Data Visualization

I often face the challenge of Data Visualization and the right tools ….

 

Click here to see some solutions : http://selection.datavisualization.ch/

 

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Download your Free Book: Networks, Crowds, and Markets: Reasoning About a Highly Connected World

Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

 

access here : http://www.cs.cornell.edu/home/kleinber/networks-book/

 

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Network Science Book Project : Free Download

Network Science Book Project aims to produce an interactive textbook for network science. It is a work in progress.

It is freely available under the Creative Commons licence for iPad and in pdf, together with the slides to teach the material.

http://barabasilab.neu.edu/networksciencebook/

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Free Machine Learning Papers : Journal of Machine Learning Research

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

Access here:

http://jmlr.org/

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Download the Classic Book: The Elements of Statistical Learning

During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting–the first comprehensive treatment of this topic in any book.

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This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization and spectral clustering. There is also a chapter on methods for “wide” data (italics p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful {italics An Introduct ion to the Bootstrap}. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Download here:   http://statweb.stanford.edu/~tibs/ElemStatLearn/

Free Big Data Book : An Introduction to Statistical Learning

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Download here : http://www-bcf.usc.edu/~gareth/ISL/index.html

 

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Big Data Mini Course …online…

The UC Berkeley AmpLab has posted an online Big Data mini-course,

http://ampcamp.berkeley.edu/big-data-mini-course-home/

Big Data Gets Bigger…

 

Came across an interesting article.  Big data is on a roll now…

 

Data is being generated about the activities of people and inanimate objects on a massive and increasing scale. We examine how much data is involved, how much might be useful, what tools and techniques are available to analyse it, and whether businesses are actually getting to grips with big data.

 

Computing’s ‘Big Bang’ moment came during World War 2, in the shape of the world’s first programmable digital computer, Colossus. Built at the UK’s Bletchley Park codebreaking centre to help break the German High Command’s Lorenz cipher, Colossus could store 20,000 5-bit characters (~125KB) and input data at 5,000 characters per second via paper tape (~25Kbps). Small data in today’s terms perhaps, but Colossus decrypts made a vital contribution to the Allied planning for D-Day, in particular.

The Digital Universe

In December 2012, IDC and EMC estimated the size of the digital universe (that is, all the digital data created, replicated and consumed in that year) to be 2,837 exabytes (EB) and forecast this to grow to 40,000EB by 2020 — a doubling time of roughly two years. One exabyte equals a thousand petabytes (PB), or a million terabytes (TB), or a billion gigabytes (GB). So by 2020, according to IDC and EMC, the digital universe will amount to over 5,200GB per person on the planet.

http://www.zdnet.com/big-data-an-overview-7000020785/

 

 

 

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BIG Data–As Big As It Gets!

The Real World of Big DataThe Real World of Big Data via Wikibon Infographics