself up for getting a job, you’ll want to learning by working on projects. Projects are what you’ll be creating in a data science job, and building them on your own is a good way to practice. Projects also help you improve your portfolio, and boost your odds of scoring an interview.

我强烈建议您将大部分时间都花在学习上。 为了使自己做好工作，您需要通过从事项目来学习。 项目是您在数据科学工作中将要创建的项目，而自己构建项目是实践的好方法。 项目还可以帮助您改善投资组合，并提高获得面试得分的几率。

Creating a project can follow this rough process:

创建项目可以遵循以下大致过程：

• Find an interesting dataset. You can consult this list of places to find datasets.
• Formulate a few questions you want to use the dataset to answer. Pull in any supplementary datasets you want to use.
  • Make sure at least one or two of these questions push the boundaries of your knowledge, or force you to learn new tools.
• Use Jupyter Notebook or an equivalent tool to explore and analyze the data.
• Store your notebooks on Github.

• 找到一个有趣的数据集。 您可以查阅此位置列表以查找数据集 。
• 提出一些您想使用数据集回答的问题。 提取您要使用的任何补充数据集。
  • 确保这些问题中的至少一个或两个问题超越了您的知识范围，或迫使您学习新工具。
• 使用Jupyter Notebook或等效工具来浏览和分析数据。
• 将您的笔记本存放在Github上 。

If you need more guidance, see this post and this post for assistance in formulating and creating these projects.

如果您需要更多指导，请参阅此帖子和此帖子，以帮助您制定和创建这些项目。

As you learn, make sure to pay special attention to the following topics, which tend to be underemphasized in most data science teaching materials:

在学习过程中，请确保特别注意以下主题，而这些主题在大多数数据科学教材中往往没有被强调：

• Data Cleaning – being able to clean data is a critical skill, and will be most of your job. You can take a Dataquest data cleaning course to help you with this.
• Statistics – a good part of being a data scientist is being rigorous, which involves statistics. You can take a Dataquest statistics course to help you with this.
• End To End – going from a raw dataset to a rigorous analysis is a very important skill.

• 数据清理–能够清理数据是一项关键技能，这将是您的大部分工作。 您可以参加Dataquest数据清理课程来帮助您。
• 统计–严格执行数据科学家的工作，其中涉及统计。 您可以参加Dataquest统计课程以帮助您解决此问题。
• 端到端–从原始数据集到严格的分析是一项非常重要的技能。

2.炫耀你的作品 (2. Show Off Your Work)

As you’ve learned data science, you’ve hopefully been building up a portfolio of projects. These projects will ideally be authored in Jupyter Notebook or an equivalent tool.

当您学习数据科学时，希望您已经建立了一个项目组合。 理想情况下，这些项目将在Jupyter Notebook或等效工具中编写。

Projects are a great way to practice your skills, but it’s when you show them off that they become extremely valuable in your job search. By posting your portfolio publicly, you raise your profile, and allow other people to see your work and skills.

项目是练习技能的好方法，但是当您向他们炫耀时，它们在求职中变得非常有价值。 通过公开发布您的作品集，您可以提高个人资料，并允许其他人看到您的工作和技能。

Github (Github)

The quickest way to share your work is to create a Github account and start uploading your notebooks to their own repositories. Here are some example project repositories:

共享工作的最快方法是创建一个Github帐户，然后开始将笔记本上传到自己的存储库。 以下是一些示例项目存储库：

As you can see, both projects have a README file that clearly explains what they do, and how to use them. If you upload a Jupyter Notebook to Github, like this example, it will automatically be rendered in the interface, making uploading notebooks a great way to show off your work.

如您所见，两个项目都有一个README文件，该文件清楚地说明了它们的作用以及如何使用它们。 如果将Jupyter笔记本上载到Github（如本例所示） ，它将自动在界面中呈现，这使上载笔记本成为炫耀您的作品的好方法。

You should aim to have 5-10 projects on Github as you embark on your job search.

着手寻找工作时，您应该在Github上拥有5-10个项目。

建立网志 (Creating A Blog)

A slower, but more rewarding, way to show off your work is to create a blog post about it. A blog post will typically describe how you created the project, and why you took certain steps. Here are some good project walkthrough blog posts:

展示您的作品的一种较慢但更有意义的方法是创建有关它的博客文章。 博客文章通常会描述您如何创建项目以及为什么要执行某些步骤。 以下是一些不错的项目演练博客文章：

In order to create posts like the above, you’ll need to make your own blog. You can read a good tutorial on making your own data science blog here.

为了创建上述内容，您需要创建自己的博客。 您可以在此处阅读有关制作自己的数据科学博客的优秀教程。

You should aim to have 5 or so blog posts as you embark on your job search.

