Do you want to enter the data science world? Congratulations! That’s (still) the right choice.

您想進入數據科學世界嗎？ 恭喜你！ 那(仍然)是正確的選擇。

The market currently gets tougher. So, you must be mentally prepared for a long hiring journey and many rejections. I assume that you have already read that a data science portfolio is crucial and how to build it up. Most of the time, you will do data crunching and wrangling and not applying fancy models.

目前市場變得更加艱難。 因此，您必須為漫長的征途和許多拒絕做好心理準備。 我認為您已經讀過數據科學資料集至關重要，以及如何構建它 。 在大多數情況下，您將進行數據打亂和整理，而不應用復雜的模型。

One question that I am asked on and on is about concrete data sources for cool data and project opportunities to build such a portfolio.

我不斷被問到的一個問題是關于酷數據的具體數據源和建立這樣一個項目組合的項目機會。

I give you the following five ideas for your data science portfolio and a few hints on developing uniqueness.

我為您的數據科學產品組合提供以下五個想法，以及有關發展獨特性的一些提示。

數據科學項目的五個具體構想 (Five Concrete Ideas for Data Science Projects)

1. Customer analytics for a local non-profit organization

1.本地非營利組織的客戶分析

An essential task of a non-profit organization is to find the right person, at the right place or location, in the right moment, approached with the right medium for donations for charitable activities. When that can be optimized, the non-profit organization can collect more funds and do more activities.

非營利組織的一項基本任務是在正確的時間，在正確的地點或地點找到合適的人，并以合適的媒介與之進行慈善活動的捐款。 如果可以對其進行優化，則非營利組織可以收集更多資金并開展更多活動。

What makes that project interesting?

是什么使該項目有趣？

First, most non-profit organizations have much data, not necessarily in digitized form, and often not in good quality. The main task is building a database, data crunching, and getting the data in a usable form. You learn to structure the whole data mess, which is still up to 80% of a data science job.

首先，大多數非營利組織擁有大量數據，不一定是數字化形式，而且往往質量也不高。 主要任務是建立數據庫，處理數據并以可用形式獲取數據。 您將學習構建整個數據混亂的結構，這仍然是數據科學工作的80％。

Second, you do something good for the local community, and you show your social responsibility. You interact with people who are not data experts. Both shows needed soft skills for a data science position.

其次，您為當地社區做點好事，并表現出您的社會責任。 您與不是數據專家的人進行交互。 兩者都顯示了數據科學職位所需的軟技能。

I did voluntarily such projects for an organization that helps children in poverty and for an organization that provides care at home for elderly besides my professional job. Having these experiences builds trust in your person and is a door opener for many other exciting projects.

我自愿為幫助貧困兒童的組織以及為我的專業工作以外的家庭提供養老服務的組織自愿進行了此類項目。 擁有這些經驗可以建立對您的信任，并為許多其他激動人心的項目打開大門。

Finally, non-profit organizations work the same as private banking or wealth management. They also have to acquire the right customer, at the right moment, with the right campaign to bring them money. And I can tell you; the data are also not of better quality than of a non-profit organization. You can directly leverage your experience in other industries.

最后，非營利組織的運作與私人銀行或財富管理相同。 他們還必須在正確的時機通過正確的活動來獲取合適的客戶，以使他們賺錢。 我可以告訴你； 數據的質量也不比非營利組織的更好。 您可以直接利用您在其他行業中的經驗。

How to start?

如何開始？

I found the non-profit organizations through my network. There is always somebody within your family, relatives, and friends engaged with a non-profit organization. Then, I agreed on a first get to know meeting and explained to them what my skills are and what is the value of such analyses. I have given them examples from Google and Facebook. And I searched for publicly available information about the increase in leads at other non-profit organizations to provide them with a flavor. After I have given them first the time to think a few days about it, and in each case, they came back and agreed to do the project. Then, I started the whole data crunching work.

我通過我的網絡找到了非營利組織。 您的家人，親戚和朋友中總會有人與非營利組織合作。 然后，我同意了第一次相識會議，并向他們解釋了我的技能是什么，這種分析的價值是什么。 我給了他們谷歌和Facebook的例子。 然后，我在其他非營利組織中搜索了有關線索增加的公開信息，以向他們提供一種風味。 在我先給他們時間思考幾天之后，在每種情況下，他們都回來了并同意做這個項目。 然后，我開始了整個數據整理工作。

When the data is ready to use, you can work through the classical descriptive, predictive, and prescriptive analytics cycle.

當數據準備好使用時，您可以完成經典的描述性，預測性和規范性分析周期。

2. CERN

2. 歐洲核子研究組織

The CERN is mainly known for its leading fundamental research in particle physics and the largest particle laboratory globally.

