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This article contains phrases taken from the machine learning and analysis world. Data scientists and algorithm engineers will feel more comfortable with reading it although… Read More »Detecting In-App Purchase Fraud with Machine Learning

There is growing demand of data scientists in every organization. For growth of any business enterprise there is need to evaluate data in order to… Read More »8 Essential Tools for Data Scientists

In a prior post I outlined some thoughts on the outlook for the data analytics sector and referenced a database I prepared of analytics companies. … Read More »A Database of 800 Analytics Companies

This image comes from Xkcd, a webcomic of romance, sarcasm, math, and language. Created by Randall Munroe, he is a CNU graduate with a degree in physics. Before starting… Read More »Linear Regression in Astronomy: Cartoon

Originally posted on Analytic Bridge By Dan Kellett, Director of Data Science, Capital One UK Disclaimer: This is my attempt to explain some of the ‘Big… Read More »Making data science accessible – HDFS

INTRODUCTION “Alone we can do so little and together we can do much” – a phrase from Helen Keller during 50’s is a reflection of… Read More »Improving Predictions with Ensemble Model

originally posted by the author on Linkedin : Link It is very tempting for  data science practitioners to opt for the best known  algorithms for… Read More »Feature Engineering: Data scientist's Secret Sauce !

Summary:  Continuing from out last article, we searched the web to find all of the most common myths and misconceptions about Big Data.  There were… Read More »Debunking the 68 Most Common Myths About Big Data – Part 2