在开始求职时，您的目标应该是拥有5篇左右的博客文章。

3.分享您的工作 (3. Share Your Work)

As you raise your profile in the data science world, you’ll find it easier to access new opportunities. One way to raise your profile is to share your work with others in relevant contexts. There are quite a few data science and programming communities that appreciate tutorial or project walkthrough blog posts:

随着您在数据科学界的知名度不断提高，您会发现获得新机遇的机会更加轻松。 提高个人形象的一种方法是在相关情况下与他人共享您的作品。 有很多数据科学和编程社区喜欢教程或项目演练博客文章：

Before you submit your article to any of these communities, make sure you engage in the community, and understand what types of content they respond well to. This will generally involve reading articles in the community for a few weeks, and making relevant comments.

在将您的文章提交到这些社区中的任何一个之前，请确保您参与了该社区，并了解他们对哪种类型的内容React良好。 通常，这将涉及在社区中阅读文章数周并发表相关评论。

By sharing your work, you’ll get traffic for your blog, which can result in:

通过共享您的工作，您将获得博客流量，这可能导致：

• Suggestions for improving your work.
• Direct leads from recruiters.
• Direct opportunities from companies and community members.
• A sense of the data science market, and who’s hiring.

• 有关改善工作的建议。
• 招聘人员的直接领导。
• 公司和社区成员的直接机会。
• 对数据科学市场的了解以及正在招聘的人。

4.网络 (4. Network)

Networking is a great way to get inbound interest from companies, and to discover any holes in your skills. You should primarily network through online communities, meetups, and coffee meetings.

联网是一种吸引公司入站兴趣并发现您的技能漏洞的好方法。 您应该首先通过在线社区，聚会和咖啡会议建立网络。

在线社区 (Online Communities)

As part of sharing your work, you should be engaging in online communities. Make sure to read articles in those communities to get a sense of what data science trends are. Also make sure to comment regularly so you can share your viewpoint and meet other people.

作为共享工作的一部分，您应该参与在线社区。 确保阅读这些社区中的文章，以了解什么是数据科学趋势。 另外，请确保定期发表评论，以便您分享自己的观点并结识其他人。

It’s also common to create posts to ask for advice from others. Here are some examples of useful posts from data science communities:

创建帖子以征询他人的建议也是很常见的。 以下是来自数据科学界的有用帖子的一些示例：

As you engage with the communities more, you’ll learn more about the data science world, and be in a better position to get a job.

随着您与社区的互动越来越多，您将了解有关数据科学领域的更多信息，并且更有能力找到工作。

聚会 (Meetups)

Meetups can be a good way to meet a lot of data scientists at once, and to hear interesting talks. You can browse Meetups in your city here. Searching for keywords related to what you’re interested in, like “data science”, “data visualization”, “Python”, or “R” will be helpful.

聚会可以是一次与众多数据科学家见面并聆听有趣的演讲的好方法。 您可以在此处浏览您所在城市的聚会。 搜索与您感兴趣的主题相关的关键字，例如“数据科学”，“数据可视化”，“ Python”或“ R”将很有帮助。

Some meetups are sponsored by companies that are hiring data scientists, and thus can be a good way to find opportunities. Be careful to not just listen to talks at meetups – interact with people around you, and talk to them. You’d be surprised at the opportunities that can come out of casual interactions at these events. Make sure not to see these casual interactions as pure networking opportunities, though. If you can help someone else out, you’ll be able to build a more authentic, two-way relationship.

一些聚会是由雇用数据科学家的公司赞助的，因此可能是寻找机会的好方法。 注意不仅要听见聚会上的谈话，还要与周围的人互动并与他们交谈。 您会惊讶于这些事件中偶然的互动带来的机会。 但是，请确保不要将这些偶然的交互视为纯粹的联网机会。 如果您可以帮助其他人，那么您将能够建立更真实的双向关系。

咖啡会议 (Coffee Meetings)

Try to reach out to 1-2 people in your area every week for coffee meetings. You can find people on LinkedIn or AngelList by searching for data scientists in your city. You can also find data scientists on Twitter, or through popular data science blogs.

每周尝试与您所在地区的1-2个人进行咖啡聚会。 您可以通过搜索所在城市的数据科学家来在LinkedIn或AngelList上找到人。 您还可以在Twitter或流行的数据科学博客上找到数据科学家。

Don’t worry too much about only networking with managers, or only people who’re in charge of hiring. Often the best referrals come from your peers, so focus on networking with other data scientists. It’s also a good idea to build a relationship with folks by commenting on their blog, chatting with them over Twitter, or commenting on their Quora answers before reaching out.

