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 Data’ co...
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 achievements and successf...
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 tool to the...
originally posted by the author on Linkedin : Link It is very tempting for data science practitioners to opt for the best known algorithms for a given problem.However...
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 Ronaldo after training ...
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 a lot more than we t...
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 values –...
For a service provider, being able to anticipate its customer’s behaviour has three major benefits. It can generate customer delight, prevent customer exhaustion, a...
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 therefore better ...
For almost as long as we have been writing, we’ve been putting meaning into maps, charts, and graphs. Some 1,300 years ago, Chinese astronomers recorded the position of...
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