This might be one of the most rudimentary scoring systems that I have ever seen, yet I love it; it's based on powerful predictors, and easy to interpret, measure and understand. However, it somewhat…Continue
Usually, data scientists receive unsolicited job interview requests pretty much every day. Yet, there are some projects for which - instead of getting job interview requests - you get the opposite:…Continue
Analytics can go a long way to filter out Inbox messages that have no value (waste of your time), and to identify the right prospects. So what are the metrics to use, to filter our bad leads that…Continue
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.
In this article, I further emphasize the difference between data scientists and other analytic practitioners. I wrote last week that statisticians are to data scientists what astronomers are to physicists: there's some overlap, but less than most people think. Here, I elaborate on this theme.…Continue
Here is a non-exhausting list of curious problems that could greatly benefit from data analysis. If you think you can't get a job as a data scientist (because you only apply to jobs at Facebook, LinkedIn, Twitter or Apple), here's a way to find or create new jobs, broaden your horizons, and make Earth a better world not just for human beings, but for all living creatures. Even beyond Earth indeed. Help us grow this list of 33 problems, to 100+.
The actual number is higher than 33, as…Continue
The success of any big data or data science initiative is determined by the kind of data that you collect, and how you analyze it. In this article, we describe a simple criterion to select great metrics out of dozens, hundreds or even millions of potential predictors - sometimes called features or rules by machine learning professionals, or independent variables, by statisticians.…Continue