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Dr. Vincent Granville is a visionary data scientist with 15 years of big data, predictive modeling, digital and business analytics experience. Vincent is widely recognized as the leading expert in scoring technology, fraud detection and web traffic optimization and growth. Over the last ten years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, change point detection with Microsoft, online user experience with Wells Fargo, search intelligence with InfoSpace, automated bidding with eBay, click fraud detection with major search engines, ad networks and large advertising clients.
Most recently, Vincent launched Data Science Central, the leading social network for big data, business analytics and data science practitioners. Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals and is an invited speaker at international conferences. He also developed a new data mining technology known as hidden decision trees, owns multiple patents, published the first data science book, and raised $6MM in start-up funding. Vincent is a top 20 big data influencers according to Forbes, was featured on CNN, and is #1 in Gil Press' A-List of data scientists.
This famous statement -- the six degrees of separation -- claims that there is at most 6 degrees of separation between you and anyone else on Earth. Here we feature a simple algorithm that simulates how we are connected, and indeed confirms the claim. We also explain how it applies to web crawlers: Any web page is connected to any other web page by a path of 6 links at most.
The algorithm below is rudimentary and can be used for simulation purposes by any programmer: It does not even…Continue
There are numbers that are so large that there is no compact formula to represent them. Think of a number so large, that its number of digits is so large, that the number of digits of its number of digits is so large... and it goes on and on -- you get the idea.
Sure, if you are able to define such a number, then add one, or even 0.5, and you get an even bigger number. But this is not the point. The issue is to come up with such massive numbers in the first place. The biggest…Continue
Big Data News is one of Data Science Central channels. Below is a selection of popular articles published a while back:Continue