Use Python for big data analysis

Big data has become an indispensable part of any business communication. Desktop and mobile search provide data to marketers and companies around the world on an unprecedented scale. With the advent of the internet of things, a large amount of data for consumption will grow exponentially. This kind of consumption data is undoubtedly a gold mine for companies that want to better target customers, understand how people use their products or services, and collect information to increase profits. Big data has become an indispensable part of any business communication. Desktop and mobile search provide data to marketers and companies around the world on an unprecedented scale. With the advent of the internet of things, a large amount of data for consumption will grow exponentially. This kind of consumption data is undoubtedly a gold mine for companies that want to better target customers, understand how people use their products or services, and collect information to increase profits.

The role of screening data and finding the results that companies can actually use falls into software developers, data scientists, and statisticians. There are many tools to assist in big data analysis, but the most popular one is Python.

Why Python?

Python is easy to use. This language has an intuitive syntax and is also a powerful multi-purpose language. This is important in the big data analysis environment, and many enterprises are already using Python, such as Google, YouTube, Disney, and Sony DreamWorks. In addition, Python is open-source and has many class libraries for data science. Therefore, the big data market urgently needs Python developers. not experts of Python developers can also learn this language at a considerable speed to maximize the time spent on data analysis, minimize the time required to learn the language.

Before using Python for data analysis, you need to download Anaconda from Continuum. io. This package has everything you may need when studying data science in Python. Its disadvantage is that the download and update operations are performed in one unit, so it takes a lot of time to update a single library. But it is worth it. after all, it gives you all the tools you need, so you don't need to worry about it.

Now, if you really want to use Python for big data analysis, there is no doubt you need to become a Python developer. This does not mean that you need to become a master of this language, but you need to understand Python syntax and regular expressions, knowing what is a tuple, string, dictionary, dictionary derivation, list, and list derivation is just the beginning.

Various class libraries

After you have mastered the basic knowledge of Python, you need to understand how the class libraries related to data science work and what you need. Key points include NumPy, a basic class library that provides advanced mathematical operations, SciPy, a reliable class library dedicated to tools and algorithms, Sci-kit-learn for machine learning, pandas also provides a set of tools to operate DataFrame functions.

In addition to class libraries, you also need to know that Python is not recognized as the best integrated development environment (IDE), and the R language is also the same. Therefore, you need to try different IDEs to see which one meets your requirements. We recommend that you use IPython Notebook, Rodeo, and Spyder at the beginning. Like a variety of Ides, Python also provides a variety of data visualization libraries, such as Pygal, Bokeh, and Seaborn. Matplotlib is the most essential tool for data visualization. it is a simple and effective numerical drawing class library.

All these databases are included in Anaconda, so after downloading them, you can study which tool combinations can better meet your needs. You may make many mistakes when using Python for data analysis, so be careful. Once you are familiar with the installation settings and various tools, you will find that Python is currently one of the best platforms on the market for big data analysis.

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