不必太担心仅与管理人员或负责招聘的人员建立联系。 通常，最好的推荐来自您的同龄人，因此请专注于与其他数据科学家建立网络。 通过在博客上发表评论，在Twitter上与他们聊天或在Quora答案上发表评论来与人们建立关系也是一个好主意。

Once you’re ready to email, a great way to reach out to folks is to say something along the lines of this:

准备好发送电子邮件后，与人们建立联系的一种好方法是按照以下方式发表意见：

Hi! I’ve been learning data science, and I’m looking for a job now. I’d love some advice if you have time to get coffee. I’ve been following you online, and I love your [INSERT SOME OF THEIR WORK HERE].

嗨！ 我一直在学习数据科学，现在正在寻找工作。 如果您有时间喝咖啡，我会建议您。 我一直在网上关注您，我喜欢您的[在此处插入某些图片]。

I’m specifically interested in [INSERT QUESTIONS HERE]. I’m happy to travel to where your office is. [INSERT TIMES HERE] work for me, but I’m happy to accomodate your schedule if these don’t work for you.

我对[在此插入问题]特别感兴趣。 我很高兴前往您的办公室。 [在这里插入时间]对我有用，但是如果这些都不对您有用，我很乐意为您安排时间表。

Some good questions to ask are:

要问的一些好问题是：

• Anything about the background of the person you’re connecting with, and how they got started.
• Questions about the work the person is doing now, and what their day to day is like.
• Questions about sector-specific data science applications. Example: “What are some of the most interesting data science applications in education?”
• Questions about openings at the employer of the person you’re meeting with, and if they’re hiring now.
• “Can I help you with anything?” – believe it or not, you probably have skills and experiences that the person you’re meeting with doesn’t. If you can help them with something, you’ll build a stronger, more authentic, relationship.

• 有关与您联系的人的背景以及他们如何开始的任何事情。
• 有关此人现在正在做的工作以及他们的日常状况的问题。
• 有关特定部门的数据科学应用程序的问题。 示例：“在教育中最有趣的数据科学应用是什么？”
• 有关您遇到的人的雇主的职位空缺以及他们现在是否在招聘的问题。
• “我能帮您什么吗？” –信不信由你，你可能拥有与遇见的人所没有的技能和经验。 如果您可以帮助他们做一些事情，那么您将建立起更牢固，更真实的关系。

Some bad questions to ask are:

要问的一些坏问题是：

• “How can I get a job?” – the more focused the question, the better. A better version of this question would be “I’ve created projects that cover random forests, data cleaning, and visualization. What other skills is your employer looking for in new hires? What skills should I work on next?”
• “Can you review my portfolio?” – reviewing a portfolio and giving good feedback can take a lot of time, and you only want to ask this after you’ve built a good relationship.
• “How do I do this thing in R?” – focus on high-level career advice to maximize your time.

• “我怎么能找到工作？” –问题越集中，越好。 这个问题的一个更好的版本是“我创建了涵盖随机森林，数据清理和可视化的项目。 您的雇主正在寻找新的其他技能吗？ 接下来我应该学习什么技能？”
• “您可以查看我的投资组合吗？” –审查投资组合并提供良好的反馈可能会花费很多时间，并且您只想在建立良好的关系后问这个问题。
• “我如何在R中执行此操作？” –专注于高级职业建议，以最大限度地利用您的时间。

You’ll probably get one of 4 responses to outreach like this:

您可能会收到以下4针对外联的回复之一：

• Silence. If you get this response, you can follow up once, but if you still don’t get a response, it’s best to move on. Not everyone has time for these meetings, or is receptive to them.
• “Sorry, but I don’t have time right now”. This is a common response, and that’s fine. Thank them for their time, and move on.
• “I don’t have time to meet in person, but I’d love to do a call and/or answer more questions over email”. This is a good response, and you should follow up as soon as you can.
• “I’d love to meet in person” – Awesome! You’ve just scored a valuable in person meeting. Be careful to ask focused, directed questions and maximize your time.

• 安静。 如果收到此回复，则可以跟进一次，但是如果仍然没有收到回复，则最好继续进行下去。 不是每个人都有时间参加这些会议，还是愿意接受这些会议。
• “对不起，但我现在没有时间”。 这是常见的回答，没关系。 感谢他们的时间，继续前进。
• “我没有时间亲自见面，但我想打个电话和/或通过电子邮件回答更多问题”。 这是一个很好的回应，您应该尽快跟进。
• “我很想见面” –太棒了！ 您刚刚在面对面会议中取得了宝贵的成绩。 请小心地提出有针对性的，有针对性的问题，并尽可能多地利用您的时间。

5.建立在线状态 (5. Establish An Online Presence)

In order to get inbound leads, you’ll want to establish an online presence. At the least, you should have a presence on the following sites:

为了获得入站线索，您需要建立在线形象。 至少，您应该在以下站点上进行展示：

Make sure your profiles in each one are up-to-date, have links to your projects, and have all of your relevant experience.

