Using the Power of Deep Learning for Cyber Security
Introduction The majority of the deep learning applications that we see in the community are usually geared towards fields like marketing, sales, finance, etc. …
Hiring the Right Data Scientist – The Needle in a Haystack Problem
Introduction One of the most common questions asked these days is what makes a good data scientist. The simple answer – it depends. The …
Data Science in the Indian Agriculture Industry
Introduction Agriculture is the backbone of the Indian economy, but the industry currently needs more support than any other. India is a country of …
An Introduction to Graph Theory and Network Analysis (with Python codes)
Introduction “A picture speaks a thousand words” is one of the most commonly used phrases. But a graph speaks so much more than that. …
Demystifying Information Security Using Data Science
Introduction When you search for security data science on the internet, it’s difficult to find resources with crisp and clear information about the use …
Using Data? Master the Science in Data Science
Introduction This past November, Avi Patchava and Paul Meinshausen participated in panel discussions at the Data Hack Summit in Bangalore and were encouraged to …
A Beginner’s Guide to Channel Attribution Modeling in Marketing (using Markov Chains, with a case study in R)
Introduction In a typical ‘from think to buy’ customer journey, a customer goes through multiple touch points before zeroing in on the final product …
A Step Towards Reproducible Data Science : Docker for Data Science Workflows
Introduction My first encounter with Docker was not to solve a Data Science problem, but to install MySQL. Yes, to install MySQL! Quite an …
Introductory guide to Linear Optimization in Python (with TED videos case study)
Introduction Data Science & Machine Learning are being used by organizations to solve a variety of business problems today. In order to create a …
Bollinger Bands and their use in Stock Market Analysis (using Quandl & tidyverse in R)
Introduction Finding underlying patterns and taking decisions is very critical in Stock market. The same skill can be applied to many parallel domains. For …
