Data Science is one the of the few domains where every college graduate wants to get in. It’s primarily driven by the amount of Hype this domain has received in the past — starting from HBR’s “Data Scientist: The Sexiest Job of the 21st Century” article to the recent developer surveys that put Data Scientist as one of the most paid roles. Riding on this Hype, Training Institutes, Educational Institutes and Bootcamps started introducing Business Analytics / Machine Learning / Data Science / Artifical Intelligence courses which a lot of working professionals and fresh grads take with the hope that these courses would land them in a Data Science Job. But it hardly does!
Because, any sane recruiter hardly cares about the names of Courses one has taken rather the emphasis is actually on “The Portfolio” — The stuff that you have done/built to show your passion for Data / Numbers. It could be anything from a Visualization of Public Data to a Github Code of your project with a well-read documentation.
Before we dive in further, I’d like to set a framework for what could make a compelling Portfolio. The portfolio that you would want to build, should be capable of doing the following to you:
In this article, I’m trying to make a point of How one can show off their Data science skills with Kaggle Kernels — where you can build your portfolio — which could be either Visualizations with Storytelling or the state-of-art Neural Nets Implementations.
The name Kaggle highly resonates in the data science community for a Competitive Machine Learning Platform — much like topcoder / hackerrank — in the computer science community. Because of that, A lot of beginners fear entering the world of Kaggle, with this naive assumption that Kaggle is a place for Pros. What most of them (including me at some point) forget is that, those Pros (who are Grand Masters or Masters in Kaggle Ranking) once were Beginners when they joined Kaggle. Moreover, Competitions is not just what Kaggle is all about. Competitions is just one of the tracks available on Kaggle. Kernels is another very powerful track on Kaggle. Kaggle Kernels are very good resource to learn something and also to share something — thus, making it your Data Science Portfolio.
While it’s advisable to have a diversified portfolio — let’s say Blogposts, Github Codes, Slideshare presentations. For those who can’t manage the multi-faceted portfolio, Kaggle Kernels Platform serves as a powerful alternative for all these. While fitting in the Portfolio framework that we discussed above, Kaggle Kernels can also etch your name among the Data Science community.
Thus, you can simply show off your data science skills with Kaggle Kernels which ultimately could help you land in a job.
Let me know your thoughts in the comments, If you agree / disagree with me!