Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib BOOK FREE FULL 2022

 Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib BOOK FREE FULL 2022.

The steps for using Python in data science:

In recent years, the python language has taken on a completely unexpected dimension with an ever-widening use. In data science, python has become the language of choice for data processing and analysis. Python in data science has become a reference.

Thanks to an extremely rich API ecosystem, it can process data of very varied types (including SQL but also noSQL) and control advanced processing tools (notably Spark with PySpark for massively parallel processing of big data).

In this article, my goal is to introduce you to a fairly standard tool development process for data processing with the python language by introducing the appropriate libraries. 

 The use of python in data science Python has taken over many other languages thanks to 3 factors:

 The simplicity of language: 

for an object-oriented programming language, python has an ultra-fast learning curve. 

You are very quickly operational in python. A few days are enough to acquire the basics of the language and make you operational. 

 The multitude of libraries (libraries or packages according to terminology): setting up a python library is extremely simple and this has allowed the publication of specialized libraries by research teams. The impressive number of APIs to other programs or environments.

 It is extremely simple to connect to other environments with python. These three points make python the language of choice for many projects and especially for data processing and data science.

The steps for using Python in data science:

Setting up a python project in data science: Whatever your project, big data, IoT (Internet of Things), «classic» data processing, a number of questions are required when setting up your project. 1- The i/o (input / output): The idea is to define entry and exit in the broad sense first. The input information and the objectives. In an IoT project, for example, we will have the readings made by the object as input and output, we may either want to display these readings, or display decisions to be made, or make a decision directly. Once these inputs and outputs are defined at the global level, they must be identified at the local level. This is the input and output data format. Is it real-time data retrieved, data stored in databases, data stored in files...? For the outputs, in the same way, we will have to question the format of the data to be returned. Do we store the results in databases, do we transmit them to objects in the form of commands...? Answering these questions will allow you to define the tools to use in your python program in data science.

2- The volume:

Today, we talk a lot about big data, and for many of you, it’s still vague concepts. The question for each project is: Do I need to use “big data” technologies or can I keep the technologies I currently use? Of course, the answer depends on the context. There are generally 4 different cases: My data are already structured in databases (SQL type) and I do not foresee rapid changes in volume. In this case, the use of classic technologies such as MySQL, SQLite… is adapted and will include a python library import to make queries in SQL and with processing using python tools. My data is structured, small and I have no major time constraints (daily, weekly, no real time). In this case, we prefer importing data with python tools from databases or flat files. The amount of data recovered is very important and I have to perform heavy processing in terms of analytics with machine learning algorithms. In this case, you will have to implement massively parallel calculations using so-called “big data” tools. We prefer Apache Spark based on Hadoop clusters to store your data. Python will allow you to manage the entire process with the use of the PySpark API. I will not go into the details of this kind of case in this article, I will soon publish an article on Apache Spark and all its specificities. The volume of data is important but the treatments are light. In this case, we can favour Hadoop clusters for storage and MapReduce processing for analyses. You can also use python with an API to make MapReduce. Python will serve you for these 4 cases, I focus on the first 2 in this article with the use of machine learning algorithms.

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