Where does almost every aspiring Data Scientist start ? Andrew Ng at Coursera! And thats a wonderful start, the whole community could not be more thankful to him; and before we know it ( given we like what we learn ); we are deep into Neural Networks, drawing sketches and generating art using TensorFlow. I take no issue with this course of learning, if I were to become an ML Scientist but Data Science - that is a lot more than just building and optimising machine learning models - two equally if not more important pieces of the whole process are Data Cleaning at the begging and Data Visualisation at the end. Focus of this tutorial is the later and the importance of it. Traditional approaches to Data Visualisation are two - either make static charts with no regard to narration design effectiveness or ability to interact or; leave it to 'viz'ards - the specialists in visualisation and design. The first approach is intuitively ineffective and naive but the second one is also not the best way to go about it - since the designer has almost always got too little context about what the problem at hand was and how it was effectively solved. Solution - Make model builders learn effective Data Visualisation.

In this hands-on tutorial we will learn how to use the D3.js library to make visualisations that enable end users to effectively grasp the results that the author wishes to communicate and interact with them to make their own discoveries.