What they are, what they’re not and why you should focus on getting your hands dirty. I thought that I would first have to be working professionally as a data scientist to have a portfolio.
The word portfolio used to scare me. It sounded so professional and unattainable. I didn’t know what exactly was expected of me. I thought I would have to make my projects look very sleek and perfect. I thought it would have to include projects with some sort of breakthrough or a novel idea in data science. I thought that I would first have to be working professionally as a data scientist to have a portfolio. I thought maybe if I didn’t do projects for a client or in a professional setting, they wouldn’t matter. And it didn’t make it better that most advice on the internet about finding a job mentioned having a portfolio.
I’m pretty sure, as an aspiring data scientist, you hear about the importance of having a portfolio every other day. But there is no reason to be anxious about it. Let’s talk about portfolios.
Don’t be overwhelmed by the word itself. People like using fancy words and portfolio is a fancy word for saying a collection of your works. It is a collection of things you worked on, you put effort and thought into. Anything that illustrates what you learned so far and clever solutions you thought of.
Your work doesn’t have to be professional. There doesn’t have to be stakeholders. Think about it from an employer’s point of view. Before they hire someone, they want to be able to see with some evidence, what the applicant knows. Python might be one of the skills you mention on your CV, but can you show a piece of your code in Python? Your cover letter might say that you finished five online courses but can you show a project where you analysed the data and trained a machine learning algorithm yourself?
Employers mostly just want to see some evidence. Your work doesn’t have to be cutting-edge or perfect. It just needs to be somewhere where they can see it and look through it.
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