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hansdezwart

YouTube - Authors@Google: Ian Ayres - 0 views

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    Ian Ayres visits Google's Mountain View, CA headquarters to discuss his book, "Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart." This event took place on November 8, 2007 as part of the Authors@Google series.
hansdezwart

YouTube - Stephen Wolfram: Computing a theory of everything - 0 views

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    Stephen Wolfram, creator of Mathematica, talks about his quest to make all knowledge computational -- able to be searched, processed and manipulated. His new search engine, Wolfram Alpha, has no lesser goal than to model and explain the physics underlying the universe.
hansdezwart

Scraping for Journalism: A Guide for Collecting Data - ProPublica - 0 views

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    We've written a series of how-to guides explaining how we collected the data. Most of the techniques are within the ability of the moderately experienced programmer.
hansdezwart

Cloudera Hadoop Training: Thinking at Scale on Vimeo - 1 views

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    You know your data is big - you found Hadoop. What implications must you consider when working at this scale? This lecture addresses common challenges and general best practices for scaling with your data.
hansdezwart

YouTube - Why Data Matters: IBM Leads Data Analysis in the Decade of Smart - 0 views

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    Data is present in all the systems and processes in the world. IBM helps analyze this multitude of information to make intelligent decisions, while enabling business efficiency and adding value to many industries.
hansdezwart

HP Labs' Central Nervous System for the Earth project aims to build a planetwide sensin... - 0 views

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    In the first commercial application of CeNSE technology, HP and Shell will build a wireless sensing system to acquire high-resolution seismic data. By vastly improving the quality of seismic imaging, the new system will allow Shell to more easily and cost-effectively explore difficult oil and gas reservoirs.
hansdezwart

dataists » Blog Archive » A Taxonomy of Data Science - 0 views

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    We thought it would be useful to propose one possible taxonomy - we call it the Snice* taxonomy - of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret (or, if you like, OSEMN, which rhymes with possum).
hansdezwart

DataShop > Home - 0 views

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    A data analysis service for the learning science community
hansdezwart

Conversations for a Smarter Planet - 0 views

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    With so much technology and networking available at such low cost, what wouldn't you enhance? What wouldn't you connect? What information wouldn't you mine for insight? What service wouldn't you provide a customer, a citizen, a student or a patient?
hansdezwart

The Grim Threat to British Universities by Simon Head | The New York Review of Books - 0 views

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    The British universities, Oxford and Cambridge included, are under siege from a system of state control that is undermining the one thing upon which their worldwide reputation depends: the caliber of their scholarship. The theories and practices that are driving this assault are mostly American in origin, conceived in American business schools and management consulting firms. They are frequently embedded in intensive management systems that make use of information technology (IT) marketed by corporations such as IBM, Oracle, and SAP. They are then sold to clients such as the UK government and its bureaucracies, including the universities. This alliance between the public and private sector has become a threat to academic freedom in the UK, and a warning to the American academy about how its own freedoms can be threatened.
hansdezwart

7 Data Blogs To Explore - ReadWriteCloud - 1 views

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    Several weeks ago, I posted a question on Quora asking for the best data blogs. There have been 26 replies with dozens of blogs recommended.
Vanessa Vaile

LAK11: Big Data Small Data « Viplav Baxi's Meanderings - 0 views

  • which data is more appropriate - BIG or small
  • most discussion about big data centres on quantity
  • other elements you mention – implication, new models, new decision making approaches – all flow from this abundance of data.
  • ...15 more annotations...
  • Increased data quantity requires new approaches
  • Is small beautiful? Look at the following links. Big Data, Small Data New Age of Innovation (Prahalad) So you like Big Data
  • reading on Insurers and the work done by Levitt and Dubner on Freakonomics tells us clearly that data not earlier thought relevant or causal can be an efficient predictor.
  • Secondly, strategies designed on BIG data
  • may overpower small data strategies
  • Thirdly, BIG data also has BIG impacting factors.
  • Fourthly, actions taken on BIG data will have big consequences,
  • Lastly, if everybody, big or small, started using BIG analytics, to make decisions
  • companies would anyway lose the competitive differentiator that analytics brings to them.
  • Corresponding to the question, how big does BIG need to be, the question I have is - how small really is small.
  • defining patterns that emerge from very small pieces of data (e.g. synchronicity)
  • how tools for SNA and analysis of BIG data can apply to Learning and Knowledge Analytics
  • at the other end it embraces how small changes can cause long term variations
  • not easy to analyze the small data
  • data that is small enough not to be generalizable
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