HTML com UTF-8-pt-br (CGI-Bin)

Um servidor com o Microsoft Windows Server e o IIS (Internet Information Services) instalado, nele vem como padrão de instalação reconhecer o texto do HTML no idioma inglês. 

Recentemente, tive problemas de meu código HTML parar de reconhecer os acentos utilizados do idioma português-brasileiro.  Tentei de tudo, e nada fazia os acentos voltar a funcionar. Pesquisei em dois livros velhos de HTML da década de 80, e lá tinha um capitulo falando de CGI, então eu resolvi fazer um arquivo CGI para testar, e funcionou, a acentuação das palavras voltaram a funcionar perfeito novamente no HTML.

Caso algum de vocês passem pelo mesmo problema que eu, podem testar esta solução.

Esse arquivo paginacao.cgi tem que ser feito o upload para dentro do servidor web.

Criei um arquivo CGI-Bin chamado paginacao.cgi com o seguinte conteúdo abaixo, ele funcionou perfeito:

#!/bin/sh
Content-type: text/html
Language: pt-br
Charset: UTF-8-pt-br

========================================================================

Translation:

A server that has Microsoft Windows Server and Internet Information Services (IIS) installed, it is by default that the installation recognizes English-language HTML text.

I recently had problems with my HTML code failing to recognize the accents used in the Portuguese-Brazilian language. I tried everything, and nothing made the accents work again. I researched two old HTML books from the 80's, and there was a chapter talking about CGI, so I decided to make a CGI file to test, and it worked, the accentuation of the words came back perfect working again in HTML.

If any of you go through the same problem as me, you can test this solution.

This paginacao.cgi file has to be uploaded to the web server.

I created a CGI-Bin file called paginacao.cgi with the following content below, it worked perfect:

#! / Bin / sh

Content-type: text / html

Language: pt-br

Charset: UTF-8-pt-br

========================================================================

IIS configuration for UTF-8-pt-br (CGI-Bin) 

https://social.technet.microsoft.com/wiki/pt-br/contents/articles/37754.iis-configuration-for-utf-8-pt-br-cgi-bin.aspx

========================================================================

Por Ana Mercedes Gauna (18/04/2017)
Senior Business Systems Analyst
Twitter: @amgauna @AnaGaunaTech
WebSite: http://www.amgauna.eti.br
Blog: http://anagaunatech.blogspot.com
Blog: anagauna.wordpress.com

Data Science (Books)

Bibliography Recommendation Data Science

 Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President) em More Free Data Mining, Data Science Books and Resources

The list below based on the list compiled by Pedro Martins, but we added the book authors and year, sorted alphabetically by title, fixed spelling, and removed the links that did not work.

1. An Introduction to Data Science by Jeffrey Stanton, Robert De Graaf, 2013.

1. An introductory level resource developed by Syracuse University

1. An Introduction to Statistical Learning: with Applications in R by G. Casella, S, Fienberg, I Olkin, 2013.

1. Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.

1. A Programmer’s Guide to Data Mining by Ron Zacharski, 2012.

1. A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.

1. Bayesian Reasoning and Machine Learning by David Barber, 2012.

1. focusing on applying it to machine learning algorithms and processes. It is a hands-on resource, great to absorb all the knowledge in the book.

1. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean, 2014.

1. On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning.

1. Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014.

1. A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.

1. Data Mining and Business Analytics with R by Johannes Ledolter, 2013.

1. Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.

1. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J.A. Berry, Gordon S. Linoff, 2004.

1. A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.

1. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery by Graham Williams, 2011.

1. The objective of this book is to provide you lots of information on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.

1. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams, 2006.

