New tools for big data visualization are popping up all the time, but rarely do they attract such wide spread attention as the Dialect Survey Maps application which became the most-viewed story last year at The New York Times.
Microsoft Research, the research arm of the software giant, is a hotbed of data science and machine learning research. Microsoft has the resources to hire the best and brightest researchers from around the globe. A recent publication is available for download (PDF): “Deep Learning: Methods and Applications” by Li Deng and Dong Yu, two prominent researchers in the field.
As an attempt to remain relevant in an increasingly data-driven world, many traditional news publications are embracing the sweeping changes in their industry by employing a broad swath of new technologies. Here is a good case in point: The Los Angeles Times Data Desk, offering content such as maps, databases, analysis, and visualizations.
Big Data will change the way your organization responds to business opportunities. But to reap its full benefits, you have to move from proof of concept into full production. Here is an informative, 52-minute presentation that provides the guidelines for successfully integrating Hadoop into your standard data center processes.
Big Data & Brews reminds me of the good old pre-dotcom-bubble days when I used to attend many networking events over at the Westwood Brewery near UCLA. Those were heady days. It was beer and tech back then as it seems to be today. Now, instead of meeting up in person, you can enjoy contemporary discussions of the big data industry by watching the host, Datameer CEO Stephan Groschupf, and his guest in an informal pub atmosphere.
“Tackling big data without a cloud-centric worldview is sort of like building a skyscraper without doing a soil study first: you might make some initial progress, but sooner or later you’ll discover that you need to understand and thoroughly adapt an (inadequate) foundation. At a minimum, you’ll experience false starts and thrashing; in many cases, you may never place a capstone.”
Jeremy Howard made a presentation to the Melbourne R meetup group, where he gave a brief overview of his “data scientist’s toolbox” (using a few Kaggle competitions as practical examples), and also provided an introduction to ensembles of decision trees (including the well-known Random Forest™ algorithm).