Introduction

Portfolio projects are key for any Data Science. Not only do they showcase your work, abilities and strengths to recruiters, but they are also a great way to apply your learning.

Unfortunately, many people don’t really craft their portfolio projects to the best it can be. Instead, they write some code, put it up on Github, and possibly write an article about it, or, they do a Kaggle competition and drop the code on Github.

This unfortunately is **not **a great way of creating portfolio projects. Why? because the model you have created is just living in a notebook, thus you don’t learn about model deployment. Second, most of these competitions have clean datasets for you to use, which is quite a contrast to real world data, so you are not really doing the hard part of obtaining the data.

I this article, I will share with you the following:

• How to come up with great ideas for a portfolio.
• Where you can find free open datasets for your next portfolio project
• How to approach a portfolio project
• 2 essential steps to take in order to make the most of your portfolio project

So, sit back, relax, and enjoy the article!

Step 1: Thinking of great ideas for a portfolio project

This is arguably the hardest part of a portfolio project, because sometimes you may have all the skills to do a project, but you don’t know where and how to apply it!

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