We live in a world of Information overload!
Google throws 56,40,00,000 in 0.54 seconds when I search it for “Learn data science“! This still does not reveal the entire picture – it probably hasn’t searched through all the YouTube videos, the GitHub repos, the presentations on SlideShare, numerous blogs and discussions happening on the topic!
We come across people daily who are following too many things and chasing too many directions in their attempt to learn data science. It is hard to blame them, when there is so much content to absorb. These people start doing a MOOC – 2 videos down the line, they switch on to a blog, then a notebook and end up reaching nowhere.
That is why we created learning paths. Learning paths are meant to provide crystal clear direction for end to end journey on various tools and techniques. So, if you want to learn a topic, all you have to do is to follow a learning path.
Not only this, if you have already started your learning, you can pick them up from your next step or see which steps have you missed in past. We think that learning paths would be immensely helpful to our audience. They will act like light houses for people doing their knowledge journeys on data science.
So here are the learning paths we have created:
Brand new comprehensive learning paths for 2019:
A comprehensive Learning path to become a data scientist in 2019
A Comprehensive Learning Path for Deep Learning in 2019
Other learning paths:
Business Analyst using SAS
LeaRning Data Science on R – step by step guide
Data Science in Python – from a python noob to a Kaggler
Data Visualization with QlikView – from starter to a Luminary
Data Visualization expert with Tableau
Machine Learning with Weka
Interactive Data Stories with D3.js