This story was originally published on AI Time Journal and republished with the permission of the author, Admond Lee.
In the article, I briefly talked about the type of portfolio that can help you land your first job in either the data science or machine learning field. However, I didn’t go into detail on how you could actually build that portfolio in the first place.
After the article was published though, I started receiving messages from aspiring data scientists on LinkedIn. These individuals wanted to know how they could build a data science portfolio.
Seeing this, I decided to write this article that focuses on how aspiring data scientists can build their very own portfolios. After all, I, too, was once an aimless millennial with zero sense of direction in my career and life.
In this article, I’ll focus on the three core steps that I’ve personally used to build my own data science portfolio and will show you how you can create your own.
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Consider Taking On A Data Science Internship
When I say internships, I’m not referring to just taking on a data science internship strictly.
Instead, you should consider taking on internships in related fields. These roles can include being a data analyst, data engineer or business intelligence analyst and the like.
The important point is this: As long as the internship requires you do some form of data collection, analysis, model building, or visualisation, the skills learned are highly transferable to any data science jobs in the market.
So getting a data science relevant internship is the first step. But why?
Because employers often look for students or fresh graduates with some experience in data science work. Most importantly, they want to hire someone who can start working on industry-related projects on the fly with minimum training time; simply because time is money in the corporate world.
Also, having a data science internship is a big boost to your portfolio and resume as a whole. Regardless of your academic background, having this internship shows that you’re serious and passionate about working in the industry. It shows that you’re not just another aspiring data scientist who says, “I am very passionate about data science and would like to learn more about it.” Instead, you’re demonstrating that you are, in fact, passionate about the field.
Work On Projects
Getting data science internships is the first step.
But what if you’re just starting out and have zero experience in data science work? How do you gain experience when you’re not given an opportunity in the first place?
The answer is by doing projects.
In my opinion, there are two types of projects — school projects and personal projects. And I’d definitely recommend that you go for the latter.
Let’s be brutally honest to ourselves for a moment. Think about the current competitive job market. In the sea of candidates seeking employment in the data science field, it’s likely that you and someone else would have the same goals. The only thing that would differentiate you and another candidate apart is your experiences.
That said, how can you stand out in the sea of candidates?
The answer? Personal projects.
While school projects are great, they only showcase your capabilities to a certain extent and are not sufficient to convince employers that you’re entirely passionate.
You see, school projects are typically done in a guided environment and assigned to students to work in teams. Problems are often well-framed and solutions are usually provided at the end.
The problem here is: School projects don’t demonstrate your passion in data science because you typically do what was assigned to you. School projects don’t show your full capability as employers can’t differentiate you with your peers in the same team.
On the other hand, personal projects are done outside the course curriculum. There are your side hustles — the side projects that are solely done by you.
Personal projects are able to showcase your passion and capability in data science field with experience beyond the coursework in school.
So, what kind of personal projects can you work on?
Kaggle and Hackathon
Participate in Kaggle competitions. This is arguably the most popular platform for various data science projects and competitions. The community on Kaggle is so vibrant and willing to help and learn from one another.
Not only will you learn a lot from Kaggle competitions, your Kaggle profile and ranking in competitions can also showcase your proficiency in data analysis and models development and optimisation with different machine learning techniques. Same thing applies to hackathons that are organised by other companies from time to time.
However, there is a misconception here. Kaggle and hackathons alone are not enough to qualify you to be a data scientist. They can only add experience and augment your portfolio as well as complement to other projects.
Data in the real world is a mess. What you do well in competitions are simply part of the whole data science workflow. Which is why the next personal project is important — finding and doing your own project.
Create Your Own Project
Find something that you care and passionate about. Identify a problem that you want to solve. Collect data (open source, self-collected data from different sources, or through web-scraping). Apply your knowledge on the data and learn along the journey.
Volunteer To Help NGOs Or Companies — Pro Bono
This is just one of my methods to build my portfolio. The main purpose here is this: Build your portfolio, regardless of whether it is paid or unpaid.
Sometimes what we need is nothing but an opportunity. An opportunity to learn and help other NGOs and companies solve their problems using data at the same time.
The benefits are twofold — you can learn and build your portfolio while adding your values to solve problems for organisations. Who knows? You might be considered for a full-time employment after the completion of the projects.
Imagine now that you have a solid portfolio with data science internships and involvement in various projects.
After spending countless hours on working on your internships and side projects, you know you’re well-prepared with knowledge and experience that could potentially land you a job in data science. But you have nowhere to showcase your capability and portfolio except on a piece of paper also known as your resume.
I hate to say that but the reality is: A resume can only take you this far with little or no social presence at all. Nowadays, the typical way of applying for jobs is through online job portals (JobStreet, Glassdoor, Indeed etc.) — which again, is through social media platforms.
Therefore, having your online profiles is in fact part of your portfolio to get noticed by hiring managers.
Now you may have a question: If everyone has their own portfolios online on social media, then what makes you stand out? My answer — Personal branding.
Personal branding is not about faking your own brand and experience just to impress hiring managers or employers.
In other words, you need to know what you love and find your niche. You need know how to position yourself and market your personal branding by leveraging social media and allowing it to speak for you. You need to provide value to others, while at the same time creating and sharing what it is you love.
Personally, I have three online platforms to develop my personal branding and social presence — Medium, LinkedIn and GitHub — that has helped me in my data science career tremendously. Again, I’m sharing what I have done and what works for me, and hopefully for you as well.
You may be active on Instagram, Twitter or Pinterest and are building your social presence there. This is perfectly fine as long as you are using social media in the right way.
Want to get into the Data Science industry? Get started on your Data Science by signing up for the Python Development class at UpCode Academy here.