When writing about learning or breaking into data science, I always advise building projects.

在撰写有关学习或涉足数据科学的文章时，我总是建议构建项目。

It is the best way to learn as well as showcase your skills.

这是学习和展示技能的最佳方式。

But I often get messages from readers asking, “How exactly do I come up with ideas for my projects?”

但是我经常从读者那里收到消息，问我：“我究竟如何提出我的项目构想？”

Any seasoned entrepreneur or engineer will tell you they have too many ideas. But it’s not always easy when you’re starting out.

任何经验丰富的企业家或工程师都会告诉您他们有太多想法。 但是，刚开始时并不总是那么容易。

So here’s a few ways I’ve personally come up with ideas.

因此，这是我个人提出想法的几种方法。

参加社交活动并与人们交谈 (Attend networking events and talk to people)

Most people are surprisingly willing to share their own ideas. You just have to ask.

令人惊讶的是，大多数人都愿意分享自己的想法。 您只需要问。

My default question at networking events is, “What are you working on or trying to solve?”

我在网络活动中的默认问题是： “您在做什么或试图解决什么？”

Last week at a virtual event, every single non-technical person I talked to shared a use-case for ML that they wanted to build.

上周在一次虚拟活动中，我交谈过的每一个非技术人员都共享了他们想要构建的ML用例。

Now don’t steal anyone’s idea. But if you’re already dedicating hours to learn data science, consider helping someone for free. You’ll get experience to put on your resume and a connection that may be useful in your career.

现在，不要窃取任何人的想法。 但是，如果您已经投入了数小时来学习数据科学，请考虑免费帮助某人。 您将获得经验丰富的履历表以及对您的职业有用的联系。

Successful people are happy to share ideas. They understand there are an infinite number of problems to solve in the world, and sharing isn’t a zero-sum game.

成功人士乐于分享想法。 他们知道世界上有无数的问题要解决，共享不是零和游戏。

利用您的兴趣爱好产生想法 (Use your hobbies and interests to generate ideas)

Many great ideas have come from merging expertise across different domains.

来自不同领域的专业知识融合产生了许多伟大的想法。

For example, Geoffrey Hinton, the inventor of neural networks, had a background in psychology from which he drew many early ideas about artificial intelligence.

例如， 神经网络的发明者杰弗里·欣顿 ( Geoffrey Hinton)具有心理学背景，他从中汲取了许多关于人工智能的早期想法。

How can you apply this to your own interests?

您如何将其应用于自己的利益？

Personally, I love my dog, badminton, and cooking. I’m also aware of the general topics under the machine learning umbrella. So I’ll try to match a type of ML with each of my hobbies to generate an idea.

就个人而言，我爱我的狗，羽毛球和烹饪。 我也知道机器学习框架下的一般主题。 因此，我将尝试将ML类型与我的每个爱好进行匹配，以产生一个想法。

1. My dog — Categorize audio recordings of my dog’s different barks, ruffs and growls with machine learning.
   我的狗—通过机器学习对狗的不同吠、,和咆哮的音频进行分类。
2. Badminton —Detect if a video of someone swinging a badminton racket has proper form, using machine learning.
   羽毛球-使用机器学习来检测某人挥舞羽毛球拍的视频是否格式正确。
3. Cooking — Classify images of food, by country.
   烹饪-按国家分类食物的图像。

These could all be very interesting projects, if you dug deep into them.

如果您深入研究这些项目，它们可能都是非常有趣的项目。

So ask yourself, what are you interested in? Could data science help you do it better, or extract interesting incites?

所以问问自己，您对什么感兴趣？ 数据科学可以帮助您做得更好，还是提取有趣的内容？

解决日常工作中的问题 (Solve problems in your day job)

Your current job may not be in data science. But that doesn’t mean there aren’t interesting data science problems to solve.

您当前的工作可能不是数据科学。 但这并不意味着没有有趣的数据科学问题可以解决。

Every company has manual operational tasks begging to be automated. If you don’t have them yourself, your colleagues in marketing or customer service might. Can you help them?

每个公司都要求将手动操作任务自动化。 如果您自己没有，那么您在市场营销或客户服务方面的同事可能会。 你能帮他们吗？

Consider if automation, decision trees, or data visualization could help someone in your organization.

考虑自动化，决策树或数据可视化是否可以帮助您组织中的某人。

If this is outside your normal scope, you might have to work on it during your own time. But that’s a small price to pay if it adds value and gives you experience.

