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terça-feira, 18 de abril de 2017

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:

Content-type: text/html
Language: pt-br
Charset: UTF-8-pt-br



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)


Por Ana Mercedes Gauna (18/04/2017)
Senior Business Systems Analyst
Twitter: @amgauna @AnaGaunaTech

domingo, 2 de abril de 2017

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.
  1. An Introduction to Data Science (Jeffrey Stanton, 2013)
Distributed Computing Tools
Data Mining and Machine Learning
  • Deep Learning (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)
  • DSC Resources
  • Additional Reading

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