From the course: 15 Tips for Landing a Data Science Job (2020)

Showcasing your work

From the course: 15 Tips for Landing a Data Science Job (2020)

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Showcasing your work

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- While having a resume matters, having a portfolio, which essentially serves as public evidence of your data science skills, can do wonders for your job prospects. Even if you have a referral, the ability to show employers what you can do instead of just telling them you can do something is a significant added advantage. In this video, I'm going to show you how you can make a compelling data science portfolio. And when it comes to building a data science portfolio, there are three key things to know. So let's start at the beginning. So what exactly is a data science portfolio? One definition is public evidence of your data science skills. This can consist of things like GitHub repositories of your code, a data science blog, which you can use to showcase your communication skills, open source work, answering Stack Overflow questions, participation in Kaggle competitions, and much more. While you certainly don't have to build a massive portfolio, the more quality work you share, the higher chance of your work being seen by a hiring manager at a company you haven't even applied to. The second thing you need to know is how design a data science portfolio to fit with your career goals. If you're looking for a position in machine learning, it's wise to have public evidence of your modeling skills through blogging, code on GitHub, or even a Kaggle Kernel. If your goal is to teach data science, you can create a blog explaining common statistical technique, a machine learning model, or even a visualization. Focusing on projects that are in line with your career goals is also a great way to learn more about your interests. The third point is why it's so important to focus on developing your data science portfolio. Data scientists often utilize GitHub, sometimes reference a Kaggle Kernel, view questions and answers on Stack Overflow, review various technical blogs, and more. If you utilize these tools, you're signaling to data scientists that you're competent, and it can give you social proof. Developing a data science portfolio can also make it so people find you online and see if you're interested in interviewing. I now challenge you to think about the types of things you want to include in your data science portfolio.

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