Build a data science portfolio with these easy steps and get hired!

‘Data science!’, if you are thrilled to bits after reading this word, you are meant to do something bigger for this technology-driven world. But are you prepared to give it your best shot? Do you think that you are doing justice to your passion by coding a few projects in a year? If yes, will mentioning these projects on your resume lead you to get a satisfactory job? Have you heard of the data science portfolio? Have you built one? Is it aligned with standards to attract clients for your dream start-up? Well, take a deep breath and dive into this article to put your questions to rest.

Surprisingly, a large number of data science enthusiasts have not yet realized the importance of a portfolio. Let’s face it - just your degree or a well-brushed resume won’t get you a solid career anymore. With more than a dozen job applications each day, HR’s do not have enough time to go through hundreds of lines, objective statements, and cover letters. So, to help you stand out from the crowd, we have rounded up 5 steps to create a data science portfolio. These portfolios are extremely critical to have, to represent real-world experience in the most systematic way possible. Let’s get started.

Step 1: Create a portfolio on GitHub 

It is the first name that hits the mind while making a data science portfolio. It serves as a perfect platform to access and display hands-on ability to solve problems. One can either present each project as a standalone repository or can collate all the repositories together by creating a website with the help of GitHub pages. As per the reviews of users here are some of its advantages and disadvantages, which can be helpful for some.

Step II: Participate in Kaggle competitions

To add projects to the data science portfolio, one will need to build projects, and what can be better than using Kaggle? A large chunk of the data science community joins competitions on Kaggle to solve real-world ML problems. It provides huge benefits at low cost and at a zero pay to data scientists. So, roll up your sleeves and participate in the predictive modelling and analytics competitions posted by the companies and researchers. Showcase your best ability and engage with the top data miners to produce the models for predicting and describing the data. 

Step III: Create projects using Google Cloud Platform

It is a web-based, graphical user interface that hosts a number of tools for showcasing your work. You can also explore BigQuery, Google's cloud data warehouse product. By getting access to its data, you can use cloud-hosted notebooks, model deployment tools and machine learning API’s to create your projects. Some of the advantages and disadvantages of GCP for creating a data science portfolio are as follows.

Step IV: Share insights on social media

When it comes to social media, streamline your inputs majorly on LinkedIn and Twitter. Put up articles you have written, or draft engaging posts that can attract a possible data scientist community. Be confident to share your participation in the data science competitions and feedback regarding the same on your handle. Get in touch with experts in the field, follow their blogs, and stay abreast with the latest trends in the industry. 

Step V: Keep creating and learning

After extreme lows and amazing highs, don’t give up. Yes, it will take time to stand tall among the best minds of the world but for that, you need to keep creating and learning. Don’t let your portfolio lie untouched. Get your juices flowing, approach people in the industry, keep updating your projects and make sure you showcase them nicely in your portfolio. So that when opportunity knocks your door, you don't hesitate to grab it. Ignite your coding spirit and do wonders!

Here is a list of other resources that can be helpful to you: 

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