建立大数据能力的6大要素

When I launched Dataquest a little under two years ago, one of the first things I did was write a . At the time, if you wanted to become a data scientist, you were confronted with dozens of courses on sites like edX or Coursera with no easy path to getting a job.

当我不到两年前启动Dataquest时，我做的第一件事就是写的 。 当时，如果您想成为一名数据科学家，那么您将面对诸如edX或Coursera等网站上的数十门课程，而这并不容易。

I saw many promising students give up on learning data science because they got stuck in a loop of taking the same courses over and over. There were two main barriers to learning data science that I was trying to solve with Dataquest: the challenge of getting from theory to application, and the challenge of knowing what to learn next.

我看到许多有前途的学生放弃学习数据科学，因为他们陷入了反复学习相同课程的循环中。 我正在尝试使用Dataquest解决的两个主要学习数据科学障碍：从理论到应用的挑战，以及了解下一步学习的挑战。

I that everyone deserves a chance to do work that they find interesting, and Dataquest was a way to put that belief into action and help others get a toehold in a difficult field. Over the past two years, we’ve made it simple to learn all of the skills you need for a data science role in one place. From basic Python to SQL to Machine Learning, Dataquest teaches you the right skills, and helps you build a portfolio of projects along the way.

我每个人都应该有机会做自己认为有趣的工作，Dataquest是一种将这种信念付诸实践并帮助其他人在困难领域中站稳脚跟的方法。 在过去的两年中，我们使在一个地方学习数据科学角色所需的所有技能变得简单。 从基本的Python到SQL再到机器学习，Dataquest可以教给您正确的技能，并帮助您逐步构建项目组合。

As we’ve built the site, we’ve learned quite a few lessons on how to most effectively help our students. We’ve been gradually increasing the scope of our initial vision. In this post, I want to outline what we’re focused on now, and where we’re headed. Along the way, I hope to make the case for why Dataquest is the place you should be learning data science.

建立网站时，我们已经学到了很多关于如何最有效地帮助学生的课程。 我们一直在逐渐扩大我们的最初愿景的范围。 在这篇文章中，我想概述一下我们现在关注的重点以及前进的方向。 在此过程中，我希望说明为什么Dataquest是您应该学习数据科学的地方。

两年的观察 (Two Years Of Observations)

It’s a common refrain that learning is its own reward. Massively Open Online Course (MOOC) sites like the aforementioned edX and Coursera were created with this wisdom in mind. What we’ve found instead is that our students are learning data science because they enjoy it and because they want more interesting jobs.

人们普遍认为学习是自己的报酬。 出于上述考虑，创建了诸如上述edX和Coursera之类的大规模开放在线课程（MOOC）网站。 相反，我们发现我们的学生正在学习数据科学是因为他们喜欢它，并且因为他们想要更多有趣的工作。

This observation has pushed us to become more career-focused. The most common thing students want is a better path to data science careers, and we feel that it’s the highest leverage thing we can work on.

这种观察促使我们变得更加注重职业。 学生最想要的东西是通往数据科学职业的一条更好的道路，我们认为这是我们可以从事的最高杠杆作用。

As we help people get ready for new careers, we’ve made four key observations:

在帮助人们为新职业做好准备的过程中，我们进行了四个关键观察：

• Focus is critical to retaining knowledge, especially when you have limited time
• Motivation is the most important determinant of whether you’ll get a job
• It’s easy to get “stuck” and frustrated – timely help is key
• There isn’t a lot of good career advice and interview preparation help

• 专注对于保留知识至关重要，尤其是在时间有限的情况下
• 动机是决定您能否找到工作的最重要因素
• 容易被“卡住”和沮丧–及时的帮助是关键
• 没有很多好的职业建议和面试准备帮助

Let’s dive into each of these observations in more depth, and see how they’ve affected our thinking.

让我们更深入地研究每个观察，看看它们如何影响我们的思维。

焦点 (Focus)

When you’re learning data science, it’s tempting to get lost in a sea of tools. You’re told that you have to learn R, Python, Spark, and Tensorflow. If you don’t, you’re not a “real” data scientist.

