3 steps to build your data science portfolio

A few months ago, I wrote an article to answer some of the most common questions and challenges faced by many beginners in data science. After it was published, I started receiving messages from many aspiring data scientists, asking how to build a data science portfolio.

Some of the messages I received on LinkedIn

I was once jobless, with nothing but a piece of paper with three shiny words written on it: Bachelor of Science. I was also once a millennial with zero sense of direction. So the messages resonated with me as I understood their struggles and challenges. This is why I wrote this article to share some lessons from my experience.

Note: As far as data science is concerned, “portfolio” is defined as public evidence of your data science skills, according to DataCamp chief data scientist David Robinson. Given that, this article doesn’t talk about how to create a resume. Instead, it dives straight into the three steps I used (and still use) to build my portfolio in the shortest time possible.

1. Data science internship (or equivalent) 

This includes any related internships, such as stints as a data analyst, data engineer, business intelligence or analyst, and research engineer, among many others.

Why is an internship important? The skills you learn – data collection, analysis, model building, visualization, etc. – can be used in any data science jobs in the market.

Getting a relevant internship is the first step because employers often look for students or fresh graduates with some data science experience. Most importantly, they want to hire someone who can start working on real stuff on the fly with minimum training – 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, your internship shows that you’re serious about the field. 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.”

In my first article, I shared how I looked for a data analytics internship and worked as a part-time intern while studying. That period wasn’t easy. My friends and family were utterly confused, as they had no idea why I didn’t go for a full-time job instead after graduating. But I chose the uncommon path because I believe in my long-term goal.

2. Projects 

In my opinion, there are two types of projects: school projects and personal projects. I definitely recommend going for the latter.

Let’s be brutally honest for a moment. Think about today’s competitive job market. In the sea of candidates seeking for employment, how can you stand out? This is where the power of personal projects comes in.

I’m not saying that school projects are not useful, but they can showcase your capability only to a certain extent. They are not sufficient to convince employers that you’re passionate and good enough.

If you’re doing what everyone else is doing, you’ll get what everyone else is getting.

School projects are typically done in a guided environment and must be completed in teams. Problems are often carefully crafted, and solutions are usually provided at the end.

3. Social media