What Is a Data Scientist Portfolio?

Data science is a popular career path and a growing field. India is becoming increasingly competitive as the need for data scientists increases across various industries. In fact, by 2026, India’s big data industry is set to make up 32 percent of the global market [1]. With this in mind, recruiters want candidates with the qualifications and skills to meet the rigors of the job. Your portfolio is key to giving potential employers examples of your skills and experience in tangible ways. Read on to discover why having a data scientist portfolio is essential, what to include, and tips to boost your chances of securing a data science job. 

What is a data scientist portfolio?

A data scientist portfolio is a curated collection of your work. You can use this to supplement your CV and provide examples that showcase your skills, qualifications, and experience to employers. It includes samples of your work, examples of projects you’ve worked on, and proof of your skills.

Why do I need a data scientist portfolio?

Employers actively seek data scientists with a robust skill set, including experience with programming, machine learning, big data, and data visualisation—all of which you can demonstrate with your portfolio. This valuable tool can help you gain entry-level work and advance your career as you showcase your experience working with data sets.   Creating a data scientist portfolio allows you to put all of this in one place. You’ll find various benefits of doing so, including having a record of your accomplishments to show employers and increasing your confidence. 

Showcasing skills to employers

With competition for data science roles and an increase in people studying for data science degrees, it’s essential to distinguish yourself from your competition. Many recruiters look for proof and examples of your claims, and a resume doesn’t have the space to offer that. 

If you work in data science, much of your work is digital, and a resume isn’t the ideal place to showcase this. You may have evidence of your work in public places such as LinkedIn or on platforms such as GitHub. A data scientist portfolio allows you to pull evidence from various areas and present it in a cohesive format.

Engaging in a community

Creating a portfolio to illustrate your expertise in data science is an excellent way to identify with and network with a community of other professionals in your field. Showcase your portfolio, work on platforms with an engagement element like LinkedIn and Discord, and interact with others to build connections. 

Building confidence 

Having a portfolio where you document your skills, qualifications, and achievements serves as a confidence boost. Organising your work in one place and owning something tangible to show employers highlights how far you’ve come and what you’re capable of. It also can give you talking points during your interview. 

What to include in a data scientist portfolio

When building your data scientist portfolio, you’ll want to include a range of work you have done professionally, during your degree course, and as personal projects to demonstrate all the skills you have to work as a data scientist. 

The below list offers a few examples of what you can include as you begin working on your portfolio.  

Code examples 

Coding and programming are essential skills in data science, and showcasing how you’ve used these in your portfolio is an advantage. If you can deploy code, highlight this valuable skill by deploying projects you’ve undertaken to your digital portfolio. 

GitHub 

GitHub is an excellent platform for creating portfolio examples. Building a presence on GitHub allows you to participate in exciting projects and showcase your results. It’s also a community platform where you can make connections, network, and collaborate on projects. 

Kaggle

Kaggle, an active online data science community, hosts competitions and league tables. A platform like this allows you to participate in discussions, practice your skills, learn from others who are experts, ask questions, and get yourself seen by winning competitions and climbing the tables.

Deepnote

Deepnote is a notebook built for collaboration on data science projects. You can use it to work with others on projects you can later add to your main portfolio. 

Hackathons

Participating in hackathons, where you work with other participants to complete fast-paced data science projects, can help you develop your existing skills and learn new ones. They also provide excellent examples to add to your data scientist portfolio by showing that you can solve problems, participate in a community, commit to a challenge, and build various technical skills. 

Personal projects 

You don’t need to limit the experience you build to professional work only. Personal projects and freelance work help show off your real-world skills and provide a competitive edge. Pick projects relevant to the roles you’re applying for that demonstrate the skills you want to get across.

Tips for creating a great data scientist portfolio

A robust, attention-grabbing portfolio highlighting your unique strengths is essential and can significantly impact your odds of getting a job. Now that you know what to add, let’s look at how to combine it as a portfolio representing you and your skills. 

Think of yourself as a brand

Your portfolio is a sales tool to sell “you.” Use it to pitch yourself as an expert in data science or a more specific field. How you present your portfolio website, the projects you participate in, and your online presence all contribute to your brand. Visually, you may also focus on your brand by creating a logo and using specific colours consistently. 

Keep it relevant

Putting everything you’ve accomplished into your portfolio is tempting, but keep it relevant, just as you would with a CV. Peruse the role's description to tailor your portfolio and pertinent skills and experience accordingly. Update your portfolio regularly with relevant projects and omit older ones that seem outdated or don’t showcase your skills.

Include workplace skills

Including examples of your projects, qualifications, and technical skills is critical. It’s also vital to emphasise relevant workplace skills. Look at the job description of the roles you’re interested in and make sure you evidence the workplace skills the ads specify in your portfolio, such as critical thinking, research, and leadership abilities. 

Getting started with Coursera

Your data science portfolio is powerful in demonstrating your proficiencies and experience working with data. If you’re putting together a portfolio for a career in data science, you might consider taking some online courses and earning new credentials. It can boost your CV, provide more material for your portfolio, and offer proof of your commitment to learning. Check out the IBM Data Science Professional Certificate and the Google Data Analytics Professional Certificate, both listed on Coursera.