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John Callery
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John Callery

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Can natural language processing be used to predict US Supreme Court outcomes? +Chris Nasrallah has done some interesting work on this:
Trying to predict the decisions of the Supreme Court is probably as old as the Court itself. And with many high profile cases being argued lately, the Court has been in the news a lot (see here, he...
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John Callery

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Podcast for the day: +Ford Motor Company's "Data Science Leader", +Michael Cavaretta, talks connected cars, dealer recommendation systems and hadoop (interview starts 18 minutes in). +GigaOM 
Summary: On this week’s Structure Show we take on (what else?) the new Microsoft CEO, Tableau’s rip-roaring quarter and how Ford uses data to build better cars and sell more of them.
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John Callery

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Really great work on extracting value from old and challenging data sets, plus finding ways to create/combine new data to come up with some interesting conclusions.
It turns out, we may be more social than we were 30 years ago — at least in public spaces.
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Catchy headline, but it sounds like more of a case of "maturing" and "focusing" than "shrinking" to me...

"In the case of Big Data, this probably means less focus on back-end technologies like new types of storage or database frameworks, and a rethinking about how best to integrate human knowledge, algorithms and diverse sets of data."
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+The Economist is having an interesting debate on cities' use of data and what value it can provide.
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Using social network analysis and language processing to predict gender, age and personality. The Time article focuses on gender stereotypes, but the full PLOS ONE article expands on a number of interesting methods and findings.
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Gender will soon be no longer relevant. A user will be identified by what they buy and what they sell. Sadly.
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Great advice from a guy who knows what he's talking about when it comes to visualization.
 
My answer to What should everyone know about making good charts and graphs to represent data? http://qr.ae/IqOrc
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John Callery

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Analytics can help us understand our employees as well as we understand our own customers. This article suggests that asking "What should we predict?" rather than simply "What can we predict?" is often just as important. I'd suggest that communicating well and building trust with employees and leaders can help make the gap between "can" and "should" quite small...
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This is a good look at an areas that can use a lot more focus on analytics, though I'm not sure why we're considered 'scary'... :-)
Going beyond the expected is essential to the practice of analytics and is the fundamental difference between true analytics and reporting. And to achieve this end in HR, you need to hire a scary person. Known as data scientists or quants, these whizzes can be scary to more-traditional CHROs and HR leadership teams, because they don’t think or speak in traditional HR terms. Nevertheless, their innate curiosity, business knowledge, and supreme ...
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Great projects like this help support the need for open data initiatives.
Summary: The Refugee Project is an example of what cutting edge data designers can do with the massive amount of data sets that were buried within the U.N.’s refugee group.
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John Callery

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Interesting look at 'closed' data sets and the implications for privacy, accuracy and peer review inside the walls of 'corporate science'. +WIRED 
Facebook is a company that knows the personal habits of more than one billion people across the globe. That can be a scary thought — especially when you consider that Facebook is using this data to target ads and may share it with people and businesses you’d rather Zuckerberg and Co. didn’t share it with. But at the same time, this enormous trove of online data can give us a new means of seeing and understanding the world we live in.
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It would be interesting to compare this with LinkedIn information.
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Nice viz of TLDs
 
Geography of Top-Level Domain names.
This graphic maps a combination of generic top-level domains (gTLDs) and country code top-level domains (ccTLDs) in order to provide an indication of the total number of domain registrations in every country worldwide.

All gTLDs are mapped through an analysis of information returned by the WHOIS Internet protocol, that provides contact information for any given domain. For instance, this meant that for every .com domain name, the location registered in that domain’s WHOIS data was retrieved and stored in a database.

#domainnames   #map   #infographic  
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Have him in circles
127 people
satish bapatla's profile photo
Kathryn Kerner's profile photo
Frankie Rodriguez Jr.'s profile photo
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Portia Brown's profile photo
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Matt Davenport's profile photo
Melissa Cohen's profile photo
Xaymara Perez's profile photo
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