Learn how to leverage data exhaust, the digital byproduct of our online activities, to solve problems and discover insights about the world around you. We will walk through a real world example which combines several datasets and statistical techniques to discover insights and make predictions about attendees at O'Reilly Strata.
Many of the tools Google created to store, query, analyze, visualize data are exposed to external developers. This talk will give you an overview of Google services for Data Crunchers: Google Storage for developers, BigQuery, Machine Learning API, App Engine, Visualization API.
Data modeling competitions allow companies and researchers to post a problem and have it scrutinised by the world's best data scientists. By exposing a problem to a wide audience, competitions are a great way to get the most out of a dataset. In just a few months, Kaggle's competitions have helped to progress the state of the art in chess ratings and HIV research.
Virtual worlds are a goldmine of untapped insights, even for predicting physical behaviors. Not only will we share PARC findings and methods developed to extract key data from online games, but more importantly, we'll discuss how social scientists converted and processed raw behavioral metrics into meaningful psychological variables that can be applied to a broad spectrum of business applications.
Moderated by: Alistair Croll
Today's web analyst has moved far beyond funnels and visitors. Automated systems decide who gets what content, and language parsing tries to distill sentiment from millions of online interactions.
This panel will look at where web analytics is headed, and how new algorithms and approaches are yielding fresh insights into online commerce.
With growing amounts of digital data at the fingertips of software developers the need for a scalable, easy to use framework is tremendous. This talk introduces Apache Mahout - a project with the goal of implementing scalable machine learning algorithms for the masses.