d3.js 40 Under 40: Ones to watch - Josh James (1) Founder, CEO and Chairman, DomoAge: 38 James made the 40 Under 40 main list in 2009 for his web analytics firm, Omniture, which Adobe bought for $1.8 billion. His new venture, Domo, hasn't launched its product yet, but will provide software that puts business information services on notice. NEXT: Hosain Rahman EDITORIAL PACKAGE REPORTED BY KATIE BENNER, SCOTT CENDROWSKI, BETSY FELDMAN, MINA KIMES, ALEX KONRAD, BETH KOWITT, COLLEEN LEAHEY, MICHAL LEV-RAM, TARA MOORE, TORY NEWMYER, DANIEL ROBERTS, CHRISTOPHER TKACZYK, ANNE VANDERMEY @FortuneMagazine - Last updated November 30 2011: 10:19 AM ET
It's Sink or Swim in the IoT's Ocean of Bigger Data The network of connected devices commonly called the Internet of Things is poised to drive data growth to stratospheric levels. Our ability to wield analytics on these big, fast, and diverse data will determine whether we can successfully harness the IoT to improve livelihoods and boost bottom lines, or whether we’ll crumble under the weight of the new data. If you thought today’s data levels were big or fast or diverse, you haven’t seen anything yet. How big is the IoT? In the speech, Chambers predicted that in 10 years, the IoT industry would generate $19 trillion in revenue and savings. “2014 will be the transformative, pivotal point for the Internet of Everything,” Chambers said. To date, many of the IoT use cases have been focused on improving life in cities. “I think we’re beginning to see that this will impact every aspect of our lives,” Chambers said. The notion of a totally “dumb” endpoint will soon become antiquated, predicts IBM big data evangelist James Kobielus.
Graphical visualization of text similarities in essays in a book | munterbund.de Early stages in the process While developing the visualization algorithms, we plotted out a lot of different approaches that in the end we discarded for one reason or another. Here you can look at some of them. Examples of final results Here are some examples of the final visualization algorithm at work. The Question to Ask Before Hiring a Data Scientist - Michael Li by Michael Li | 10:00 AM August 6, 2014 When hiring data scientists, there’s nothing more frustrating than making the wrong hire. Data scientists are in notoriously high demand, hard to attract, and command large salaries — compounding the cost of a mistake. At The Data Incubator, we’ve talked to dozens of employers looking to hire data scientists from our training program, from large corporates like Pfizer and JPMorgan Chase to smaller tech startups like Foursquare and Upstart. Is your data scientist producing analytics for machines or humans? This distinction is important across organizations, industries, and job titles (our fellows are being placed at jobs with titles that range from Quant to Data Scientist to Analyst to Statistician). While this isn’t the only distinction among data scientists, it’s one of the biggest when it comes to hiring. Analytics for machines: In this case, the ultimate decision maker and consumer of the analysis is a computer.
Fidg't: Your Social Networking Address Book Explore your network with the Fidg't Visualizer* The Fidg't Visualizer allows you to play around with your network. You interface with the Visualizer through Flickr and LastFM tags, using any tag to create a Magnet. Once a Tag Magnet is created, members of the network will gravitate towards it if they have photos or music with that same Tag. This simple mechanic lets you visualize your Network in a unique way, demonstrating its Predisposition towards certain things. For good measure, you can also search through the network for certain users, and check out their recent photos and music. The Fidg't Visualizer is in an alpha release. Windows users might need to download Java, which can be done here . *You can download and play with the Fidg't Visualizer even if you haven't created a Fidg't account. 8.1.07 Thanks to Casey Reas for putting our Visualizer up on the Processing Home Page.
