If you’re new in data science, “doing data science” likely sounds like a big deal to you. You might think that you need meticulously collected data, all the tools for data science and a flawless knowledge before you can claim that you “do data science”. But this is not true. You can, and in fact, you should start working and playing with data as soon as you can. You don’t have to call it “doing data science” if you don’t want to but working with data will only do good for you.

如果您是数据科学的新手，那么“做数据科学”对您来说可能听起来很重要。 您可能会认为自己需要精心收集的数据，用于数据科学的所有工具以及无懈可击的知识，然后才能宣称自己“进行数据科学”。 但是这是错误的。 您可以并且实际上应该尽快开始使用数据并进行处理。 如果您不想这么做，则不必称其为“进行数据科学”，但是处理数据只会对您有所帮助。

In this article, I will explain why working with data as part of your current job is a great idea. I will then give you examples of projects to get your imagination going. Lastly, we will look into some things to keep in mind while working on data science projects at your current company.

在本文中，我将解释为什么将数据作为当前工作的一部分是一个好主意。 然后，我将为您提供一些项目示例，以激发您的想象力。 最后，在您当前公司从事数据科学项目时，我们将研究一些注意事项。

为什么开始做项目如此重要？ (Why is it so important to start doing projects?)

First of all, you will learn a ton of skills you didn’t even know you needed by doing hands-on work. Secondly, it is a great way to hint to your future employer that you mean business, you are interested in this job and you take every opportunity to improve yourself.

首先，您将通过动手工作学习甚至不知道自己需要的大量技能。 其次，这是向您的未来雇主暗示您的意思的好方法，您对这项工作感兴趣，并抓住一切机会提高自己。

To learn new skills, any type of project counts. Work with data that you can get your hands on. Simple datasets from the internet, your own WhatsApp chat history, data you found on Reddit, anything goes. Take opportunities to stretch your working-with-data muscles. You can take on bigger challenges as you up your game.

要学习新技能，任何类型的项目都至关重要。 处理可以获取的数据。 来自互联网的简单数据集，您自己的WhatsApp聊天历史记录，在Reddit上找到的数据，任何事情都会发生。 抓住机会来扩展您的数据工作肌肉。 在玩游戏时，您可以面对更大的挑战。

Impressing a potential employer is a different deal though. It’s hard to get ahead of the competition by showing simple projects. At that point, your proof will be the portfolio of projects with a good deal of thought gone into them. Creating a portfolio of projects is a very vast topic. In this article, I want to focus on professional portfolio projects.

不过，打动潜在的雇主是另一回事。 通过展示简单的项目很难领先于竞争对手。 到那时，您的证明将是经过大量考虑的项目组合。 创建项目组合是一个非常广阔的主题。 在本文中，我想专注于专业投资组合项目。

什么是专业组合项目？ (What are professional portfolio projects?)

Those are the data science projects you do at your current job. As I said in my article on Quick tips for career switchers who doesn’t want to start from a junior position one of your main selling points is your professional experience and experience working with other people. You need to make use of this advantage to the fullest.

这些是您当前工作的数据科学项目。 正如我在《职业转换者的快速提示》文章中所说的那样， 他们不想从初级职位开始 ，您的主要卖点之一就是您的专业经验和与他人合作的经验。 您需要充分利用这一优势。

Professional projects are good indicators of a promising future data scientist for a couple of reasons:

专业项目是有前途的未来数据科学家的良好指标，其原因有两个：

• It shows that you are interested它表明你有兴趣
• It shows that you take charge when you need to它表明您在需要时负责
• It shows that you can work in a professional environment它表明您可以在专业环境中工作
• It shows that you know how to formulate a data science project它表明您知道如何制定数据科学项目
• It shows initiative它显示出主动性

“这听起来很棒，但我无法想象自己能做些什么。” (“It all sounds awesome but I can’t imagine what I can do at my job.”)

Well, your title really doesn’t have to be “data scientist” or “something analyst” for you to get access to some data and work with it. Doesn’t matter if you work in marketing, design or HR, if you ask for it, you can get the data as long as it is not confidential. Many companies are not yet making use of the data they collect remotely enough and they welcome opportunities for anyone to analyse it and draw conclusions from it. It’s a win-win situation because you probably won’t be able to find better data on the internet.

好吧，您的标题实际上不必是“数据科学家”或“分析师”，您才可以访问某些数据并使用它们。 无论您是从事市场营销，设计还是人力资源工作，都可以，只要您不是机密信息，就可以获取数据。 许多公司尚未充分利用他们远程收集的数据，因此他们欢迎任何人进行分析并从中得出结论的机会。 这是双赢的局面，因为您可能无法在互联网上找到更好的数据。

Let me give you examples of some cases.

让我举一些例子。

Your company is selling beauty products (or any other retail product). Ask for a rundown of all sales. You can analyse the data to look for trends, try to see if you can predict sales in a region per day. Add a couple of your own features and see if the performance gets better. You can also take it to the next level and use explainability techniques to explain why your model is predicting what it’s predicting.

您的公司正在销售美容产品(或任何其他零售产品)。 要求所有销售减少。 您可以分析数据以查找趋势，并尝试查看是否可以预测每天某个区域的销售额。 添加您自己的几个功能，然后查看性能是否有所提高。 您还可以将其带入一个新的水平，并使用可解释性技术来解释模型为何预测其预测。

Let’s say you work for a scooter rental company. If your product collects data, can you create a model that anticipates after how long the scooter breaks down? Can you predict the need for maintenance ahead of time?

