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
When many organizations invest in new Business Intelligence (BI) tools and systems, much of the focus is put into the technical requirements of connecting the… Read More »Requirements Elicitation for Enterprise Business Analytics
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 !
Finding the quality of a tennis player by calibrating and analyzing the aces notched up by tennis players or predicting the next Pele or Cristiano… Read More »Scraping 101
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
One of most excruciating pain points during Data Exploration and Preparation stage of an Analytics project are missing values. How do you deal with missing… Read More »How to Treat Missing Values in Your Data
For a service provider, being able to anticipate its customer’s behaviour has three major benefits. It can generate customer delight, prevent customer exhaustion, and improve… Read More »Using Machine Learning to Predict Customer Behaviour
Summary: It’s become almost part of our culture to believe that more data, particularly Big Data quantities of data will result in better models and… Read More »7 Cases Where Big Data Isn’t Better