当您学习数据科学时，很容易迷失在大量的工具中。 有人告诉您必须学习R，Python，Spark和Tensorflow。 否则，您就不是“真正的”数据科学家。

What we’ve found instead is that the students who end up getting jobs focus on concepts over tools. If you learn how to implement a random forest from scratch, and know the tradeoffs involved in training it, it doesn’t matter if you use Python, Scala, or R to make predictions. Concepts generalize between tools; if you learn a concept well, you can use any tool to implement it. If you can fit a decision tree model in R, you’ll have some job prospects, but if you deeply understand the model and how it works, you’ll have an order of magnitude more.

相反，我们发现最终找到工作的学生将重点放在工具概念上。 如果您学习如何从头开始实现随机森林，并了解训练它所涉及的折衷，那么使用Python，Scala或R进行预测都没关系。 概念概括了工具之间的关系。 如果您很好地学习了一个概念，则可以使用任何工具来实现它。 如果您可以在R中适应决策树模型，那么您将有一定的工作前景，但是如果您深入了解该模型及其工作原理，则将有一个数量级的提高。

Focusing on a few concepts at a time and mastering them before moving on is key to retaining knowledge. We’ve kept Dataquest extremely focused, so knowledge sinks in. We have a linear curriculum that takes you from no programming knowledge all the way to advanced machine learning. Because we develop the entire curriculum, we’re able to teach things in logical order, and make sure you’re never lost. Our consistent style and focus on concepts mean that you can stay focused on learning one concept at a time.

一次专注于几个概念并在继续进行之前对其进行掌握是保留知识的关键。 我们一直使Dataquest高度专注，因此知识会渗入。我们拥有线性课程，从无编程知识到高级机器学习的全过程。 因为我们开发了整个课程，所以我们能够按照逻辑顺序进行教学，并确保您永远不会迷路。 我们一贯的风格和对概念的关注意味着您可以专注于一次学习一个概念。

动机 (Motivation)

It’s often taught in school that it’s a teacher’s job to teach, and your job to be motivated. But if you’re unmotivated, even a teacher who knows the material well won’t be effective. We’ve found is that motivation is the single biggest difference between students who get jobs, and those who don’t. It’s not enough to just “check the boxes” and get certificates. You have to build projects to demonstrate your skills, and build a portfolio. In order to be motivated build effective projects, you have to genuinely enjoy working with data. As I wrote in a , a prerequisite for learning data science is finding problems that interest and motivate you.

在学校里经常有人教，这是老师的工作，要激发你的工作动力。 但是，如果您没有动力，即使是一位非常了解教材的老师也不会有效。 我们发现，动机是有工作的学生与没有工作的学生之间最大的不同。 仅“勾选”框并获取证书是不够的。 您必须构建项目以展示自己的技能，并构建项目组合。 为了有动力建设有效的项目，您必须真正享受使用数据的乐趣。 正如我在的所写的那样的先决条件是找到引起您兴趣并激发您的问题。

At Dataquest, we’ve realized that it’s our job to be motivating, and we’ve oriented the site around it. We’ve designed our curriculum to interleave dozens of interesting data sets, including data on CIA interventions and NBA player stats. When you’re ready for them, we include dozens of interesting projects exploring topics like how to win Jeopardy and stock price forecasting. By focusing on engaging and motivating you, we help you get further in your journey to get a data science job.

在Dataquest，我们已经意识到激励自己是我们的工作，并且我们围绕它来定位网站。 我们将课程设计为交错许多有趣的数据集，包括有关CIA干预和NBA球员数据的数据。 当您为他们做好准备时，我们会提供数十个有趣的项目，探讨有关如何赢得“危险”和股价预测的主题。 通过专注于吸引和激励您，我们可以帮助您在获得数据科学工作的过程中走得更远。

被“卡住” (Getting “Stuck”)

When it comes to more open-ended projects, we’ve found that students need help getting “unstuck”. Being stuck can range from not knowing how to install a package to having trouble conceptualizing the structure of the data. Students often don’t need major help – just a small nudge in the right direction or a confidence boost can be invaluable.

当涉及到更多开放式项目时，我们发现学生需要获得帮助以“解脱”。 卡住的范围可能从不知道如何安装软件包到难以概念化数据结构。 学生通常不需要主要的帮助-朝正确的方向轻轻一推或增强信心是非常宝贵的。

We’ve realized that as these small moments of frustration when you’re stuck pile up, they decrease your motivation, and make it more likely that you won’t reach your goals. We’ve designed systems that ensure you can get help either from a mentor or peers to avoid this frustration. We help students directly with 1:1 mentorship and office hours. We’ve created a strong community where students help each other learn and avoid common pitfalls.

