When I landed my first data science internship, I hadn’t graduated yet. I was pursuing a computer science degree but managed to get the job even before showing the employer any of my grades or academic transcripts. I got this role only around four months into teaching myself data science.
I have now transitioned into a full-time role at the same company.
The only way I managed to land this job was with the help of my data science portfolio.
In the past year alone, I’ve received multiple data science job offers from organizations all over the world.
I’m currently taking on some freelance jobs while also working full time and would never have had these opportunities without the portfolio I’ve built for myself.
When I first started teaching myself data science, I experimented with datasets on Kaggle — simple ones, like the Boston House Pricing Dataset and the Titanic Dataset.
Over time, boredom started to kick in. These projects had a very similar structure, and the topics didn’t really excite me.
I wanted to work on something I found interesting, so I went online and tried looking for datasets on topics that excited me.
After some time, I came up with a data science project idea. However, I wasn’t able to find pre-existing datasets that had all the information I required to start building the project.
I had to collect data on my own with the help of APIs and web scraping tools, clean the data, and then build a data frame from there. This entire process helped me understand the different data collection techniques I could use to collect data from the web.
The project proved to be a lot harder than I thought, but I learned a lot along the way. I started to get a better grasp of Python and its libraries.
Once I was done with a couple of projects, I thought of sharing them on my GitHub repository.
However, I wasn’t satisfied with just sharing the source code to my project. I wanted to share my findings and the technique I used to collect the data. I wanted to tell a story about all the work I did.
I started writing articles explaining the steps taken to create my project. I submitted these articles to a data science publication.
After creating around 3–4 projects, I built a portfolio website. This website included a basic description of the project, along with the associated source code and article I wrote about it.
This was a way for me to showcase all my work in one place so that anyone who had a link to my site could see everything I’d worked on.
My portfolio website was the only reason I got an internship within the first few months of learning data science.
I had some spare time during my internship. I spent this time learning skills outside of work, building projects, and writing articles about them.
As I started to reach a larger audience through my writing, I’ve had recruiters, and data scientists contact me based on projects I’d written about. I started freelancing and building data science projects for clients on a contract basis.
This was a great way for me to upskill and gain knowledge outside the scope of my day job.
I also received multiple technical writing opportunities, and I occasionally write for publications on a freelance basis.
My portfolio comprises projects I’ve worked on, my blog, testimonies, and clients I’ve worked with. Most of these opportunities have come my way because I have had the opportunity to share my work with the world through writing.
If you are a beginner in the data science industry, I suggest learning new things and writing about them. Write tutorials about concepts you just learned.
If you have a data science project idea, bring the project to life. It might take days, weeks, or even months to do this.
Once you’ve successfully built the project, share it with the rest of the world. Break it down into layman terms and explain everything you did.
This will spark the interest of recruiters when you apply for a data science job. It will also distinguish you from other applicants who only have online certificates listed on their resumes.
Creating projects is also a great way to learn and hone your data science skills. If you’ve taken a data science course or two, then it’s time to put your programming and machine learning skills to use by building something you are passionate about.