确保您每个人的个人资料都是最新的，具有指向您的项目的链接，并具有所有相关经验。

Some sites that you may also want to consider establishing a presence on are:

您可能还需要考虑在其上建立状态的一些站点是：

• Twitter – by following data scientists and chatting with them, you can learn more about the field.
• Quora – by following data science topics and answering questions, you can raise your profile.

• Twitter –通过关注数据科学家并与他们聊天，您可以了解更多有关该领域的信息。
• Quora –通过关注数据科学主题并回答问题，您可以提高自己的形象。

6.选择性地向公司发送消息 (6. Selectively Message Companies)

So far, all of our focus has been on getting inbound leads and opportunities. You’ll also want to selectively pursue outbound opportunities. LinkedIn and AngelList make it easy to indicate interest in hundreds of companies, but you’ll want to avoid this strategy. It results in a lot of wasted time and effort, which actually makes it harder to get a job.

到目前为止，我们所有的注意力都集中在获得入站线索和机会上。 您还需要选择性地寻求出站机会。 使用LinkedIn和AngelList可以很容易地表明对数百家公司的兴趣，但是您要避免使用这种策略。 这导致浪费大量时间和精力，实际上使找工作变得更加困难。

Instead, you’ll want to be extremely selective:

相反，您将需要非常有选择性：

1. Figure out what kinds of companies you’re interested in. Hopefully, from your networking, you should have a good picture of what data science work is like at different types of companies in different sectors. If you’re really passionate about healthcare, and like to work at smaller companies, you should only look at smaller health companies.
2. Find the interesting companies in the sector you’re interested in. You can search AngelList for this. Create a list of 20-30 companies that you think are interesting.
3. Optimize your online presence and portfolio for your chosen sector(s). For example, if you care about healthcare, you should write healthcare-related blog posts, like this one.
4. Network with data scientists at companies you find interesting. See if you can get coffee meetings with data scientists at the companies you’re interested in.
5. Apply to jobs at the companies you’re interested in. Make sure to write an email to the hiring manager (if you can find the email) describing your specific passion for this role, and why you’d be a good fit. If you’ve networked well, you’ll be able to get a referral as well.

1. 找出您感兴趣的公司类型。希望，通过您的网络，您应该对不同行业中不同类型公司的数据科学工作有一个很好的了解。 如果您真的对医疗保健充满热情，并且喜欢在较小的公司工作，则应该只看较小的健康公司。
2. 在您感兴趣的行业中找到有趣的公司。您可以搜索AngelList。 创建一个20-30家您认为有趣的公司的列表。
3. 优化您所选部门的在线业务和投资组合。 例如，如果您关心医疗保健，则应撰写与医疗保健相关的博客文章，例如此类 。
4. 与您发现有趣的公司的数据科学家建立联系。 看看您是否可以与您感兴趣的公司的数据科学家见面。
5. 申请您​​感兴趣的公司的职位。请确保给招聘经理写一封电子邮件（如果您可以找到该电子邮件），描述您对此职位的特殊热情以及适合的原因。 如果您的网络关系良好，那么您也可以获得推荐。

If after going through the 20 – 30 companies, you don’t get any interviews, you may want to get some specific advice and change your approach. Coffee meetings can be a good way to get this advice. If you selectively message companies, but don’t get an interview, some of the most common reasons will be:

如果经过20到30家公司之后，您没有得到任何采访，那么您可能希望获得一些具体建议并改变您的方法。 咖啡会议可能是获得此建议的好方法。 如果您有选择地向公司发送消息，但没有接受采访，则最常见的原因有：

• Your resume and cover letter aren’t geared towards the company well.
• You didn’t manage to get any referrals from people at the company.
• The jobs require more experience or skills than you have currently.

• 您的简历和求职信不太适合公司。
• 您没有设法从公司人员那里获得任何推荐。
• 这些工作需要比您目前更多的经验或技能。

底线 (The Bottom Line)

There are no guarantees, but if you follow these steps, you’ll be in excellent position to get a data science job. I personally went through a multiyear journey while I transitioned into data science, which you can read about here.

没有保证，但是如果您按照这些步骤操作，您将处在获得数据科学工作的有利位置。 当我过渡到数据科学时，我个人经历了多年的旅程，您可以在此处阅读有关信息 。

翻译自: https://www.pybloggers.com/2016/12/how-to-get-a-data-science-job/

数据科学家美国工作待遇