如果这超出了您的正常范围，则可能需要在您自己的时间内进行处理。 但是，如果它增加了价值并为您提供了经验，那是一个很小的代价。

Back when I managed business intelligence for an e-commerce company, I wanted to break into software engineering. So I started writing code on weekends to scrape competitor websites selling similar products, and auto generated reports on our overpriced products. Then I sent the reports to our buying department so they could lower prices — This project helped me land my next job.

当我为一家电子商务公司管理商业智能时，我想涉足软件工程。 因此，我开始在周末编写代码，以刮擦销售类似产品的竞争对手网站，并自动生成有关我们定价过高的产品的报告。 然后，我将报告发送给我们的采购部门，以便他们降低价格-这个项目帮助我找到了下一份工作。

Go deep into your current job and you’re almost guaranteed to find a project that data science can be applied to.

深入研究当前的工作，几乎可以保证您找到一个可以应用数据科学的项目。

熟悉数据科学工具包 (Get familiar with the data science toolkit)

Even if you don’t know how every model works, it’s valuable to know the general topics under the ML and data science umbrellas.

即使您不了解每种模型的工作原理，了解ML和数据科学伞下的一般主题也很有价值。

This gives you the ability to fit these models onto the world around you.

这使您能够将这些模型拟合到您周围的世界中。

For example, I know that NLP encompasses “text classification”, “information retrieval” and “question and answer systems”.

例如，我知道NLP包含“文本分类”，“信息检索”和“问答系统”。

So when I have a dataset in mind (ie: Reddit threads), it’s easy to think of potential applications and generate preliminary ideas.

因此，当我想到一个数据集(即Reddit线程)时，很容易想到潜在的应用程序并产生初步的想法。

Once you have the high-level toolkit, coming up with ideas becomes easier across the board.

有了高级工具包后，全面提出想法就变得容易了。

解决您自己的数据科学问题 (Solve your own data science problems)

What problems do you have in your search for a data science job? Could machine learning assist you?

您在寻找数据科学工作时遇到什么问题？ 机器学习可以帮助您吗？

Maybe you could scrape job boards, classify whether a job is data science related, and perform analytics on the job requirements.

也许您可以刮擦工作板，对工作是否与数据科学相关进行分类，并根据工作要求执行分析。

That would be an awesome project!

那将是一个了不起的项目！

You could also add competitive analytics showing hiring differences between companies, and show it to the company you want to work for.

您还可以添加竞争性分析，以显示公司之间的雇用差异，并将其显示给您想要工作的公司。

As someone who hires engineers, I’d be fascinated to see the results of a project like this in someone’s portfolio.

作为雇用工程师的人，我会着迷于某人的投资组合中看到这样的项目的结果。

通过数据科学家的眼镜看世界 (Look at the world through data scientist glasses)

Ask yourself what can be analyzed, tested, or automated as you walk around in your daily life.

问自己一遍，日常生活中可以分析，测试或自动化的内容。

Watering houseplants: could you analyze soil moisture to optimize plant growth?

给室内植物浇水：您能分析土壤湿度以优化植物生长吗？

Shopping: could the department store detect theft with machine learning?

购物：百货商店可以通过机器学习检测到盗窃吗？

Cooking: could a photo of the inside of your fridge detect what ingredients need to be replenished?

烹饪：冰箱内部的照片可以检测需要补充哪些成分吗？

Then take the smallest component of the project, and actually try to build it.

然后，使用项目的最小组件，并尝试进行构建。

There are an unlimited number of ideas to stumble across. You just need the right mindset to see them.

有不计其数的想法可以偶然发现。 您只需要正确的思维方式就能看到它们。

结论 (Conclusion)

Coming up with ideas when you’re starting out is hard. I know because I used to be there.

刚开始时想出主意很难。 我知道，因为我曾经在那里。

But understand — all great ideas come from real experiences. There are no ideas in a vacuum.

但是请理解-所有很棒的想法都来自真实的经验。 真空中没有想法 。

That’s why it’s important to put down your laptop, get outside and talk to people.

这就是为什么放下笔记本电脑，到户外与人交谈很重要的原因。

Seasoned entrepreneurs have too many ideas because they’re already working on lots of projects, and cross-pollinating ideas between different domains.

经验丰富的企业家有太多的想法，因为他们已经在从事许多项目，并且在不同领域之间相互授粉。

Eventually, you’ll also get to the point where you have too many ideas. When you get there, share some!

最终，您还会有太多想法。 当您到达那里时，分享一些！