BUSINESS INTELLIGENCE

Python Data Science Tutorials
“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components:

Data exploration & analysis.

Included here: Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library.
Data visualization. A pretty self-explanatory name. Taking data and turning it into something colorful.

Included here: Matplotlib; Seaborn; Datashader; others.
Classical machine learning. Conceptually, we could define this as any supervised or unsupervised learning task that is not deep learning (see below). Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features.

Included here: Scikit-Learn, StatsModels.
Deep learning. This is a subset of machine learning that is seeing a renaissance, and is commonly implemented with Keras, among other libraries. It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.

Included here: Keras, TensorFlow, and a whole host of others.
Data storage and big data frameworks. Big data is best defined as data that is either literally too large to reside on a single machine, or can’t be processed in the absence of a distributed environment. The Python bindings to Apache technologies play heavily here.

Apache Spark; Apache Hadoop; HDFS; Dask; h5py/pytables.
Odds and ends. Includes subtopics such as natural language processing, and image manipulation with libraries such as OpenCV.

Included here: nltk; Spacy; OpenCV/cv2; scikit-image; Cython.

DATA SCIENCE

In this module, general, professional, and interesting aspects related to data science will be discussed.

It is impossible not to start this part without defining what is data science or what is a data scientist.

On a personal level, I consider that the data scientist is the person with the most professional skills related to data who is responsible for generating value with direct information, taking into account 3 fundamental aspects. The first the generation and application of technical knowledge, the second the knowledge of business and questions that generate expected value, and the most useful but not least the application of mathematical knowledge.

This section will show how I will become a data scientist and what was the process that I had to do to achieve it.

1. Talk more about statistical math.

Two sets of statistical Tools.

DATA ENGINEER

En ocasiones nos interesa saber el texto que lleva por dentro un procedimiento almacenado en Sql Server, en búsqueda de algún campo o condición, para esto podemos utilizar el siguiente script con la cláusula LIKE de transact sql para realizar dicha actividad. Solo se debe sustituir la palabra TEXTO A BUSCAR.


SELECT * 
FROM   sysobjects S 
       INNER JOIN syscomments SC 
               ON S.id = SC.id 
WHERE  SC.text LIKE '%TEXTO A BUSCAR%' 
       AND S.xtype IN ( 'P', 'FN', 'V' ) 
ORDER  BY 2

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