50 Great Examples of Data Visualization

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Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information.

And trying to find related content can also be difficult, depending on what data you’re looking for.

But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner.

Below are 50 of the best data visualizations and tools for creating your own visualizations out there, covering everything from Digg activity to network connectivity to what’s currently happening on Twitter.

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ontologies and data models

Já se perguntaram qual a diferença entre ontologias e modelos de dados?

Já se perguntaram qual a diferença entre ontologias e modelos de dados?

Ontologies versus Data Models

By Malcolm Chisholm
AUG 12, 2014 5:00am ET

Data models have been with us since Ted Codd described normalization in 1970 and Peter Chen published his paper on entity relationship diagrams in 1976. Ontology as a discipline in philosophy can trace its roots to ancient Greece. As applied to data management, it is much more recent than data modeling and has only appeared in the past few years. But just what is the difference between ontologies and data models? If they are both about data, do they not boil down to the same thing?

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portal smart datacollective.com

Um portal de notícias sobre ciencia dos dados, big data, analytics

Um portal de notícias sobre ciencia dos dados, big data, analytics

SmartData Collective, an online community moderated by Social Media Today, provides enterprise leaders access to the latest trends in Business Intelligence and Data Management. Our innovative model serves as a platform for recognized, global experts to share their insights through peer contributions, custom content publishing and alignment with industry leaders. SmartData Collective is a key resource for executives who need to make informed data management decisions.

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Data Intelligence and Analytics Resources

Excelentes textos sobre ciencia dos dados e big data

Excelentes textos sobre ciencia dos dados e big data

3. Big Data

4. Visualization

5. Best and Worst of Data Science

6. New Analytics Start-up Ideas

7. Rants about Healthcare, Education, etc.

8. Career Stuff, Training, Salary Surveys

9. Miscellaneous

10. DSC Webinar Series – with video access

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17 short tutorials all data scientists should read

Excelentes textos fundamentais para cientistas dos dados

Excelentes textos fundamentais para cientistas dos dados

Here’s the list:

Related linkThe Data Science Toolkit

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Century of rock history

Para quem gosta de música esta visualização é extraordinária

Para quem gosta de música esta visualização é extraordinária

Jessica Edmondson visualized the history of rock music, from foundations in the pre-1900s to a boom in the 1960s and finally to what we have now. Nodes represent music styles, and edges represent musical connections. There are a lot of them and as a whole it’s a screen of spaghetti, but it’s animated, which is key. It starts at the beginning and develops over time, so you know where to go and what to look at. Music samples for each genre is also a nice touch. [Thanks, Jessica]

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Brainstorm

Bom texto sobre uma técnica de captura de conhecimento tácito

Bom texto sobre uma técnica de captura de conhecimento tácito

Brainstorm, ou ainda Brainstorming, significa literalmente “tempestade de ideias”. No Brasil, por vezes é jocosamente denominado “toró de parpites”. É uma técnica criativa para obter ideias e soluções. De tão simples que é, muitas vezes é aplicada de forma inadequada, simplesmente como se fosse um bate-papo. Iremos ver aqui no Blogtek algumas técnicas para a busca de soluções de problemas.

Brainstorm – definição e aplicações

Brainstorm – princípios

Brainstorm – regras

Brainstorm – etapas


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The Field Guide to Data Science

Bom e-book sobre data science

Bom e-book sobre data science

Data Science is the competitive advantage of the future for organizations interested in turning their data into a product through analytics. Industries from health, to national security, to finance, to energy can be improved by creating better data analytics through Data Science. The winners and the losers in the emerging data economy are going to be determined by their Data Science teams.

Booz Allen Hamilton created The Field Guide to Data Science to help organizations of all types and missions understand how to make use of data as a resource. The text spells out what Data Science is and why it matters to organizations as well as how to create Data Science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of Data Science. Practitioners will add to their toolboxes.

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Machine Learning MOOC

Um curso muito completo de machine learning

Um curso muito completo de machine learning

About the Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

FAQ

  • What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
  • How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
  • Do I need to buy a textbook for the course?No, it is self-contained.
  • Will I get a statement of accomplishment after completing this class?Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

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LIBSVM — A Library for Support Vector Machines

Página dos autores da biblioteca LIBSVM, a mais usada para SVM

Página dos autores da biblioteca LIBSVM, a mais usada para SVM

LIBSVM — A Library for Support Vector Machines

Chih-Chung Chang and Chih-Jen Lin


Version 3.17 released on April Fools’ day, 2013. We slightly adjust the way class labels are handled internally. By default labels are ordered by their first occurrence in the training set. Hence for a set with -1/+1 labels, if -1 appears first, then internally -1 becomes +1. This has caused confusion. Now for data with -1/+1 labels, we specifically ensure that internally the binary SVM has positive data corresponding to the +1 instances. For developers, see changes in the subrouting svm_group_classes of svm.cpp.
We now have a nice page LIBSVM data sets providing problems in LIBSVM format.
A practical guide to SVM classification is available now! (mainly written for beginners)
LIBSVM tools available now!
We now have an easy script (easy.py) for users who know NOTHING about svm. It makes everything automatic–from data scaling to parameter selection.
The parameter selection tool grid.py generates the following contour of cross-validation accuracy. To use this tool, you also need to install python and gnuplot.

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