CERN主要以其領先的粒子物理學基礎研究和全球最大的粒子實驗室而聞名。

It is often unknown that the CERN makes most of its data, codes, algorithms, and tools they have developed and is using for their research, available to the public. They have sophisticated algorithm testing toolboxes and provide 1-, 2-, 3- and 4-dimensional images. And they have much more.

眾所周知，歐洲核子研究中心(CERN)會將其已經開發并用于研究的大多數數據，代碼，算法和工具公開提供給公眾。 他們具有完善的算法測試工具箱，并提供1、2、3和4維圖像。 他們還有更多。

The CERN does not call that all “innovation.” No, these are just “tools” to perform their “real” innovation task: new frontiers in particle physics.

歐洲核子研究中心(CERN)并不將其全部稱為“創新”。 不，這些只是執行“真正”創新任務的“工具”：粒子物理學的新領域。

I can only highly recommend investing some time, browsing through their web pages and explore all the data and tools available for data analytics. It is one of their core businesses and on a very sophisticated level. I still learn a lot today and get many new ideas.

我僅強烈建議您花一些時間，瀏覽他們的網頁并瀏覽所有可用于數據分析的數據和工具。 它是他們的核心業務之一，而且水平很高。 今天我仍然學到很多東西，并且得到了很多新的想法。

The web page is nested. Please do not lose your passion for the first time browsing it!

該網頁是嵌套的。 請不要失去對第一次瀏覽的熱情！

On the CERN Open Data Portal, you can find two petabytes of particle physics data, for starting your own analyses.

在CERN開放數據門戶上 ，您可以找到兩個PB的粒子物理數據，以開始自己的分析。

What makes that project interesting?

是什么使該項目有趣？

When you start as a data scientist with a project, you typically only know that there are somewhere some data. First, you have to explore what data is available, where it can be found, whether it has redundancies, who has knowledge and access to the data, etc.

當您從一個項目的數據科學家開始時，您通常只知道某處有一些數據 。 首先，您必須探索可用的數據，可在何處找到，是否有冗余，誰擁有知識并可以訪問數據等。

When starting with CERN data, the task is the same when you are unfamiliar with all the particle physics experiments. Luckily, I had in my data science teams always ex-CERN scientists, making it a lot easier to understand.

從CERN數據開始時，如果您不熟悉所有粒子物理學實驗，則任務是相同的。 幸運的是，我在數據科學團隊中一直是前CERN科學家，這使它更容易理解。

Second, having “CERN” on the resume is always an advantage, presupposed that some serious work had been done. Through the physics classes, published issues, webinars, and discussions, you can get part of the community. CERN employs about 2,500 people on-side and has approximately 17,500 contributing scientists globally. Many startup founders have a CERN community background.

其次，在簡歷上加上“ CERN”始終是一個優勢，前提是必須進行一些認真的工作。 通過物理課程，已出版的問題，網絡研討會和討論，您可以加入社區。 歐洲核子研究組織在全球擁有約2500名員工，在全球擁有約17,500名貢獻科學家。 許多創業者都有CERN社區背景。

Last, you have sparse data, meaning the vital information represented in the data is rare. Of thousands or millions of data points, you only look for a few patterns to find and identify. Finding such sparse signals is essential in many fields: predictive maintenance, finding the billionaire ready to invest in your fund, or precision medicine.

最后，您的數據稀疏，這意味著數據中表示的重要信息很少。 在成千上萬的數據點中，您只需要尋找一些模式即可找到和識別。 在許多領域中，找到這種稀疏信號至關重要：預測性維護，尋找準備投資您的基金的億萬富翁或精密醫學。

How to start?

如何開始？

Start with getting familiar what the CERN is doing by browsing their web page and Wikipedia. On the Open Data Portal, you have a document link where a lot of background information including links to GitHub, and tutorials can be found. There is also a dedicated Data Science node. Look what the CERN scientists have already done, learn from them, and start analyzing individually selected datasets with your own methods.

首先通過瀏覽CERN的網頁和Wikipedia來了解他們的工作。 在開放數據門戶網站上，您有一個文檔鏈接 ，可在其中找到許多背景信息，包括到GitHub的鏈接和教程。 還有一個專用的Data Science節點 。 看看CERN科學家已經做了什么，向他們學習，然后開始使用您自己的方法分析單獨選擇的數據集。

Working with CERN data is not a fast project, but a very instructive one. Besides, you can learn a lot about a topic on the frontier of physics.

