CVthèque - PMO MarketPlace What Does Big Data Have to Do With Me and My Organization? - The TechSoup Blog - Community - TechSoup Without a doubt big data is one of the most talked about topics in nonprofit technology and IT in general this year. It was last year as well. But if you’re not a big collector of online information like Google, Facebook or Microsoft, what does big data have to do with you and your work? It’s a big fuzzy topic, but I’ll give it a shot. What Is Big Data Anyway? Like me you may be a bit unclear about what big data actually is. I find NPR commentator and linguist, Geoff Nunberg’s description of it more useful. As we have more and more devices collecting data about what we do, vast amount of information is piling up about us. Keisha Taylor’s Examples of Charity Big Data Keisha Taylor is TechSoup Global's senior manager for business planning and research for our Global Data Services work. She also likes the big data for good work coming out of the UN Global Pulse initiative. Keisha also recommends having a look at the work of DataKind, especially their online project gallery. Image: IBM
Join top Silicon Valley investors, hear their fund-raising secrets | Bootstrap See that giant building above? That's the headquarters of CBS Interactive, home to CNET, in San Francisco's SoMa neighborhood. And if you've ever wondered what's inside--keep reading out-of-towners, this is for you too--now's your chance to find out. On Thursday, March 29 at 6 p.m. In addition to the staples--wine, beer, and light grub--we'll have three longtime Valley players--Dave McClure of 500 Startups, Naval Ravikant of AngelList, and George Zachary of Charles River partners. If you're in San Francisco, you can sign up here: Startup Funding: Strategies and Opportunities. Take McClure.
Freaky Data Science I've received quite a few comments and emails about my most recent blog, "What is Data Science – Again?" Much of the reaction has been positive, agreeing with the take that the discipline revolves on the combination of computation and statistical science. Some is negative, decrying “data science” as nothing more than a trumped up neologism for “programmer”. The perspective that most gets me thinking, though, is the one that seeks to define data science not only by the tasks of practitioners, as I've done, but also by its higher purpose – something akin to "the scientific study of the creation, validation and transformation of data to create meaning". I'm certainly in agreement with this need to view data science through the lenses of both “what” and “how”. Alas bridging the what-how gap is not so straightforward. Last Fall I had the opportunity to interview two students completing their doctoral dissertations in biological fields for positions with Inquidia.
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