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.

如果您是數據科學的新手，那么“做數據科學”對您來說可能聽起來很重要。 您可能會認為自己需要精心收集的數據，用于數據科學的所有工具以及無懈可擊的知識，然后才能宣稱自己“進行數據科學”。 但是這是錯誤的。 您可以并且實際上應該盡快開始使用數據并進行處理。 如果您不想這么做，則不必稱其為“進行數據科學”，但是處理數據只會對您有所幫助。

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，我將解釋為什么將數據作為當前工作的一部分是一個好主意。 然后，我將為您提供一些項目示例，以激發您的想象力。 最后，在您當前公司從事數據科學項目時，我們將研究一些注意事項。

為什么開始做項目如此重要？ (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，您將通過動手工作學習甚至不知道自己需要的大量技能。 其次，這是向您的未來雇主暗示您的意思的好方法，您對這項工作感興趣，并抓住一切機會提高自己。

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.

要學習新技能，任何類型的項目都至關重要。 處理可以獲取的數據。 來自互聯網的簡單數據集，您自己的WhatsApp聊天歷史記錄，在Reddit上找到的數據，任何事情都會發生。 抓住機會來擴展您的數據工作肌肉。 在玩游戲時，您可以面對更大的挑戰。

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.

不過，打動潛在的雇主是另一回事。 通過展示簡單的項目很難領先于競爭對手。 到那時，您的證明將是經過大量考慮的項目組合。 創建項目組合是一個非常廣闊的主題。 在本文中，我想專注于專業投資組合項目。

什么是專業組合項目？ (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.

這些是您當前工作的數據科學項目。 正如我在《職業轉換者的快速提示》文章中所說的那樣， 他們不想從初級職位開始 ，您的主要賣點之一就是您的專業經驗和與他人合作的經驗。 您需要充分利用這一優勢。

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

專業項目是有前途的未來數據科學家的良好指標，其原因有兩個：

• It shows that you are interested
  它表明你有興趣
• It shows that you take charge when you need to
  它表明您在需要時負責
• It shows that you can work in a professional environment
  它表明您可以在專業環境中工作
• It shows that you know how to formulate a data science project
  它表明您知道如何制定數據科學項目
• It shows initiative
  它顯示出主動性

“這聽起來很棒，但我無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，您的標題實際上不必是“數據科學家”或“分析師”，您才可以訪問某些數據并使用它們。 無論您是從事市場營銷，設計還是人力資源工作，都可以，只要您不是機密信息，就可以獲取數據。 許多公司尚未充分利用他們遠程收集的數據，因此他們歡迎任何人進行分析并從中得出結論的機會。 這是雙贏的局面，因為您可能無法在互聯網上找到更好的數據。

Let me give you examples of some cases.

讓我舉一些例子。

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.

您的公司正在銷售美容產品(或任何其他零售產品)。 要求所有銷售減少。 您可以分析數據以查找趨勢，并嘗試查看是否可以預測每天某個區域的銷售額。 添加您自己的幾個功能，然后查看性能是否有所提高。 您還可以將其帶入一個新的水平，并使用可解釋性技術來解釋模型為何預測其預測。

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?

假設您在踏板車租賃公司工作。 如果您的產品收集數據，您是否可以創建一個模型，該模型可以預測踏板車發生故障的時間？ 您可以提前預測維護需求嗎？

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.

也許你在健身房工作。 如果您收集會員的進入數據，則可以將其匿名化，并嘗試查看誰更有可能繼續顯示。 這是一個棘手的問題。 您不希望使用任何可能導致不道德的結果的變量，例如某人的種族或性別。 雖然在您的公司中可以使用性別，除非您的公司決定對可能出現更多性別的人提供折扣。 (因為那是歧視。)您還可以根據季節，一天中的時間，天氣等來預測健身房在特定時間的繁忙程度。這樣做可能更安全。

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，則可以嘗試掌握過去的招聘信息，并查看這些人的個人資料與他們所在的公司之間的關系。 一種選擇是制作一個模型來預測個人和公司的適應程度。 一些可以安全使用的功能包括教育程度，學歷，學位，該行業的工作經驗，該人員的一般專業經驗和年資水平，行業，成熟度以及工作清單或公司的其他類似因素。 您可能要注意潛在的代理變量(例如，郵政編碼可能是在一個種族相同的居民居住在同一社區的城市中的種族的代理)。

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.

這些項目不必是開創性的或新穎的。 您可以復制已經完成的項目。 只要這是您自己的工作，并且考慮到您的具體情況，它就是有價值的數據科學工作。

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

在項目期間，您應重點關注什么？在介紹此工作時應強調什么？

• Make sure you understand your company’s business
  確保您了解公司的業務
• Create a list of potential projects you’re interested in looking into, not all of them need to be feasible
  創建您感興趣的潛在項目列表，并非所有項目都需要可行
• 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.
  在收集數據以進行最終報告的過程中做筆記。 在獲取數據時遇到的困難是數據科學過程的一部分。
• 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.
  明確說明您的目標和方法。 您采用的方法可能會采用最終形式。 只需確保記下您的決定點和決定前進的方式即可。
• Note ethical issues you faced. Variables you decided to use, the ones you decided to omit and why.
  注意您面臨的道德問題。 您決定使用的變量，您決定忽略的變量以及原因。
• Talk about the features you created or added and why.
  討論您創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：除了從事技術工作外，數據科學還非常具有創造力和批判性。 解釋您的決定并突出您提出的智能解決方案。

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

為了在求職過程中獲得加分，您可以：

• Try and arrange a time to present your work to your team
  嘗試安排時間向團隊介紹您的工作
• Try to see if anyone in the company is interested in your results and treat them like your stakeholders
  嘗試查看公司中是否有人對您的結果感興趣并將其視為您的利益相關者
• 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.
  如果您可以讓您的公司使用您的結果/模型，那就可以加分。 部署到現實生活是數據科學管道中最有問題的部分之一。 展示實施經驗對您非常重要。

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：如果您無法實施您從事的大型項目，請做一個簡單的項目并實施。 在公司中部署工作總是令人印象深刻。 即使只是簡單的分析，它也將顯示您處理復雜情況并完成任務的能力。

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.

當您轉行另一職業時，成為數據科學家的秘密在于打出您的專業經驗卡。 如果最重要的是，您還有一些在專業環境中完成的數據科學項目，那對您來說將是一個巨大的優勢。 睜大眼睛，為當前的工作提供機會，不要猶豫，四處詢問。 我相信您會對公司免費進行數據分析如此渴望而感到驚訝。

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.

查看我的網站所以您想成為數據科學家？ 專為忙碌的專業人士準備的文章和免費資源，希望將其職業轉變為數據科學。