使用CERN數據不是一個快速的項目，而是一個很有啟發性的項目。 此外，您可以了解有關物理前沿的很多知識。

3. Omdena

3. Omdena

Omdena calls itself a collaborative AI platform. It brings project-wise 30–50 people together that solve with data and AI a real-existing problem in this world.

Omdena稱自己為協作式AI平臺。 它匯集了30-50名項目專家，他們通過數據和AI解決了這個世界上現存的問題。

Unlike a Kaggle competition, it is a real end-to-end project with all the project struggles. You are working in a team with different skills, and with all the interpersonal challenges. And you can have a real impact as all projects are linked to one of the UN’s 17 Sustainable Development Goals.

與Kaggle競賽不同，它是一個真正的端到端項目，需要進行所有項目努力。 您正在一個具有不同技能并面臨所有人際挑戰的團隊中工作。 由于所有項目都與聯合國的17個可持續發展目標之一相關，因此您將產生真正的影響。

A good friend of mine with 20+ years as a data science expert contributes, on average, 20% of his time for projects on Omdena. And even he is saying that he is always learning a lot of new stuff.

我有20多年數據科學專家的好朋友，平均有20％的時間用于Omdena項目。 甚至他說自己一直在學習很多新知識。

Omdena needs a wide range of skills in the AI, data science and machine learning field, and expertise levels. You have to go through an application process, like applying for an internship, with the big difference that not competitive personalities are looked for but people with team spirit. They do not look only for experts. It is the spirit of collaboration.

Omdena在AI，數據科學和機器學習領域以及專業水平方面需要廣泛的技能。 您必須經歷一個申請過程，例如申請實習，這之間的最大區別在于，他們不是在尋找具有競爭能力的人，而是在尋找具有團隊合作精神的人。 他們不僅尋找專家。 這是合作的精神。

What makes that project interesting?

是什么使該項目有趣？

You are part of a real-world data science project. There are no sugarcoated missions, data, and outcomes. It “just” has to solve a real issue with a data-driven approach. You are getting familiar with the whole data science project cycle, and you can experience the different stages and roles.

您屬于真實世界的數據科學項目的一部分。 沒有糖衣任務，數據和成果。 它“只是”必須使用數據驅動的方法解決實際問題。 您已經熟悉了整個數據科學項目周期，并且可以體驗不同的階段和角色。

Next, it is exciting to work side by side with experienced people and to get their mentorship. In just one project, you will learn more than in all your 10 MOOCs and Kaggle competitions.

接下來，與經驗豐富的人們并肩工作并得到他們的指導很令人興奮。 在一個項目中，您將學到的全部10項MOOC和Kaggle競賽中所學到的知識都將多于其他項目。

And last but not least, you are getting a project certificate. Yes, it is another certificate besides your Coursera, Udacity, and university degrees, but it attests your practical experience.

最后但并非最不重要的一點是，您將獲得項目證書。 是的，它是除了Coursera，Udacity和大學學位以外的另一種證書，但是它證明了您的實踐經驗。

How to start?

如何開始？

Look at the completed, ongoing and upcoming projects. Become familiar with Omdena’s approach and, when interested in participating, follow the guideline here.

查看已完成，正在進行和即將進行的項目。 熟悉Omdena的方法，如果有興趣參加，請遵循此處的指南。

4. International and governmental organization

4. 國際和政府組織

Many international and governmental development organizations are now working data-driven. The UN, WHO, World Bank, International Finance Corporation, Inter-American Development Bank, and the European Bank for Reconstruction and Development are some. Also, most governments have task forces responsible for mission-driven data and AI projects and building an ecosystem.

許多國際和政府發展組織現在都在以數據為驅動力。 聯合國，世界衛生組織，世界銀行，國際金融公司，美洲開發銀行和歐洲復興開發銀行都在其中。 此外，大多數政府都有專責小組，負責任務驅動的數據和AI項目以及構建生態系統。

Besides offering internships, paid, or unpaid, most contracts are fixed-term contracts lasting from a few months to three years.

除了提供實習(帶薪或無薪)外，大多數合同都是定期合同，期限從幾個月到三年不等。

Further, many data science and AI startups are working with governmental departments.

此外，許多數據科學和AI初創公司正在與政府部門合作。

In the last 12 months, I supported two former team members to find such projects. The one, half-Thai, went to Thailand to work in a big data startup that is working with Thailand’s government.

在過去的12個月中，我支持了兩位前團隊成員來尋找此類項目。 一個半泰國人去了泰國，在一家與泰國政府合作的大數據初創公司中工作。

The other scanned all the job adds, submitted his CV to these international organizations, and contacted people to finally get a fixed-term contract for a project of 4 months at one of the development banks abroad.

