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(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…
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(T) NumPy in Detail

In my post previous there was some examples contained matrices or other data structures of higher dimensionality—just one-dimensional vectors. To understand how NumPy treats objects with dimensions greater than one, we need to develop at least a superficial understanding for the way NumPy is implemented. It is misleading to think of NumPy as a “matrix package for Python” (although it’s commonly used as such). I find it more helpful to think of NumPy as a wrapper and access layer for underlying C buffers. These buffers are contiguous blocks of C memory, which—by their nature—are one-dimensional data structures. All elements in those data structures must be of the same size, and we can specify almost any native C type (including C structs) as the type of the individual elements. The default type corresponds to a C double and that is what we use in the examples that follow, but keep in mind that other choices are possible. All operations that apply to the data overall are performed in C…

(T) NumPy in Action

The NumPy module provides effecient and convenient handling of large numerical arrays in Python. This module is used by many other libraries and projects and in this sense is a "base" technology. Let's look at some quick examples.
NumPy objects are of type ndarray. There are different ways of creating then. We can create an ndarray by:
Converting a Python listUsing a library function that returns a populated vectorReading data from a file directly into a NumPy object The listing that follows shows five different ways to create NumPy objects. First we create one by converting a Python list. Then we show two different factory routines that generate equally spaced grid points. These routines differ in how they interpret the provided boundary values: one routine includes both boundary values, and the other includes one and excludes the other. Next we create a vector filled with zeros and set each element in a loop. Finally, we read data from a text file. (I am showing only t…

(T) Entendendo o que é Big Data

O que é Big Data?

O termo "Big Data" tem sido usado para descrever conjuntos de dados que são tão grandes que os meios típicos e tradicionais de armazenamento de dados, gestão, pesquisa, análise e outro processamento tornou-se um desafio. Big Data é caracterizado pela magnitude da informação digital que pode vir de muitas fontes e formatos de dados (estruturados e não-estruturados), e os dados que podem ser processados ​​e analisados ​​para encontrar ideias e padrões usados ​​para tomar decisões com base nas informações.

Ou seja, podemos concluir que Big Data é definido como o conjunto de soluções tecnológicas capazes de lidar com dados digitais em volume, variedade e velocidade sem precedentes. Na prática, esta tecnologia permite analisar qualquer tipo de informação digital em tempo real, sendo fundamental para a tomada de decisões.

Definições mais técnicas e aprofundadas acerca do que é Big Data, assim como uma metodologia para gerenciar tais informações, podem ser obtidos…

(O) Criando um gráfico tipo "Stephen Few" no QlikView

This chart as a replacement for a traditional gauge. A traditional gauge takes up a large amount of space to encode, usually, only one value. A bullet chart is linear and can encode more than one value. The main components of a bullet chart (from Stephen Few's perceptualedge.com)
are as follows: A text labelA quantitative scale along a single linear axisThe featured measureOne or two comparative measures (optional)From two to five ranges along the quantitative scale to declare the featured measure's qualitative state (optional) There is no native bullet chart in QlikView. However, we can create one by combining a couple of objects. Items 1, 2, 4, and 5 can be achieved with a linear gauge chart. The bar, item 3, can then be overlaid using a separate and transparent bar chart.


 Load the following script:
    Country, Sales, Target
    USA, 1000, 1100
    UK, 800, 1000
    Germany, 800, 700
    Japan, 1000, 1000
 Add a new gauge chart. You should add a t…