Sunday, July 29. 2012
Link roundup, week 30/2012
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David MacKay: Information Theory, Pattern Recognition and Neural Networks: The Book
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Information Theory, Inference, and Learning Algorithms
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640 pages, Published September 2003
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PDF (A4) pdf (9M) (fourth printing, March 2005)
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Cookbook for R » Cookbook for R
HTML
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formerly named R Cookbook
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It is not related to Paul Teetor’s excellent R Cookbook
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Big Data -- Why the 3Vs Just Don’t Make Sense -- TDWI -The Data Warehousing Institute
When are we done defining big data? p.1
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settled on 3 Vs -- volume, variety, and velocity
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another V: value
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if big data is understood solely on the basis of these trends, it isn’t clear that it’s at all hype-worthy
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if “big data” simply describes the volume, variety, and velocity of the information that constitutes it, our existing data management practices are still arguably up to the task
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big data is hyped on the basis of its real or imagined outputs
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a lot more interesting when you bring in ‘V’ for value
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No, Really, Some of My Best Friends Are Data Scientists
When are we done defining data science?
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the skills of a “data scientist” are those of a modern statistician
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know how to move data around and manipulate data with some programming language
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know how to draw informative pictures of data
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Knowledge of stats, errorbars, confidence intervals
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try to get people from different backgrounds
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Great communication skills
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a lot of what we teach The Kids now looks a lot more like machine learning than statistics as it was taught circa 1970, or even circa 1980
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Everything I know about statistics I’ve learned without formal instruction
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is not, in my experience, intrinsically hard for anyone who already has a decent grounding in some other mathematical science
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mastering them really does mean trying to do things and failing
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potentially hazardous. This is the idea that all that really matters is being “smart”
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counter-productive for students to attribute their success or failure in learning about something to an innate talent
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Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley & SAS Business): Bill Franks
Bill Franks is Chief Analytics Officer at Teradata
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27. April 2012
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336 Seiten
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The Big Question In Big Data Is...What’s The Question? | SmartData Collective
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Some folks like to confuse Hadoop with big data
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Focus On the Questions To Ask, Not The Answers
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The failure of data warehouses to provide real-time data led to the creation of data marts
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Data marts failed to provide complete and updated and comprehensive views
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existing solutions still don’t solve the problem. Why? The market and business environment have changed
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Data moves from structured to unstructured. Sources exponentially proliferate. Data quality is paramount.
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Real-time is irrelevant because speed does not trump fidelity. Quantity does not trump quality
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Business questions remained unanswered despite the massive number of reports and views and charts
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The big shift is about moving from data to decisions
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Information Theory, Pattern Recognition, and Neural Networks
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Draft videos (editing incomplete)
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Entropy and Data Compression
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Shannon’s Source Coding Theorem
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Inference and Information Measures for Noisy Channels
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Introduction to Bayesian Inference
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Approximating Probability Distributions
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Clustering
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Monte Carlo Methods
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Gibbs sampling
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Neural Networks
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