另一個掃描了所有增加的工作，將自己的簡歷提交給了這些國際組織，并與人們聯系，最終在國外的一家開發銀行獲得了為期4個月的項目的定期合同。

What makes that project interesting?

是什么使該項目有趣？

These jobs and projects are often abroad. In addition to practical data science experience, many experiences with a foreign culture, and how to behave in an international diplomacy environment can be gained. That gives you vital soft skills for advancing on the career ladder.

這些工作和項目通常在國外。 除了實際的數據科學經驗，還可以獲得許多外國文化的經驗，以及如何在國際外交環境中表現。 這為您提供了在職業階梯上前進的重要軟技能。

You can take on responsibility from the beginning. Small teams, interactions with decision-makers, presentations in front of leading people, are part of most projects. You often get contacts and mentorship of leading experts in that field, as they often advise international and governmental organizations.

您可以從一開始就承擔責任。 小型團隊，與決策者的互動，在領導者面前的演講是大多數項目的一部分。 您經常會得到該領域領先專家的聯系和指導，因為他們經常會為國際和政府組織提供建議。

Finally, the projects are unique, and research related, which gives space for new experimentation. Examples of such projects include the analyses of road fatalities of a developing country where the government wants to take action to reduce them or geospatial cause analyses of air pollutions because the government wants to put laws in place to limit it. Many social-economic aspects are integrated into these analytics.

最后，這些項目是獨特的，并且與研究相關，這為新的實驗提供了空間。 此類項目的示例包括分析發展中國家要采取行動以減少事故的道路致死率，或者對空氣污染的地理空間原因進行分析，因為政府希望制定法律來限制這種死亡。 許多社會經濟方面都集成到這些分析中。

How to start?

如何開始？

The first task is researching the open positions, the ongoing projects, and, importantly, startups working with such organizations.

首要任務是研究職位空缺，正在進行的項目，以及重要的是與這些組織合作的初創公司。

Positions can be found on UNjobs — not only from the UN but from all the organizations, as mentioned earlier, as well as, e.g., Coursera. Further, search on official homepages for the keyword “data scientist.”

不僅在聯合國，而且在所有組織(如前所述)以及Coursera等職位，都可以找到關于UNjobs的職位。 此外，在官方主頁上搜索關鍵字“數據科學家”。

If there should be no suitable internship or short-term job, submit your CV anyway. If they have projects, they compare it with the already available CVs in the database, and if your profile matches, they will contact you.

如果沒有合適的實習或短期工作，則無論如何都要提交簡歷。 如果他們有項目，他們會將其與數據庫中現有的簡歷進行比較，如果您的個人資料匹配，他們將與您聯系。

Second, look for startups that are working with governments. If the startups have projects linked to the UN Sustainable Development Goals, they most probably work with governments.

其次，尋找與政府合作的初創公司。 如果初創公司的項目與聯合國可持續發展目標相關 ，那么它們很可能與政府合作。

Another indication for that is when addressing society’s benefits, like water resourcing, safer community, e.g., preventing road accidents or violence, equality aspects, fighting diseases like HIV or malaria, or decreasing pollution.

另一個跡象表明，這是在解決社會利益時，例如水資源，更安全的社區，例如預防交通事故或暴力，平等方面，與艾滋病毒或瘧疾等疾病作斗爭或減少污染。

Start early in looking for such a project. It takes a bit of time and persistence.

盡早開始尋找這樣的項目。 這需要一些時間和持久性。

But I can highly recommend it. Such an assignment opens many doors during your career, independent of the industry you are working. I could recently move to a global reputable think tank as a program lead. It’s a once in a lifetime chance to get such a position. Why have they asked me? Because I have done such projects in the past.

但是我強烈推薦它。 這樣的任務為您的職業打開了許多門，與您所從事的行業無關。 我最近可以擔任程序負責人，加入全球知名的智囊團。 這是一生一次獲得這樣職位的機會。 他們為什么問我？ 因為我過去做過這樣的項目。

5. The EDGAR database

5. EDGAR數據庫

EDGAR, the abbreviation for Electronic Data Gathering, Analysis, and Retrieval, is a database that contains all submissions by companies and others that are required by law to file forms with the U.S. Securities and Exchange Commission.

EDGAR是電子數據收集，分析和檢索的縮寫，是一個數據庫，其中包含公司和法律要求向美國證券交易委員會提交表格的其他人的所有提交。

You have wealthy business-relevant information in the form of figures and text. A quick introduction is provided here.

您可以使用圖形和文字形式獲取與業務相關的豐富信息。 這里提供了快速介紹。

What makes that project interesting?

