This is the fourth post in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can subscribe at the bottom of the page.
In the past few posts in this series, we’ve talked about how to build a data science project that tells a story, how to build an end to end machine learning project, and how to setup a data science blog. In this post, we’ll take a step back, and focus on your portfolio at a high level. We’ll discuss what skills employers want to see a candidate demonstrate, and how to build a portfolio that demonstrates all of those skills effectively. We’ll include examples of what each project in your portfolio should look like, and give you suggestions on how to get started.
After reading this post, you should have a good understanding of why you should build a data science portfolio, and how to go about doing it.
What employers look for
When employers hire, they’re looking for someone who can add value to their business. Often, this means someone...