Why Is A Portfolio Website Important For A Data Scientist?

Why Is A Portfolio Website Important For A Data Scientist?

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    Why Is A Portfolio Website Important For A Data Scientist?


    I am building my data science portfolio. With a web design, digital marketing, and content-producing background, I am excited to share some experience in creating a personal portfolio website.

    As a designer or a digital marker, building an attractive portfolio is common sense, but it is also important for data scientists and programmers, even you are not finding a new job recently. Programmers talk about side projects all the time, which create real use cases to grow your expertise, including some problems and solutions that you may never meet in your full-time job. As well, side projects are critical because you can open source it as you want, writing all tips and details as an article without worried about being sued by your employer. If we had a great side project, what about collecting and publishing them online as a further step?

    Of course, you can easily publish works on GitHub Pages, but owning your website means owning complete potential opportunities to expand. It is like consuming products are sold on Shopee, Amazon, as well as the official e-commerce website, which represents two different strategies and channels. From the perspective of a data scientist, we make more side projects practicable after building a website, like having a membership system, collecting emails to do a campaign, and applying a favor-oriented recommendation algorithm. All of these ideas can be fulfilled efficiently with some tools, like WordPress and Google Analytics 4, and the latter is also the tool I am learning these days. I am willing to share about it in the posts afterward.

    One of my works is Google Trends Enhanced. Google Trends is a powerful tool for digital marketers, but it will be perfect if users can search more than five words at the same time. So I hacked it with Python and deployed it on a PythonAnywhere server. This project is not relevant to forecast or machine learning, but it is more like developing a tiny product. I hope this product can help users go further and work efficiently. Also, I know some bugs should be fixed, but as the words of Reid Hoffman, the founder of LinkedIn, “if you are not embarrassed by the first version of your product, you’ve launched too late”. I believe it and do it.



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    Aron

    A data scientist working in a retail company, with experience in web design and marketing. Recently dives into cryptocurrency and quantitative investing.

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