我们已经意识到，当这些沮丧的小时刻堆积如山时，它们会降低您的动力，并更有可能使您无法实现目标。 我们设计的系统可确保您可以从导师或同事那里获得帮助，从而避免了这种挫败感。 我们以1：1的指导和办公时间直接帮助学生。 我们建立了一个强大的社区，学生可以互相帮助学习并避免常见的陷阱。

职业咨询 (Career Advice)

We’ve noticed that many of our students have career questions, which range from wondering what skills they should learn to be most marketable to employers, to what questions might be asked in an interview, to what their portfolio should look like. Many of these questions are best answered by peers, and we’ve encouraged students to help each other advance their careers. We also offer office hours to help more directly with career questions.

我们注意到，我们的许多学生都有职业问题，范围包括想知道应该学习哪些技能才能最有效地向雇主推销，到面试中可能会问哪些问题，到他们的投资组合应该是什么样子。 这些问题中的许多问题都是同学最好的回答，我们鼓励学生互相帮助，共同促进职业发展。 我们还提供办公时间，以直接解决职业问题。

There’s quite a bit more we want to do to help students in this area, though. We want to offer everything from helping students understand what working at specific companies is like to reviewing portfolios. Career advice is an area we’re actively expanding and working on, and we’ll be introducing some exciting additions to Dataquest soon!

不过，我们还有很多工作要做，以帮助该领域的学生。 我们希望提供所有内容，从帮助学生了解在特定公司工作的经历到审查投资组合。 职业建议是我们正在积极扩展和工作的领域，我们将在Dataquest中引入一些激动人心的补充！

我们的路线图 (Our Roadmap)

Based on the above observations, we’ve realized that the ideal data science learning tool:

基于以上观察，我们已经意识到理想的数据科学学习工具：

1. Gives you a roadmap for learning data science
2. Allows you to practice skills by coding in the browser
3. Teaches advanced concepts in an applied fashion
4. Helps you build your portfolio with projects
5. Gives you support along the way with mentor and community help
6. Guides you on career choices and helps you find potential employers

1. 为您提供学习数据科学的路线图
2. 允许您通过在浏览器中编码来练习技能
3. 以实用的方式教授高级概念
4. 帮助您通过项目建立投资组合
5. 在导师和社区帮助下为您提供支持
6. 指导您选择职业，并帮助您找到潜在的雇主

There’s currently no tool that does all of the above, although some (including Dataquest) cover several. We have the most work to do at the end of the learning journey, when students want to build more advanced projects and look for jobs. Let’s go through each point, and talk about where we’re at with it.

尽管某些工具（包括Dataquest）涵盖了多个工具，但目前尚无工具可以完成上述所有操作。 当学生想要建立更高级的项目并寻找工作时，我们在学习旅程的最后要做最多的工作。 让我们仔细研究每个问题，并讨论我们的立场。

1.数据科学路线图 (1. Data science roadmap)

A roadmap for data science lets you stay focused and on track, without having to figure out which course to take next. With our comprehensive paths that teach you how to be a data analyst or a data scientist, we take you through all the material you need to know and help you build projects, all in a clear, consistent way that’s designed to help you get the job you want.

数据科学路线图可让您保持专注和跟踪，而不必弄清楚下一步该怎么做。 通过我们全面的课程，教您如何成为数据分析师或数据科学家，我们以清晰一致的方式带您了解所有需要了解的知识并帮助您构建项目，以帮助您完成工作你要。

This year, we plan to develop more roadmaps, for fields like Data Engineering.

今年，我们计划为数据工程等领域开发更多路线图。

2.浏览器内编码 (2. In-browser coding)

It’s amazing how long installing packages like pandas or tools like Spark can take when you’re a beginner. We let you get your feet wet in the browser, and teach you all the skills. Afterwards, when you understand them better, we help you get everything setup on your own computer so you can work on your own.

当您是初学者时，安装像熊猫这样的软件包或像Spark这样的工具需要花费多长时间，真是令人惊讶。 我们让您在浏览器中专心致志，并教会您所有技能。 之后，如果您对它们有更好的了解，我们将帮助您在自己的计算机上进行所有设置，以便您可以自己进行工作。

We also score your answers in the browser, so you know when you’re on track. We’ve found that in-browser practice is very motivating, and helps people hit their goals.