是什么使該項目有趣？

You learn first, how to access, download, and extract information from a web database, mainly consisting of text. That can be done with Python, and there exists already OpenEDGAR, an open-source software written in Python. But I would recommend other languages like Perl. It is specially designed for text processing, i.e., extracting the required information from a specified text file and converting it into a different form. It is much faster than Python. And if you want to work in a bank, there are still many databases set up in Perl.

首先，您將學習如何從主要由文本組成的Web數據庫中訪問，下載和提取信息。 可以使用Python做到這一點，并且已經存在OpenEDGAR，這是一個用Python編寫的開源軟件。 但是我會推薦其他語言，如Perl。 它是專為文本處理而設計的，即從指定的文本文件中提取所需的信息并將其轉換為其他形式。 它比Python快得多。 而且，如果您想在銀行工作，Perl中仍然設置了許多數據庫。

It is an excellent database for sentiment analysis and using it to predict company and share price performance. Many fillings are encoded because companies want to shine and not give enough information to competitors. So, this database is a great learning resource for natural language processing (NLP).

它是用于情緒分析的出色數據庫，可用于預測公司和股價表現。 因為公司想要發光而不給競爭對手足夠的信息，所以許多填充物都有編碼。 因此，該數據庫是自然語言處理(NLP)的絕佳學習資源。

Last, these are great topics to start your own blog, either about investments, or NLP. Seriously done, you can get public awareness of your data science work, and it increases your chance for your dream data science job dramatically.

最后，這些都是開創自己的Blog的絕佳主題，涉及投資或NLP。 認真完成后，您可以使公眾意識到您的數據科學工作，這極大地增加了您從事夢想的數據科學工作的機會。

How to start?

如何開始？

Decide on one single company that you want to analyze. Take one that exists at least ten years. Start with the goal to predict if the shares of the companies should be sold or bought.

確定要分析的一家公司。 以至少存在十年的時間為例。 從目標開始，以預測是否應該出售或購買公司的股票。

Familiarize yourself with the different forms in EDGAR. Start with the 10-K, the recent annual report of the company, and the 8-K, the ‘current report’ where events that shareholders should know are published.

熟悉EDGAR中的各種形式。 從公司最近的年度報告10-K和8-K(當前報告)開始，其中發布了股東應了解的事件。

Do common sentiment analysis over the last several years and look at the positive, negative, and net sentiments trends. Compare the curves with the development of the share price. Also, the statements have forward-looking information included. Analyze them, and this will give you the trend.

在過去幾年中進行共同的情緒分析，并查看正面，負面和凈情緒的趨勢。 將曲線與股價的發展進行比較。 此外，這些聲明還包含前瞻性信息。 分析它們，這將為您提供趨勢。

Hint: the language in forward-looking statements contain words like “will”, “should”, “may”, “might”, “intend” and so forth.

提示：前瞻性陳述中的語言包含諸如“將”，“應該”，“可能”，“可能”，“打算”等詞語。

Develop it with more sophisticated NLP and sentiment algorithms, by looking at other companies in the same industry, and integrate different sources like news and macro-economic figures. Compare it with share prices and financial ratios. There are no limits in all these analyses and rich content for a blog.

通過查看同一行業中的其他公司，使用更復雜的NLP和情感算法進行開發，并整合新聞和宏觀經濟數據等不同來源。 將其與股價和財務比率進行比較。 所有這些分析和博客的豐富內容都沒有限制。

Connecting the Dots

連接點

I know that it is hard work to build up a cool data science portfolio. With such a collection, you can make above-average progress in that field, having a lot of fun, and getting your data science dream job.

我知道要建立一個很棒的數據科學產品組合很困難。 有了這樣的集合，您可以在該領域取得超乎尋常的進步，獲得很多樂趣，并使您的數據科學夢想成真。

I do not only recommend this for newbies in the data science area but also senior data scientists. It opens up many new paths during your career, not only because of the projects but also through the newly gained network.

我不僅向數據科學領域的新手推薦此方法，還向高級數據科學家推薦此方法。 它不僅為您的項目打開了道路，而且通過新獲得的網絡也為您的職業打開了許多新的道路。

These ideas show you the wide range of possibilities and give ideas to think out of the box.

這些想法為您展示了廣泛的可能性，并為您提供了開箱即用的想法。

For me and my friends, the learning factors and fun is essential. That is our main focus when dedicating time to such projects.

對于我和我的朋友來說，學習因素和樂趣至關重要。 將時間用于此類項目時，這是我們的主要重點。

That we have built up also an exciting and unique portfolio, was just a waste product.

我們建立了一個令人興奮和獨特的產品組合，這只是一種浪費。