The BigObject® - A Computing Engine Designed for Big Data
BigObject® presents an in-place* computing approach, designed to solve the complexity of big data and compute on a real-time basis. The mission of the BigObject® is to deliver affordable computing power, enabling enterprises of all scales to interpret big data. With the advances in what a commodity machine can perform, it…Continue
Added by Yuanjen Chen on November 20, 2013 at 5:29pm — No Comments
Before you select the best model based on your favorite goodness of fit statistic – Mean Squared Error, Gini, K-S, AUC, or misclassification rate – STOP! Model performance metrics are not a one size fits all measure. As an analyst, selecting the right performance metric might mean the difference between having an exceptionally good result, and having no result.
The classic example: There is only a 3% prevalence of the event of interest in my…Continue
Added by Laura E. Wood Squier on October 24, 2013 at 8:00am — No Comments
Practicing Data science indeed a long term effort than a learning handful of skills. We ought to be academically good enough to take up this challenge. However, if you think you came a long way from your academic rebuilding, but you still have that zeal & passion to take the oil from the data and fill the skill gap of data science then here is the warm-up tips. Below points must exercised before jumping into…Continue
Added by Manish Bhoge on October 18, 2013 at 9:26am — No Comments
Added by piALGO on October 17, 2013 at 8:32am — No Comments
There’s been a great deal of discussion over the past several weeks regarding data mining and predictive models. Terms like “meta data” and “algorithm” are fast moving from the domain of IT practitioners and into the realm of water cooler discussion. This might be a good opportunity to briefly review some of these concepts in order to better understand data mining practices and standards.
First, some terms.
Meta Data - refers…Continue
Added by James Sullivan on August 15, 2013 at 6:00am — No Comments
The easiest person in the world to fool is yourself. Data scientists sometimes fool themselves - in matters trivial and important. Thus, I strongly suggest that we acknowledge real or subconscious biases in ourselves, the data, the analysis and group think. It is prudent for data science teams to have…Continue
Added by Michael Walker on June 6, 2013 at 12:11pm — No Comments
When creating a predictive model, data miners need to “tune” it to make the right kind of mistakes. Setting the cut-off point between ‘promising’ and ‘unpromising’ depends a lot on our client’s biggest concern -- missed opportunities or false alarms.
Data Mining Misconceptions #1: The 50/50 Problem…Continue
Observational social media research
involves analyzing social media data without intervention or interaction from the researcher. In this mode of research, you search for, look at, collect, synthesize, and analyze data that exist in the social media sphere (blogs, newsgroups, forums, message boards, and microblogs). The goal is to measure…Continue