The [Marketing Data Scientist’s] Search for a Home: Part I
A thought provoking series that gives my account of becoming a marketing data scientist hybrid during one of the most chaotic times in the marketing industry, the Big Dat...
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Introduction to Principal Component Analysis
This formula-free summary provides a short overview about how PCA (principal component analysis) works for dimension reduction, that is, to select k features (also called...
What Happens in 60-Seconds Online? – Infographics
Originally posted on Data Science Central This infographic on Shopper Marketing was created by Steve Hashman and his team. Steve is Director at Exponential...
Keeping Your Job in the Age of Automation
Summary: What are the real threats of job loss from real and AI enhanced virtual robots? How do we position ourselves and our children to succeed in this new environm...
Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.
Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t las...
Open Source Deep Learning Frameworks and Visual Analytics
Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is...
Beyond SMAC – Digital twister of disruption!!
Have your seen the 1996 movie Twister, based on tornadoes disrupting the neighborhoods? A group of people were shown trying to perfect the devices called Dorothy which ha...
Variable Reduction: An art as well as Science
Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data. With the advent of Big Data and sophistica...
Mapping NYC Common Core Scores
Contributed by David Letzler. Introduction: A Brief History and Description of the Common Core In 2009 the National Governors’ Association and the Council of Chief St...
Avoiding Look Ahead Bias in Time Series Modelling
Any time series classification or regression forecasting involves the Y prediction at ‘t+n’ given the X and Y information available till time T. Obviously no ...