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Atendendo pedidos...

São muitas as críticas e sugestões que eu recebo com relação as mensagens que são postadas aqui neste blog. Isto é muito bom e eu gostaria de agradecer a todos por estes feedbacks!

Por esta razão, estarei classificando neste blog as mensagens que são postadas, da seguinte forma: (E), (T) e (O). Estas siglas estarão precedendo o título dos artigos. Por exemplo, '(E) Título do Artigo XYZ'.

O que significam estas siglas na prática?

(E) – são artigos que falam de gerenciamento e arquitetura da informação em um nível mais de definição, processos, desafios etc.

(T) – são dicas técnicas das ferramentas do meu know-how e expertise: OBIEE, Hyperion Essbase, Hyperion Planning e Informatica PowerCenter.

(O) – são artigos do tipo How-To, que explicam passo-a-passo a execução de uma determinada tarefa utilizando alguma ferramenta.

Desta forma, acredito realmente estar cumprindo com o meu objetivo neste blog que é trazer informação, mas principalmente, compartilhar conhecimento do que acontece na prática no mundo BI. E, que acima de tudo, este blog possa se tornar uma referência para entusiastas ou profissionais interessados neste assunto.

Um grande abraço a todos,

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Postagens mais visitadas deste blog

(A) Data Science in Practice with Python

The top trending in Twitter or other social network is the term “data science”. But ...
What’s the data science? How do real companies use data science to make products, services and operations better? How does it work? What does the data science lifecycle look like?  This is the buzzword at the moment. A lot of people ask me about it. Are many questions. I’ll try answer all of these questions through of some samples.

Sample 1 - Regression

WHAT IS A REGRESSION? This is the better definition what I found [Source: Wikipedia] - Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.
HOW DOES IT WORK? Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variable…

(A) Tucson Best Buy Analysis

“Data! Data! Data!” he cried impatiently.  “I can’t make bricks without clay.” —Arthur Conan Doyle
The Ascendance of Data

We live in a world that’s drowning in data. Websites track every user’s every click. Your smartphone is building up a record of your location and speed every second of every day. “Quantified selfers” wear pedometers-on-steroids that are ever recording their heart rates, movement habits, diet, and sleep patterns. Smart cars collect driving habits, smart homes collect living habits, and smart marketers collect purchasing habits. The Internet itself represents a huge graph of knowledge that contains (among other things) an enormous cross-referenced encyclopedia; domain-specific databases about movies, music, sports results, pinball machines, memes, and cocktails; and too many government statistics (some of them nearly true!) from too many governments to wrap your head around.
Buried in these data are answers to countless questions that no one’s ever thought to ask. In…

(A) Data Science in Practice with Python - Sample 2

In this post I'll explain what is a recommender system, how work it and show you some code examples. In my previous post I did a quick introduction:

Sample 2 - Recommender System

WHAT IS A RECOMMENDER SYSTEM? A model that filters information to present users with a curated subset of options they’re likely to find appealing.
HOW DOES IT WORK? Generally via a collaborative approach (considering user’s previous behavior) or content based approach (based on discrete assigned characteristics).

Now I'll get into in some concepts very important about recommender systems.

Recommender System in Details:

We can say that the goal of a recommender system is to make product or service recommendations to people. Of course, these recommendations should be for products or services they’re more likely to want buy or consume.

Recommender systems are active information filtering systems which personalize the information coming to a user based on his interests, relevance of the information etc.…