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

在撰寫有關學習或涉足數據科學的文章時，我總是建議構建項目。

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

這是學習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?”

但是我經常從讀者那里收到消息，問我：“我究竟如何提出我的項目構想？”

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

任何經驗豐富的企業家或工程師都會告訴您他們有太多想法。 但是，剛開始時并不總是那么容易。

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

因此，這是我個人提出想法的幾種方法。

參加社交活動并與人們交談 (Attend networking events and talk to people)

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

令人驚訝的是，大多數人都愿意分享自己的想法。 您只需要問。

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

我在網絡活動中的默認問題是： “您在做什么或試圖解決什么？”

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.

上周在一次虛擬活動中，我交談過的每一個非技術人員都共享了他們想要構建的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.

現在，不要竊取任何人的想法。 但是，如果您已經投入了數小時來學習數據科學，請考慮免費幫助某人。 您將獲得經驗豐富的履歷表以及對您的職業有用的聯系。

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.

成功人士樂于分享想法。 他們知道世界上有無數的問題要解決，共享不是零和游戲。

利用您的興趣愛好產生想法 (Use your hobbies and interests to generate ideas)

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

來自不同領域的專業知識融合產生了許多偉大的想法。

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， 神經網絡的發明者杰弗里·欣頓 ( Geoffrey Hinton)具有心理學背景，他從中汲取了許多關于人工智能的早期想法。

How can you apply this to your own interests?

您如何將其應用于自己的利益？

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.

就個人而言，我愛我的狗，羽毛球和烹飪。 我也知道機器學習框架下的一般主題。 因此，我將嘗試將ML類型與我的每個愛好進行匹配，以產生一個想法。

1. My dog — Categorize audio recordings of my dog’s different barks, ruffs and growls with machine learning.
   我的狗—通過機器學習對狗的不同吠、,和咆哮的音頻進行分類。
2. Badminton —Detect if a video of someone swinging a badminton racket has proper form, using machine learning.
   羽毛球-使用機器學習來檢測某人揮舞羽毛球拍的視頻是否格式正確。
3. Cooking — Classify images of food, by country.
   烹飪-按國家分類食物的圖像。

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

如果您深入研究這些項目，它們可能都是非常有趣的項目。

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

所以問問自己，您對什么感興趣？ 數據科學可以幫助您做得更好，還是提取有趣的內容？

解決日常工作中的問題 (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.

您當前的工作可能不是數據科學。 但這并不意味著沒有有趣的數據科學問題可以解決。

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?

每個公司都要求將手動操作任務自動化。 如果您自己沒有，那么您在市場營銷或客戶服務方面的同事可能會。 你能幫他們嗎？

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

考慮自動化，決策樹或數據可視化是否可以幫助您組織中的某人。

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.

如果這超出了您的正常范圍，則可能需要在您自己的時間內進行處理。 但是，如果它增加了價值并為您提供了經驗，那是一個很小的代價。

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.

當我為一家電子商務公司管理商業智能時，我想涉足軟件工程。 因此，我開始在周末編寫代碼，以刮擦銷售類似產品的競爭對手網站，并自動生成有關我們定價過高的產品的報告。 然后，我將報告發送給我們的采購部門，以便他們降低價格-這個項目幫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.

深入研究當前的工作，幾乎可以保證您找到一個可以應用數據科學的項目。

熟悉數據科學工具包 (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.

即使您不了解每種模型的工作原理，了解ML和數據科學傘下的一般主題也很有價值。

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

這使您能夠將這些模型擬合到您周圍的世界中。

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

例如，我知道NLP包含“文本分類”，“信息檢索”和“問答系統”。

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，當我想到一個數據集(即Reddit線程)時，很容易想到潛在的應用程序并產生初步的想法。

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

有了高級工具包后，全面提出想法就變得容易了。

解決您自己的數據科學問題 (Solve your own data science problems)

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

您在尋找數據科學工作時遇到什么問題？ 機器學習可以幫助您嗎？

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

也許您可以刮擦工作板，對工作是否與數據科學相關進行分類，并根據工作要求執行分析。

That would be an awesome project!

那將是一個了不起的項目！

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

您還可以添加競爭性分析，以顯示公司之間的雇用差異，并將其顯示給您想要工作的公司。

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

作為雇用工程師的人，我會著迷于某人的投資組合中看到這樣的項目的結果。

通過數據科學家的眼鏡看世界 (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.

問自己一遍，日常生活中可以分析，測試或自動化的內容。

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

給室內植物澆水：您能分析土壤濕度以優化植物生長嗎？

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

購物：百貨商店可以通過機器學習檢測到盜竊嗎？

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

烹飪：冰箱內部的照片可以檢測需要補充哪些成分嗎？

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

然后，使用項目的最小組件，并嘗試進行構建。

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

有不計其數的想法可以偶然發現。 您只需要正確的思維方式就能看到它們。

結論 (Conclusion)

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

剛開始時想出主意很難。 我知道，因為我曾經在那里。

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

但是請理解-所有很棒的想法都來自真實的經驗。 真空中沒有想法 。

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

這就是為什么放下筆記本電腦，到戶外與人交談很重要的原因。

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

經驗豐富的企業家有太多的想法，因為他們已經在從事許多項目，并且在不同領域之間相互授粉。

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

最終，您還會有太多想法。 當您到達那里時，分享一些！