Can someone show what the trajectory of an object falling on a "pancake-shape" Earth would be, according to the gravitation law? Also if Earth is flat but not infinite, it must have edges. Since…Continue
What do you think? Below is my answer.Using tools that your competitors are not using, or developing ad-hoc solutions, and mastering them, is more important than the tool itself. Home-made solutions…Continue
I came up with a few ideas to either create new insurance companies, or better, for existing insurance companies to sell new products. I am wondering if any of the following already exists, and if…Continue
Data science pioneer, founder, author, CEO, investor, with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT).
Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.
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. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.
Vincent's profile is accessible at http://bit.ly/1jWEfMP and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.
Data scientists spend 80% of their time preparing and cleaning their data. They spend the other 20% of their time complaining about preparing and cleaning their data.
This was posted by Kirk Borne on his Twitter account. Not sure who created the cartoon. Do we all spend 80% on our time on something, and the remaining 20% on something else? In my case, I spend 20% of my time writing articles (usually research articles that the layman can understand, and sometimes articles like…Continue
These are not business questions, but soft questions that should make any PhD candidate relaxed, even intrigued, and open to talk freely. There is no wrong answer, these are open questions, but some answers could hint that the candidate is still in his/her PhD bubble, feeling superior, not flexible, and unable to see the big picture behind the apparently innocent question. These questions were asked discretely, none of the responders knew about my PhD mathematical background. …Continue
Four pictures were posted recently on Data Science Central, and have immediately become popular. They are designed as one-page tutorials on some specific (basic or advanced) topics. Click on the links below to find those related to the subjects that you are interested in.
Four Great Pictures Illustrating Machine Learning ConceptsContinue