Making a Data Science Portfolio Using Github Pages For Free

Keegan Fernandes
2 min readAug 26, 2022

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I will show you how to build a data science portfolio using GitHub pages in this article free of cost. To complete this project, you will need a basic understanding of git and HTML. You can find my portfolio project using the following link.

First, you need to make a repository on Github. Give the repository the name <your username>.github.io, then clone the repository to your local folder. After cloning to your local folder, search for portfolio projects made using HTML and CSS and copy them. There are plenty of projects like this found online. Avoid using projects that use frameworks like Vue, React etc. as it would overcomplicate your project. I used this repository made by Maclinz for my project. It also has a youtube video attached to it, so you can refer to it in case you didn't understand anything. Clone the files to the local git folder, add, commit and push them to your remote repository. Then go to your repository settings, turn on GitHub pages, and follow their instructions.

Once you have the initial portfolio, you can edit the sections and add your information. For example, add you're about me section, your blog, previous experience and the projects you've done. Ensure your selections highlight your strengths and show the projects are in the same field as your potential employer. For example, if you're planning a job as a business analyst, make sure your top tasks are in the same area. You are your done, your project should look something like this.

Don't expect your portfolio to look good right away. I worked on the project for a few minutes every day for a month and am still unsatisfied with the result. You can also add favicons and personal projects to build a personal brand. Making commits will also improve your GitHub attendance, and your website will change as you accumulate more experience.

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Keegan Fernandes
Keegan Fernandes

Written by Keegan Fernandes

First year student in Msc Data Science. Writes about data science and machine learning tutorials and the impact it has on the world.

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