A Database of 800 Analytics Companies
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. At the time the list ...
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Feature Engineering: Data scientist's Secret Sauce !
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...
Debunking the 68 Most Common Myths About Big Data – Part 2
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...
How to Treat Missing Values in Your Data
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 –...
7 Cases Where Big Data Isn’t Better
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 ...
Will Big Data solve the Riemann Hypothesis?
The Riemann Hypothesis is arguably the most important unsolved problem in mathematics. It falls into an area called Analytic Number Theory which is essentially number the...
Data is Not the Enemy of Intuition
Summary: There is a common misconception that data is the enemy of intuition. It’s true that sometimes our clients are misled by their preconceived notions. But all...
How Big Data is Changing the World of Soccer
Did you know that athletes are not only monitored by cameras on stadiums, but also by many quirky devices such as accelerometers, heart rate sensors and even local GPS-li...
Hiring data scientists and dropping the obsession with unicorns
This article was written by Richard Downes. Richard is a Specialist Recruiter / Headhunter in the areas of Analytics, Data Science and Artificial Intelligence / Machine ...
Data is not facts – the impossibility of being unbiased
Best intentions We talk a lot about making decisions based on data but we need to be careful about how hard and fast those decisions are. Our decisions are only as good ...