Information Spectrum of Diffusion – What Analysts Need to Know

Quandl, which is a source of financial, economic, and alternative data, created a graphic called The Spectrum of Diffusion, describing the stages of information diffusion (accessibility) from “untapped” to “fully commoditized.” By Raquel Sapnu, Quandl. Throughout the history of capital markets, investors have used different tools to analyze investment opportunity. The tools available have increased exponentially with the advent of…
Original Post: Information Spectrum of Diffusion – What Analysts Need to Know

Call for bids to host KDD-2019, Premier Research Conference on Data Science and Data Mining

ACM SIGKDD Executive Committee hereby invites proposals to host the annual KDD Conference in 2019. The conference should take place in August 2019. By Ankur Teredesai, SIGKDD. KDD is the flagship conference of ACM SIGKDD and the premier research conference on data science and data mining. Proposals for hosting KDD-2019 should include information on (if you do not have all…
Original Post: Call for bids to host KDD-2019, Premier Research Conference on Data Science and Data Mining

Deep Learning Singapore & Machine Intelligence NYC – KDnuggets Offer

           DEEP LEARNING SUMMIT           20 – 21 October 2016           Singapore           Confirmed speakers include:           – Principal Solution Engineer, NVIDIA           – CEO & Founder, Eyeris           – Intern, GoogleBrain           – Engineer, Yahoo Japan     …
Original Post: Deep Learning Singapore & Machine Intelligence NYC – KDnuggets Offer

Data Science Basics: Data Mining vs. Statistics

As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes. When I was first exposed to data mining and machine learning, I’ll admit it: I thought it was magic. Make significant predictions with accuracy? Sorcery! Curiosity,…
Original Post: Data Science Basics: Data Mining vs. Statistics

Using R to detect fraud at 1 million transactions per second

In Joseph Sirosh’s keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card…
Original Post: Using R to detect fraud at 1 million transactions per second

“Find the best algorithm (program) for your dataset.”

“Find the best algorithm (program) for your dataset.” Posted by Andrew on 28 September 2016, 9:28 am Piero Foscari writes: Maybe you know about this already, but I found it amazingly brutal; while looking for some reproducible research resources I stumbled onto the following at mlcomp.org (which would be nice if done properly, at least as a standardization attempt): Find the best algorithm…
Original Post: “Find the best algorithm (program) for your dataset.”

Brainwaves hackathon on Machine Learning

This hackathon aims at attracting top developers for a 30-hour build session focused on Machine Learning. The first qualifying event will be held online in October.Societe Generale Global Solution Centre (SG GSC) is hosting the third edition of its annual hackathon, Brainwaves, on November 12-13, 2016.The event aimed at bringing together some of the brightest minds together for a 30-hour…
Original Post: Brainwaves hackathon on Machine Learning

Introducing the eRum 2016 sponsors

Guest post by Maciej Beręsewicz. It has been a very active semester in Europe regarding R meetings. After two successful main events: satRday in Budapest, and EARL in London, now is the turn for eRum 2016, the first European R users meeting. More than 250 data scientists will meet in Poznan, Poland, from 12th to 14th of October, to discuss…
Original Post: Introducing the eRum 2016 sponsors