5 Awesome Free Websites To Host Your Data Science Portfolio
Including their pros and cons
So you’re a data scientist, right? Or you’re trying to break into a data career? Perhaps, you’re somewhere in the middle.
Regardless of where you are in your data science career, having a solid portfolio that sufficiently represents your skills, background, work ethic, and everything there is that qualifies you as a data scientist is important.
First, what is a portfolio?
A portfolio is where you can showcase your projects, highlight your expertise, and generally leave a lasting impression on potential employers or clients.
Your portfolio is what sets you apart from the thousands of other data scientists out there.
Now, you’re a data scientist, not a designer or developer. So an astounding portfolio for a data scientist is not necessarily one that’s flashy or animated.
But rather, it’s one that has very relevant and interesting projects. So, take note: interesting projects over flashy designs.
Your portfolio also needs to be easy to navigate through, it needs to take a simple structure, where everything is displayed in a neat format.
Taking all of these things into consideration, I’ve put together a list of free websites that can host your data science projects.
Each platform has its advantages and disadvantages, so let’s dive in and find the perfect fit for you.
Although, I recommend having all 5 of them (wink!)
Datascienceportfol.io
Datascienceportfol.io is more like a general container for all your data science portfolios.
It’s a single-page website that allows you to display your data science projects in a clean format but it doesn’t have an environment for running codes or notebooks so you’ll have to link your other portfolios to it.
As I said, it’s a general container for all your data portfolios.
Pros:
- Neat display format of project, skills, etc
- Loads the content very quickly
- Easy to navigate
- Easy to set up
- Displays other projects for inspiration
Cons:
- No code or notebook environment
- Isn’t self-sufficient; needs to be linked to other portfolios
- Doesn’t categorize projects well enough
- Only takes a maximum of 4 external (social/ portfolio) links
- Limited customization options
Dagshub
Dagshub offers much more than just portfolio hosting.
It provides a collaborative environment for data scientists to work on their projects, collaborate with teammates, and showcase their work to the wider community.
With Dagshub, you can not only host your data science portfolio but also have a dedicated space to store, version control, and share your code, datasets, models, and visualizations.
Pros:
- Neat and simple display
- Easily track changes and manage your codebase efficiently
- Collaborate with people on a project
- Environment for hosting Jupyter notebooks
- Integration with Git
- Powerful data visualization capabilities
- Offers built-in support for popular data science libraries and frameworks
Cons:
- Steep learning curve if you’re not familiar with Git and version control
- More suitable for collaboration rather than showcasing projects
- Limited customization options for portfolio design and layout
- No room for connecting other social links
Kaggle
Kaggle is probably the most exciting out of all of them.
It brings together data techies in the most interesting ways possible.
Also, it provides a wide range of resources and opportunities for you, such as data sets, competitions, courses, and a collaborative environment.
Pros:
- An active community of data scientists and potential employers
- Built-in notebook editor
- Interesting competitions for showcasing your skills and earning recognition.
- Allows you to integrate your portfolio and social media links with your profile
- Provides a wide variety of datasets to work with
Cons:
- Limited customization options for portfolio layout and design
- Projects are public by default
- Limited storage capacity for hosting large datasets or multiple projects.
- Difficult to navigate
Github
GitHub is probably the first portfolio you should consider as a data scientist (or as anyone pursuing a tech career).
It’s a web-based hosting service for version control, which allows you to manage your code and collaborate with others.
It’s like a big playground for all things code-related, including data science projects.
Pros:
- A common platform for developers and data scientists all alike
- Offers free hosting for static websites through GitHub Pages.
- Displays code, visualizations, and Jupyter notebooks.
- Provides version control with Git, making it easy to track changes and collaborate.
- Customizable domain name and profile
- A wide community of developers and potential employers.
- Allows you to highlight your most relevant projects
Cons:
- Not so easy to setup and configure without prior knowledge
- Lack of built-in environment for running code or notebooks (only displays it)
- Difficult to navigate through
- Limited to a maximum of four external links for each project.
Machine Hack
Machine Hack is more like a social media platform but just for data scientists.
It’s a great way to show your expertise because it allows you to publish articles, participate in hackathons, get your hands on real-world projects, start up discussions, and even chat with fellow machine hack users.
Oh, and it’s perfect for newbies looking for inspiration and projects that involve simple concepts only.
Pros:
- Real-world challenges and hackathons
- Diverse datasets and problem statements (from very simple to very complex)
- An active community of data scientists and potential employers
- A dedicated section for blog writing
- Perfect for newbies
Cons:
- No dedicated section for hosting and displaying your personal projects
- No project categorization
- Maximum of 4 external links
- Difficult to navigate
Conclusion:
So there you have it — five awesome free websites to host your data science portfolio.
Your choice of platform depends on your specific goals and preferences.
Whether you prioritize simplicity, collaboration, community engagement, or integration with other tools, these five websites offer a range of options to showcase your data science skills and make a lasting impression on potential employers or clients.
As for me, I use all five of them and you should too!
If you liked this…
Feel free to buy me a coffee by clicking on the button below :)
Also, please leave as many claps as you can (up to 50) so other data scientists would see this article.
For more Data Science related blogs, leave a follow ;)