Magic Quadrant for Business Intelligence and Analytics Platforms Analyst(s): Rita L. Sallam, Joao Tapadinhas, Josh Parenteau, Daniel Yuen, Bill Hostmann The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. The BI platform market is forecast to have grown into a $14.1 billion market in 2013, largely through companies investing in IT-led consolidation projects to standardize on IT-centric BI platforms for large-scale systems-of-record reporting (see "Forecast: Enterprise Software Markets, Worldwide, 2010-2017, 3Q13 Update"). Also in support of wider adoption, companies and independent software vendors are increasingly embedding both traditional reporting, dashboards and interactive analysis, in addition to more advanced and prescriptive analytics built from statistical functions and algorithms available within the BI platform into business processes or applications. Analysis Actuate
GroupVisual.io Manifest Insights A Single Pane of Glass for your Data One of the biggest challenges for companies today is the inability to see what is really happening at any given moment. Manifest Insights is an exciting new Startup from Portland that develops powerful dashboards that visualize today’s on-premise and off-premise data in a flash. We caught up with CEO Dan Blaisdell to learn more. insideHPC: Let’s start in the beginning. Dan Blaisdell: We are a data consulting and visualization company. insideHPC: Help me out with what ‘dashboard’ means in this context. Dan Blaisdell: Yeah, it’s a high level snapshot. insideHPC: We’re here at OSCON with a lot of small companies like yours. Dan Blaisdell: I would say our key strength is how adaptable our solution is. We are going to be able to take the data from all different sources. insideHPC: That is fascinating. Dan Blaisdell: Yeah, absolutely. insideHPC: I was going to ask you about that. Dan Blaisdell: Absolutely.
Data Visualization: Modern Approaches - Smashing Magazine A Thumbnail History of Ensemble Methods By Mike Bowles Ensemble methods are the backbone of machine learning techniques. However, it can be a daunting subject for someone approaching it for the first time, so we asked Mike Bowles, machine learning expert and serial entrepreneur to provide some context. Ensemble Methods are among the most powerful and easiest to use of predictive analytics algorithms and R programming language has an outstanding collection that includes the best performers – Random Forest, Gradient Boosting and Bagging as well as big data versions that are available through Revolution Analytics. The phrase “Ensemble Methods” generally refers to building a large number of somewhat independent predictive models and then combining them by voting or averaging to yield very high performance. Bagging and Random Forests were developed to overcome variance and stability issues with binary decision trees. Tin Kam Ho of Bell Labs developed Random Decision Forests as an example of a random subspace method. References
McKinsey Web 2.0 Visualization For the past seven years, thousands of executives from around the world—across a range of industries and functional areas—have responded to a McKinsey survey on how organizations are using social (or Web 2.0) technologies. In 2009 we created an interactive tool that links the data from these survey results and charts it to the emerging trends in Web 2.0 adoption. This interactive focuses on several of the survey’s core questions—from what technologies and tools companies view as most important to what kind of investments, if any, organizations plan to make in Web 2.0 in the future. Our most recent survey examines the business use of 13 social technologies and tools: blogs, collaborative document editing, mash-ups (a Web application that combines multiple sources of data into a single tool), microblogging, online videoconferencing, podcasts, prediction markets, rating, RSS (Really Simple Syndication), social networking, tagging, video sharing, and wikis. Interactive
Roambi Protovis Protovis composes custom views of data with simple marks such as bars and dots. Unlike low-level graphics libraries that quickly become tedious for visualization, Protovis defines marks through dynamic properties that encode data, allowing inheritance, scales and layouts to simplify construction. Protovis is free and open-source, provided under the BSD License. It uses JavaScript and SVG for web-native visualizations; no plugin required (though you will need a modern web browser)! Although programming experience is helpful, Protovis is mostly declarative and designed to be learned by example. Protovis is no longer under active development.The final release of Protovis was v3.3.1 (4.7 MB). This project was led by Mike Bostock and Jeff Heer of the Stanford Visualization Group, with significant help from Vadim Ogievetsky. Updates June 28, 2011 - Protovis is no longer under active development. September 17, 2010 - Release 3.3 is available on GitHub. May 28, 2010 - ZOMG! Getting Started