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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.
In a recent article (February 2019) published in Forkes (see here) it was argued that there will be no data science job titles by 2029. The author wrote that Automation is coming for many tasks data scientists perform, including machine learning.
I disagree. If you haven't automated most of your tasks yet, you are not…Continue
Black hat data science consists of techniques designed to fool existing algorithms (Google search, Amazon rankings, and so on), compromising or tampering with the metrics -- especially ratios -- that they rely on, without actually physically touching or altering data stored in their databases. It exploits flaws in these algorithms, and it also relies on reverse engineering, to achieve its goal. So black hat data science is different from traditional hacking,…Continue