Data vs Information vs Insight

This was a session run by me as part of The Data Lab series in Multiverse. In this session, walk through some real-world scenarios where insight, data and information are used to meet business goals. At the end of the session, I give the attendees a chance to apply your learning to a group task. In this session, you’ll learn: What data, information and insights are and the difference between them....

July 27, 2021 · 1 min · Ashray Shetty

The Significance Of Knowledge Graph

I don’t know anything about conversational systems For a long time I have been wondering how a conversational agent might work, and I wasn’t been able to figure it out. The first thing I tried to do was figure out what a knowledge graph means, and I found out that the definition for it is not very clear. In particular, I could not understand the difference between knowledge base and knowledge graph....

March 7, 2020 · 4 min · Ashray Shetty

Graph Embeddings For Networks- Brief Introduction

The world around us is composed of systems, whether its solar system, digestive system, ecosystem or social system, they all have different entities, objects, or players that work together and share complex relationships with each other. Networks act as a general language for describing and modelling such complex systems. For instance let’s say we have 10 objects, they can be anything from people, phones or genes, we represent them as dots (or nodes)....

February 23, 2020 · 5 min · Ashray Shetty

Centrality in Graph Theory

One of the key points of Graph Theory is that it conveys an understanding of how things are interconnected via nodes (points where various paths meet) or edges (the paths connecting the nodes). There are a number of different types of graphs, of which the most well-known are digraphs (directed graphs, whereby A may lead to B, but the reverse may not be true), an undirected graphs (where there is no implied directionality)....

February 16, 2020 · 5 min · Ashray Shetty

Molecular Machine Learning: Introduction to building ML models to predict molecular properties.

Drug discovery is a costly and lengthy process, filled with lots of false leads and unsuccessful efforts. The pharmaceutical industry is one of the most heavily regulated industries in the world with many rules and regulations enforced by the government to protect the health and well-being of the public. The probability of a drug passing a clinical trial phase is less than 12%, most drugs failing due to safety reasons [1]....

January 3, 2020 · 8 min · Ashray Shetty