1. This is a theoretical book approaching learning algorithms based on probabilistic Gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

• Data Science by Analyticbridge (internal to DSC, one of the first books about data science)

• Data Science 2.0 (internal to DSC, check the red-starred articles)

• 27 free Data Mining books

1. An Introduction to Data Science (Jeffrey Stanton, 2013)

• School of Data Handbook (2015)

• Data Jujitsu: The Art of Turning Data into Product (DJ Patil, 2012)

• Art of Data Science (Roger D. Peng & Elizabeth Matsui, 2015)
• 
  

Distributed Computing Tools

• Hadoop: The Definitive Guide (Tom White, 2011)

• Data-Intensive Text Processing with MapReduce (Jimmy Lin & Chris Dyer, 2010)
• 
  

Data Mining and Machine Learning

• Introduction to Machine Learning (Amnon Shashua, 2008)

• Machine Learning (Abdelhamid Mellouk & Abdennacer Chebira)

• Machine Learning — The Complete Guide (Wikipedia)

• Social Media Mining An Introduction (Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014)

• Data Mining: Practical Machine Learning Tools and Techniques (Ian H. Witten & Eibe Frank, 2005)

• Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014)

• A Programmer’s Guide to Data Mining (Ron Zacharski, 2015)

• Data Mining with Rattle and R (Graham Williams, 2011)

• Data Mining and Analysis: Fundamental Concepts and Algorithms(Mohammed J. Zaki & Wagner Meria Jr., 2014)

• Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Goo…(Matthew A. Russell, 2014)

• Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)

• Data Mining Techniques For Marketing, Sales, and Customer Relations…(Michael J.A. Berry & Gordon S. Linoff, 2004)

• Inductive Logic Programming: Techniques and Applications (Nada Lavrac & Saso Dzeroski, 1994)

• Pattern Recognition and Machine Learning (Christopher M. Bishop, 2006)

• Machine Learning, Neural and Statistical Classification (D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999)

• Information Theory, Inference, and Learning Algorithms (David J.C. MacKay, 2005)

• Data Mining and Business Analytics with R (Johannes Ledolter, 2013)

• Bayesian Reasoning and Machine Learning (David Barber, 2014)

• Gaussian Processes for Machine Learning (C. E. Rasmussen & C. K. I. Williams, 2006)

• Reinforcement Learning: An Introduction (Richard S. Sutton & Andrew G. Barto, 2012)

• Algorithms for Reinforcement Learning (Csaba Szepesvari, 2009)

• Big Data, Data Mining, and Machine Learning (Jared Dean, 2014)

• Modeling With Data (Ben Klemens, 2008)

• KB — Neural Data Mining with Python Sources (Roberto Bello, 2013)

• Deep Learning (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)

• Neural Networks and Deep Learning (Michael Nielsen, 2015)

• Data Mining Algorithms In R (Wikibooks, 2014)

• Data Mining and Analysis: Fundamental Concepts and Algorithms(Mohammed J. Zaki & Wagner Meira Jr., 2014)

• Theory and Applications for Advanced Text Mining (Shigeaki Sakurai, 2012)

• DSC Resources

• Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs

• Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC

• Buzz: Business News | Announcements | Events | RSS Feeds

• Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers

• Additional Reading

• 50 Articles about Hadoop and Related Topics

• 10 Modern Statistical Concepts Discovered by Data Scientists

• Top data science keywords on DSC

• 4 easy steps to becoming a data scientist

• 13 New Trends in Big Data and Data Science

• 22 tips for better data science

• Data Science Compared to 16 Analytic Disciplines

• How to detect spurious correlations, and how to find the real ones

• 17 short tutorials all data scientists should read (and practice)

• 10 types of data scientists

• 66 job interview questions for data scientists

• High versus low-level data science

• Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

Read the full post on KDnuggets: http://www.kdnuggets.com/2015/03/free-data-mining-data-science-books-resources.html

Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President)

=======================================

Recomendação de Bibliografia Data Science = Kirk Borne‏@KirkDBorne
Download 50+ Free #DataScienceBooks:http://bit.ly/1Or1j5Z #abdsc#BigData #Analytics

Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors — usually academics — are more prone to making their books available for free). Many are O’Reilly books freely available. Here we display those most relevant to data science. I haven’t checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below. Note that at DSC, we also have our free books:

Big Data Nomenclature for Managers

Explained list of big data concepts for managers

https://medium.com/cyber-tales/big-data-nomenclature-for-managers-aa20b26b119d

Windows Server: Microsoft Visual C++ (64 bits)

Para o Microsoft Windows Server versões 2008 / 2012 / 2016 poderem funcionar melhor, deve-se fazer o seguinte:

Observação:  Comprei meses atrás na Microsoft Press, diversos livros do Microsoft Windows Server 2012 R2 (2 livros) e do Microsoft SQL Server 2012 (3 livros), os quais eu ainda não terminei de ler.  

