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Cambridge, Mass. – March 4, 2011 – Via Science announced the acquisition of Dataspora, a predictive analytics firm that helps companies solve complex big data problems. The acquisition helps strengthen Via Science’s positioning to support the consumer packaged goods and retail sectors, areas of focus for Dataspora. REFS™ provides the ability to leverage causal mathematics at scale with its supercomputing platform. This allows decision-makers to make better use of data with mathematical models that can diagnose problems or predict future outcomes. Via Science has invested over 10 years and $25 million to prove the value of REFS™ in high-stakes problem areas such as precision medicine and quantitative trading. Dataspora has experience leveraging predictive analytics in numerous industry verticals. Via Science has integrated the knowledge acquired, and will continue to target the core sectors Dataspora pioneered. About Via Science Via Science = Big (Math + Computing + Data)

http://www.viascience.com/via-science-acquires-dataspora-a-pioneer-in-predictive-analytics/

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How the Big Banks stack up on Facebook, Twitter Social media is gaining traction in the financial services industry. Even the big banks are showing an eagerness to explore Web 2.0 technologies. Here, FierceFinance presents the social media activity of some of the major players on Wall Street. Hover over the various data points to see how each bank stacks up. Dashboard 2 <br /><a href="#"><img alt="Dashboard 2 " src=" height="100%" /></a> So far, six of the top banks command a large Facebook presence. What is data science? We’ve all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean? Why do we suddenly care about statistics and about data? In this post, I examine the many sides of data science — the technologies, the companies and the unique skill sets. The web is full of “data-driven apps.”

An Introduction to R Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. Predictive Modeling and Data Mining Scientists/Analysts at Obama 2012 Presidential Campaign, Chicago, IL analyze the campaign data to guide election strategy and develop quantitative, actionable insights that drive our decision-making. Looking for people at both the senior and junior level to join the campaign Analytics Dept through November 2012. Company: Obama 2012 Presidential CampaignLocation: Chicago, ILWeb: www.barackobama.com The Obama for America Analytics Department analyzes the campaign's data to guide election strategy and develop quantitative, actionable insights that drive our decision-making. Our team's products help direct work on the ground, online and on the air.

45 Powerful CSS/JavaScript-Techniques - Smashing Magazine Advertisement CSS and JavaScript are extremely powerful tools for designers and developers. However, sometimes it’s difficult to come up with the one excellent idea that would solve a problem that you are facing right now. Good news: almost every day designers and developers come up with fresh and clever CSS tricks and techniques and share them with other developers online. We regularly collect all these tricks, filter them, sort them, revise them and prepare them for Smashing Magazine readers. In this post we present 45 useful CSS/JavaScript-techniques that may help you find clever solutions to some of your problems or just get inspired by what is possible with CSS.

What is a data scientist? Everybody loves a data scientist: ever since Google's Hal Varian told the world that the sexy job in the next ten years will be statisticians. People think I'm joking, but who would've guessed that computer engineers would've been the sexy job of the 1990s? That, combined with the McKinsey report into big data last year is a powerful blend. The R programming language for programmers coming from other pro IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages: Powered by Translate How Fab.com Raised $40 Million With A Lot Of Data And No Pain Let’s face it, fundraising can be a real pain in the ass for the entrepreneur. It takes up a ton of time that can be otherwise spent managing the business. Sure, it’s a necessary evil, but it’s also typically a big distraction. It’s also a lot like dating.

Why Not Space? 24 views this month; 0 overall Ask a random sampling of people if they think we will have colonized space in 500 years, and I expect it will be a while before you run into someone who says it’s unlikely. Our migration from this planet is a seductive vision of the future that has been given almost tangible reality by our entertainment industry. We are attracted to the narrative that our primitive progenitors crawled out of the ocean, just as we’ll crawl off our home planet (en masse) some day.

San Francisco startup make data science a sport SAN FRANCISCO (AP) — Strange secrets hide in numbers. For instance, an orange used car is least likely to be a lemon. This particular unexpected finding came to light courtesy of a data jockey who goes by the Internet alias SirGuessalot, who in fact wasn't guessing at all. Instead, he and his partner, PlanetThanet, relied on the hard math skills that make them top contenders in a sport tailor-made for the 21st century: competitive number-crunching. The used car defect prediction contest is one of dozens hosted by San Francisco online startup Kaggle, whose creators believe they can tap the global geek population's instinct for one-upmanship to mine better answers faster from the world's ever-rising mountain of data. "Competitions bring together a wide variety of people into a wide variety of problems," said Jeremy Howard, who became Kaggle's president and chief scientist after winning multiple competitions himself.

Guide to Getting Started in Machine Learning Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide. This post will be a living document…I’ll keep adding to it, so please suggest additions and make comments. Fortunately, there’s a ton of great resources that are free and on the web. The very best way to get started that I can think of is to read chapter one of The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009 edition). The pdf is available online.

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