假设您在踏板车租赁公司工作。 如果您的产品收集数据，您是否可以创建一个模型，该模型可以预测踏板车发生故障的时间？ 您可以提前预测维护需求吗？

Maybe you work at a gym. If you collect member’s entrance data, you can anonymise it and try to see who is more likely to keep showing up. This is a tricky one though. You wouldn’t want to be using any variable that might cause unethical results such as someone’s race or gender. Though gender might be okay to use here unless your company decides to give discounts to people who are likely to show up more. (Because that would be discrimination.) You can also look into predicting how busy the gym would be at a certain time based on season, time of day, the weather, etc. Probably safer to do so.

也许你在健身房工作。 如果您收集会员的进入数据，则可以将其匿名化，并尝试查看谁更有可能继续显示。 这是一个棘手的问题。 您不希望使用任何可能导致不道德的结果的变量，例如某人的种族或性别。 虽然在您的公司中可以使用性别，除非您的公司决定对可能出现更多性别的人提供折扣。 (因为那是歧视。)您还可以根据季节，一天中的时间，天气等来预测健身房在特定时间的繁忙程度。这样做可能更安全。

If you work at a recruitment company, you can try to get a hold of past hires and see how the persons’ profile correlates to the companies they were hired at. One option is to make a model that predicts how good of a fit a person and a company has. Some safe-to-use features would be, education level, school, degree, years of experience in the industry, years of general professional experience of the person and seniority level, industry, maturity and other similar factors of a job listing or company. You might want to keep an eye out for potential proxy variables (e.g. postcode might be a proxy for race in a city where people with the same ethnicity live in same neighbourhoods).

如果您在一家招聘公司工作，则可以尝试掌握过去的招聘信息，并查看这些人的个人资料与他们所在的公司之间的关系。 一种选择是制作一个模型来预测个人和公司的适应程度。 一些可以安全使用的功能包括教育程度，学历，学位，该行业的工作经验，该人员的一般专业经验和年资水平，行业，成熟度以及工作清单或公司的其他类似因素。 您可能要注意潜在的代理变量(例如，邮政编码可能是在一个种族相同的居民居住在同一社区的城市中的种族的代理)。

The projects don’t have to be groundbreaking or novel. You can replicate projects that are already done. As long as it’s your own work and you take your specific situation into consideration, it’s valuable data science work.

这些项目不必是开创性的或新颖的。 您可以复制已经完成的项目。 只要这是您自己的工作，并且考虑到您的具体情况，它就是有价值的数据科学工作。

What should you focus on during the project and what should you highlight while presenting this work?

在项目期间，您应重点关注什么？在介绍此工作时应强调什么？

• Make sure you understand your company’s business确保您了解公司的业务
• Create a list of potential projects you’re interested in looking into, not all of them need to be feasible创建您感兴趣的潜在项目列表，并非所有项目都需要可行
• Take notes on your process of collecting the data for your final reporting. Struggles you face while getting data is a part of the data science process.在收集数据以进行最终报告的过程中做笔记。 在获取数据时遇到的困难是数据科学过程的一部分。
• Clearly state your goal and your approach. Your approach might take its final form as you go. Just make sure to note down your decision points and the way you decided to go forward.明确说明您的目标和方法。 您采用的方法可能会采用最终形式。 只需确保记下您的决定点和决定前进的方式即可。
• Note ethical issues you faced. Variables you decided to use, the ones you decided to omit and why.注意您面临的道德问题。 您决定使用的变量，您决定忽略的变量以及原因。
• Talk about the features you created or added and why.讨论您创建或添加的功能以及原因。

Tip: Apart from being technical work data science is very much a creative and critical thinking process. Explain your decisions and highlight smart solutions you came up with.

提示：除了从事技术工作外，数据科学还非常具有创造力和批判性。 解释您的决定并突出您提出的智能解决方案。

To get extra points during your job hunt, you can:

为了在求职过程中获得加分，您可以：

• Try and arrange a time to present your work to your team尝试安排时间向团队介绍您的工作
• Try to see if anyone in the company is interested in your results and treat them like your stakeholders尝试查看公司中是否有人对您的结果感兴趣并将其视为您的利益相关者
• Big plus points if you can get your company to use your results/model. Deployment to real-life is one of the most problematic parts of the data science pipeline. Showing experience in implementation will be very important for you.如果您可以让您的公司使用您的结果/模型，那就可以加分。 部署到现实生活是数据科学管道中最有问题的部分之一。 展示实施经验对您非常重要。

Tip: If you can’t implement a big project you worked on, do a simple one and get it implemented. It’s always impressive to deploy a piece of work in a company. Even if it’s just a simple analysis, it will show your capability to work with complex situations and get things done.

提示：如果您无法实施您从事的大型项目，请做一个简单的项目并实施。 在公司中部署工作总是令人印象深刻。 即使只是简单的分析，它也将显示您处理复杂情况并完成任务的能力。

The secret to getting a job as a data scientist when you’re switching from a different career is to play your professional experience card. If on top of that, you have some data science projects you have done in a professional environment, that would be a huge plus for you. Keep your eyes and ears open for opportunities at your current job and don’t hesitate to ask around. I’m sure you’d be surprised at how eager your company would be to have data analysis done for free.

当您转行另一职业时，成为数据科学家的秘密在于打出您的专业经验卡。 如果最重要的是，您还有一些在专业环境中完成的数据科学项目，那对您来说将是一个巨大的优势。 睁大眼睛，为当前的工作提供机会，不要犹豫，四处询问。 我相信您会对公司免费进行数据分析如此渴望而感到惊讶。

Check out my website So you want to be a data scientist? for articles and free resources specifically for the busy professional looking to transition their career into data science.

查看我的网站所以您想成为数据科学家？ 专为忙碌的专业人士准备的文章和免费资源，希望将其职业转变为数据科学。