Sendo que no livro do exame do Microsoft Windows Server 2012 (Exam Ref 70-410 - Installing and Configuring Windows Server 2012 R2 - Author Craig Zacker), o autor do livro recomenda não existir drivers de 32 bits (x86) instalado dentro de um sistema operacional de 64 bits.  Nesse livro ele diz que esses drivers de 32 bits (x86) tem que ser bem eliminados (desinstalados) no sistema operacional de 64 bits (x64). E nesse livro, ele também informa que o sistema operacional Microsoft Windows Server desde a versão 2008 é versão 64 bits (x64).  

Processador 32 bits x 64 bits

• The x86 Processor (32 bits) (Developer Technologies)

• The x64 Processor (64 bits) (Developer Technologies)

• Interprocess Communication Between 32-bit and 64-bit Applications

• Processor Affinity Under WOW64

• Designing 64-bit-Compatible Interfaces

• x86 Architecture (processador de 32 bits) (Library MSDN)

• x64 Architecture (processador de 64 bits) (Library MSDN)

Eu notei que existe drivers de 32 bits (x86) instalados dentro de diversos computadores com o sistema operacional de 64 bits (x64), como por exemplo: Microsoft Windows Server 2008 / 2012 / 2016, Microsoft Windows 7, Microsoft Windows 8 e Microsoft Windows 10.

Caso exista instalado no servidor o sistema operacional Windows 64 bits instalado (ou exista instalado em computador de usuário comum) algum aplicativo com drivers versão 32 bits (x86) do Oracle Java ou do Microsoft Visual C++ 2005 / 2008 / 2010 / 2013 / 2015, etc, instalados, o técnico precisa fazer o download da versão de 64 bits deles, depois disso feito instalar um a um esses drivers 64 bits (x64), porque somente assim a desinstalação dos drivers de 32 bits (x86) conseguirá fazer ser concluída com sucesso, e por último reinicia o computador/servidor.  

Somente estando com os drivers de versão 64 bits instalados, é que o sistema operacional de 64 bits (x64) irá funcionar melhor.  Idem quanto aos computadores dos usuários comuns.  No site da Oracle existe o driver do JAVA versão 64 bits disponível para download.

Segue abaixo os links para download e instalação dos drivers do Oracle Java e do Microsoft Visual C++

• Java SE Runtime Environment 7 Downloads = http://www.oracle.com/technetwork/pt/java/javase/downloads/jre7-downloads-1880261.html

• Microsoft Visual C++ 2005 SP1 Redistributable Package (x64) = https://www.microsoft.com/en-us/download/details.aspx?id=18471

• Microsoft Visual C++ 2008 Redistributable Package (x64) = https://www.microsoft.com/en-us/download/details.aspx?id=15336

• Microsoft Visual C++ 2010 Redistributable Package (x64) = https://www.microsoft.com/en-us/download/details.aspx?id=14632

• Microsoft Visual C++ 2010 SP1 Redistributable Package (x64) = https://www.microsoft.com/en-us/download/details.aspx?id=13523

• Visual C++ Redistributable for Visual Studio 2012 Update 4 = https://www.microsoft.com/en-au/download/details.aspx?id=30679

• Visual C++ Redistributable Packages for Visual Studio 2013 = https://www.microsoft.com/en-us/download/details.aspx?id=40784

• Pacotes Redistribuíveis do Visual C++ para Visual Studio 2015 = https://www.microsoft.com/pt-br/download/details.aspx?id=48145

Por Ana Mercedes Gauna (27/05/2016)