Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field.

Platforms for data science portfolio building. Image by Benjamin O. Tayo.

In the modern age of information technology, there is an enormous amount of  free resources for data science self-study. As a matter of fact, you can even design your own data science curriculum from the innumerable amount of available resources. While knowledge acquired from course work is essential to lay a good foundation in data science, you need to remember that data science is a practical field. As such, hands-on skills are very important, especially if you are interested in working outside academia as a practicing data scientist.

This article will discuss 4 important platforms that will enable you to build a portfolio to showcase your experience in data science. A strong portfolio will give your employer an edge over the competition in attracting the best possible talent in the workforce. Keep in mind that employers interested in hiring you are going to ask you to provide evidence of completed data science projects. This famous quote from Elon Musk summarizes the mindset of employers in any technical discipline, including data science:

“Generally, look for things that are evidence of exceptional ability. I don’t even care if somebody graduated from college or high school or whatever… Did they build some really impressive device? Win some really tough competition? Come up with some really great idea? Solve some really tough problem?”

A strong portfolio highlighting a list of completed projects, recognitions, and awards will serve as evidence of your competence in data science.

Before delving into the topic of building a good data science portfolio, let’s first discuss 5 reasons why a data science portfolio is important.

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