What Should I Include in My Data Science Portfolio?
In the last few years, the field of data science has presented a huge opportunity for forward-thinking career-focused individuals. Almost every organization from non-profits to even traditional agriculture businesses is beginning to incorporate data into their operations. As a result, data scientists are in high demand. As a matter of fact, 93% of employers say their workforce lacks data skills to achieve optimal productivity. But, as you probably already know, high market demand inspires increased supply.
There are more data scientists now than ever. That poses a challenge for the average scientist looking to make a career in the field. Without a chance to work with good clients, budding scientists may never become pros. To give them this chance, they have to present themselves as beyond-average practitioners of the field of DS. One of the ways to do this is by having a portfolio that speaks loudly of your proficiencies and personality.
This article outlines the important elements to include in your data science portfolio as well as how to present it to convert your next prospect into an actual employer.
The perfect structure for your data science portfolio
The ideal data science portfolio should highlight the scientist’s personal qualities as well as his professional qualities. To stand out, your portfolio should have the following sections.
- A brief bio
Bios are important in virtually everything we do. People want to meet you. You’re first a human before a data scientist. You should put the human foot first even in your portfolio.
Ideally, a bio should be succinct. It should cover who you are as a person; your values and personal principles; hobbies and interests; and aspiration.
Next, it would be great to highlight your strengths, skills, and qualifications/certifications relevant to your data science career. These qualifications don’t necessarily have to be directly related to data science. For instance, you can highlight how your qualification as a journalistic investigator has facilitated your data-sourcing capabilities.
Another thing to include in the bio section of your data science portfolio is your professional experience. This should be a brief summary of the organizations you’ve worked with, the projects you worked on, and your key responsibilities while working on those projects.
Remember, your portfolio reviewer isn’t looking to read an autobiography. So, keep it very short and straight to the point. The perfect portfolio bio should cover every important piece of information in no more than 250 words.
- Outline of technical skills
Right after the brief bio, it would be great to highlight your technical skills. In this section of your portfolio, feel free to completely geek out on your data science abilities. Feel free to brag if you have to, but try not to sound arrogant.
You could have a wide range of skills as a data scientist, especially if your experience is vast. Putting all of these skills into this section of your portfolio can make it appear cumbersome and unattractive to the reviewer. So, you should focus on the skills that are relevant to the job role that you are looking to close.
However, you can also show off your vast skill set if you know how to organize them in an interactive way. If you are building a portfolio website, you should consider categorizing your skills with pull-down functions. This way, your portfolio reviewer can choose which technical skill sets to look at without getting overwhelmed with the usually conflicting skills that you had to develop in your journey through life.
- Show your soft skills
Being a data scientist is more than being good with numbers. While being good with numbers and stats is a good quality, recruiters want to know how good you are with people.
In your portfolio, you should include a section that highlights your soft skills. These skills can include effective communication, collaboration, emotional intelligence, delegation, and leadership skills, among others.
With these in your portfolio, you can show potential clients that you are a good team player as well as an effective leader. This is particularly important since most businesses will prefer to work with a scientist with the potential to grow with their company.
- Display your projects
One of the most important parts of any portfolio is the projects that you’ve worked on. This part shows your proficiencies with the various areas of data science.
Given that most portfolios these days are web-based, you should try to make this section of your portfolio as interactive as possible. You should build this section in a way that will allow the reviewer to engage with and use your projects in real time.
For instance, if you built a real-time weather analytics engine using weather data from several regions and seasons, your portfolio reviewer should be able to play around with the engine, looking at weather analysis across regions. Heck, they might want to see what the weather in their hometown looks like.
If your projects can’t be displayed in an interactive way on your portfolio site, you should consider including links to the live project. This way, reviewers can follow the link to test your creations on the appropriate platform.
Whatever option you choose, however, don’t forget to include a descriptive paragraph, or two (and even three), about each project. By doing this you can put your projects in perspective for the reviewer. Given that most portfolio reviewers (HR professionals and project managers) are not techies, relatable descriptions of your projects are crucial for helping them understand your skills.
- Use social proof to increase relevance
Simply telling prospects that you’re a good data scientist is not enough to inspire them to work with you. To complement your projects, sound technical skills, and admirable soft skills, you should include social proof to show your portfolio reviewer what others are saying about you (and your skills). Incorporating social proofs into your portfolio can increase your chances of landing your next job by 34%.
To do this, feel free to reach out to your previous clients to ask for their reviews on their experience working with you. This can make potential clients trust you in an instant. Ideally, reviews from up to 10 previous clients would be of great impact to your career since the average reviewer reads up to 10 reviews before making a decision about a candidate.
However, the more social feedback the better. People typically consider a portfolio with more than 40 reviews more trustworthy. This is because a lot of online reviews these days can be gamed. It is very unlikely that an individual will single-handedly receive up to 40 positive reviews from paid reviewers.
- Provide your contact information
You probably have a stunning portfolio that can make anyone want to hire you. But, what use would it be if you don’t give them a way to contact you?
After showing what you have to offer, it is imperative that you include a way for potential clients to contact you. Depending on your preference, this section should include your active email address, contact phone number, fax, and, if you’re not big on privacy, home/office address. Social media handles can be really useful in this section as well.
What types of projects to include in your portfolio
When it comes to the project section of your portfolio, it’s important for you to give it some special attention because that’s what backs up everything you’ve said about your technical and soft skills, as well as your personal principles.
The most effective portfolios contain a mix of two types of projects: code-based projects and content-based projects. Both are discussed below.
- Code-based projects
Code-based projects are what a majority of data scientists focus on. It includes all the technical projects that you’ve worked on and they are presented in a rather illustrative way, featuring dashboards, charts, and graphs.
This type of project is very important for a data scientist to have in their portfolio because it shows –rather than tells– what they are capable of.
Certain data scientists, especially seasoned ones, may have a load of code-based projects to include in their portfolio. Don’t do it. Having too many projects in your portfolio can easily overwhelm the reviewer. Instead, curate the projects that are most relevant to the career that you’re pursuing.
- Content-based projects
It’s easy to state your remarkable communication skills and thought processes in your portfolio. However, it is not that easy to show how remarkable you are in those areas. Including content-based projects is one of the very few ways to show how good a communicator you are.
Content-based projects include blog posts, technical how-to guides, and community forum contributions that you’ve created. When creating your portfolio, it is helpful to include blog posts that you’ve written discussing topics that are related to the code-based projects that you’ve worked on. This can help position you as a subject matter expert in that specific area.
Conclusion
In many creative careers, including data science, having a portfolio is crucial for landing new jobs. It turns out that having the perfect portfolio can be a tad tricky. But it doesn’t have to be. Instead of having the robotic portfolio common among techies and scientists today, you can humanify your portfolio by including a brief introduction of yourself followed by a breakdown of your proficiencies without neglecting the increasingly valuable soft skills.
Right after, you should show what you’re capable of by incorporating both code-based and content-based projects into your portfolio. Furthermore, you can increase your potential employer’s trust by incorporating some social proof into your portfolio. And, when they do trust you, don’t forget to give them an opportunity to reach out.
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