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In today's data-driven world, having a strong data science portfolio is crucial for landing your dream job or clients as a freelancer. Your portfolio allows you to showcase your skills, experience, and project work to potential employers or clients. While there are paid platforms out there for creating portfolios, you can build an impressive portfolio without spending a dime by leveraging free platforms.

As someone who is passionate about data science and helping others break into the field, I always encourage new data scientists to invest time in building a robust portfolio. A good portfolio typically includes links to data science projects you've worked on, a description of each project, details on your educational background, relevant work experience, and some basic info about yourself. This gives employers or clients a comprehensive view of your abilities.

The project descriptions in particular allow recruiters and clients to quickly assess your technical skills in data wrangling, analysis, modeling, etc. By providing this showcase of real-world experience, you can set yourself apart from other candidates. In this article, I will share the top 5 free platforms you can use to build and share your data science portfolio.

1. Kaggle

Kaggle is a top-notch platform for data science and machine learning. It is highly popular among students who are seeking to enhance their knowledge, discover datasets, and exchange ideas about specific problems. Additionally, it is an excellent platform to showcase your skills and grab the attention of hiring managers. To achieve this, all you have to do is participate in competitions, publish your notebooks/projects, and engage in community discussions.

The Kaggle progression system encourages data professionals to strive for excellence and improve their skills. By receiving upvotes, users can earn bronze, silver, and gold medals, which enable them to progress from Contributor to Grandmaster rank.

Kaggle Portfolio

2. DagsHub

For data scientists looking to showcase their work in a sleek, user-friendly way, DagsHub is a must-try platform and a cooler sibling of GitHub. DagsHub allows you to do almost anything related to data science and machine learning model building, all in one centralized platform designed specifically for ML practitioners and students.

With DagsHub, you can easily host your data science projects complete with code, data ,models, visualizations, experiments, and documentation. It also makes deploying your machine learning models seamless.
 

DagsHub Portfolio

3. LinkedIn

While LinkedIn may be thought of as a general professional networking platform, it can also serve as an excellent way for data scientists to showcase their projects, skills, and achievements. Given LinkedIn's huge user base and focus on careers, it provides a great opportunity to amplify your data science profile for connecting with potential employers or clients.

On LinkedIn, you can share links to your data science writing, such as technical blog posts that demonstrate your analytics chops. You can also post summaries of data science projects you've completed, whether academic, professional, or personal. Displaying certifications in data science or machine learning is another way to signal your technical capabilities.

The platform also allows you to connect with other data professionals, follow companies you're interested in, and join relevant groups. By actively engaging on LinkedIn, you increase your chances of being discovered for data roles or gigs. Essentially, it makes it easy for prospective connections and employers to find you based on your recent data science work and achievements.
 

LinkedIn Portfolio

4. Medium

Medium provides an ideal blogging platform for data scientists to showcase their work and skills by writing about their projects and research. With its simple publishing format and vast readership, Medium is a great way to create a data science content portfolio that demonstrates your analytical abilities and communication skills.

On Medium, you can publish in-depth articles that walk through a data science project from start to finish. Explain how you approached the problem, processed and explored the data, developed and evaluated models, and interpreted the results. You can also write posts that detail your expertise in specific data science techniques and tools.

In addition, you can blog about recent data science research and news to exhibit your knowledge about the field. Writing about new algorithms, breakthrough models, ethical issues, and industry trends shows that you stay on top of advancements.

Medium Portfolio

5. DataSciencePortfol.io

For data scientists seeking a straightforward way to assemble an online portfolio, datascienceportfol.io is a user-friendly, focused platform to showcase your work. Designed specifically for the data science community, datascienceportfol.io makes it simple to create a polished, professional data science portfolio in minutes.

With an intuitive interface, you can easily highlight your education, work experiences, skills, projects, and more on your datascienceportfol.io page. You can provide descriptions of your data science projects that explain the purpose, techniques used, and results. Links to GitHub repositories or live demos can also be included to provide evidence of your hands-on abilities.

Datascienceportfol.io Portfolio

Conclusion

Through my own experience as a data scientist, I've learned first-hand the importance of having a strong portfolio to demonstrate your skills and stand out from the crowd. A well-constructed data science portfolio allows you to put your best work on display for potential employers or clients.

I highly recommend taking the time to build up your portfolio leveraging the excellent free platforms now available. Curate your best projects, write compelling descriptions, and keep your portfolio updated as you take on new challenges. Treat your portfolio as an ever-evolving representation of your data science accomplishments.

Don’t wait until you desperately need a job to start thinking about your portfolio. Begin developing it early on and continue enhancing it throughout your career. Take advantage of platforms like Kaggle, DagsHub, LinkedIn, Medium, and DataSciencePortfol.io to create a robust, shareable portfolio.

A strong portfolio showcases your unique identity as a data scientist. It provides tangible evidence of the value you can bring to organizations seeking your analytics and machine learning skills. Invest in yourself by investing in your portfolio. You’ll be glad you did when exciting data science opportunities come knocking!
 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.