我们还会在浏览器中为您的答案打分，以便您按计划进行。 我们发现，浏览器内练习非常有激励作用，并且可以帮助人们实现目标。

This year, we plan to add better ways to practice concepts using spaced repetition and other methods that help complex topics really sink in.

今年，我们计划增加一些更好的方法来使用间隔重复和其他方法来实践概念，这些方法可以帮助真正解决复杂问题。

3.应用概念 (3. Applied concepts)

Our missions teach you data science concepts like decision trees by having you work through interesting datasets. You might work through data on airline accidents, or educational achievement worldwide. Once you’ve learned the skills, you’re able to apply them with projects that use more interesting datasets. This loop of learning then application helps you quickly develop and solidify your skills.

我们的任务通过让您研究有趣的数据集来教您诸如决策树之类的数据科学概念。 您可能需要处理有关航空事故或全球教育成就的数据。 一旦掌握了这些技能，便可以将其应用到使用更多有趣数据集的项目中。 这种学习然后应用的循环帮助您快速发展和巩固自己的技能。

We plan to use larger and more varied datasets this year, including audio, video, and image data.

我们计划今年使用更大，更多样化的数据集，包括音频，视频和图像数据。

4.项目 (4. Projects)

We help you build a portfolio of projects. Not only does this help you practice and learn concepts, it also helps you get job interviews! Hiring managers are increasingly looking at portfolios when making decisions on who to interview. Even interviews have moved more towards projects as a means of assessment – you might get a take-home or in person project as part of your interview.

我们帮助您建立项目组合。 这不仅可以帮助您练习和学习概念，还可以帮助您获得工作面试！ 招聘经理在决定面试对象时越来越关注投资组合。 甚至面试也已将更多的项目作为评估的手段–在面试过程中，您可能会获得实地或亲自参加的项目。

This year, we plan to have more open-ended projects, and offer more help with them. Imagine creating a bot that can have a conversation with you in together with your peers as part of a project!

今年，我们计划有更多开放式项目，并为他们提供更多帮助。 想象一下，创建一个可以与您进行对话的机器人，并将其作为项目的一部分！

5.一路支持 (5. Support along the way)

Right now, you can get help from other students learning on Dataquest, or from our teachers. This help is critical in keeping you focused and motivated.

现在，您可以从其他学习Dataquest的学生或我们的老师那里获得帮助。 此帮助对于保持您的注意力和动力至关重要。

This year, we plan to offer more hands-on help, including reviewing projects and assigning group work.

今年，我们计划提供更多动手帮助，包括审查项目和分配小组工作。

6.职业帮助 (6. Career help)

Right now, we give career advice during office hours, where we have 1:1 conversations with students. This year, we plan to develop a more robust careers section that helps you figure out how and where to interview. We also plan to more directly help you with your job search.

现在，我们在办公时间提供职业建议，在这里我们与学生进行1：1对话。 今年，我们计划开发一个更强大的职业部分，以帮助您弄清楚应聘方式和地点。 我们还计划更直接地帮助您寻找工作。

展望未来 (Looking ahead)

As you’ve read, you can expect a lot of improvements for Dataquest in 2017. But progress doesn’t happen all at once, it happens regularly, as we constantly tweak the Dataquest experience. We now have a regular release schedule, and you can expect substantial improvements every month. Take a look at the posts for our for an idea of how quickly Dataquest is evolving.

正如您所阅读的，您可以预期2017年Dataquest会有很多改进。但是，随着我们不断调整Dataquest的体验，进展不会一次全部发生，而是定期发生。 现在，我们有一个定期的发布时间表，您可以期望每个月都有实质性的改进。 请看一下我们 的帖子，以了解Dataquest的发展速度。

In the next 3 months, you can expect:

在接下来的3个月中，您可以期望：

• An easier to use coding interface
• Portfolio reviews and feedback
• Improved statistics and machine learning content
• The launch of the long-awaited Data Engineering learning path
• Better ways to practice concepts you’ve learned

• 易于使用的编码界面
• 作品集评论和反馈
• 改进的统计数据和机器学习内容
• 期待已久的数据工程学习之路的启动
• 实践已学概念的更好方法

翻译自:

建立大数据能力的6大要素