Big data analysis using Python

It is no exaggeration to say that big data has become an indispensable part of any business communication. Desktop and mobile search provide data to marketers and companies around the world at an unprecedented scale, and with the advent of the Internet of Things, the amount of data available for consumption will grow exponentially. This consumption data is a gold mine for companies that want to better target customers, understand how people use their products or services, and collect information to improve profits.

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

Why choose Python?

The biggest advantage of Python is that it is simple and easy to use. The language has an intuitive syntax and is a powerful multi-purpose language. This is important in a big data analysis environment, and many companies are already using Python internally, such as Google, YouTube, Disney, and Sony DreamWorks. Also, Python is open source and has many libraries for data science. Therefore, the big data market is in urgent need of Python developers. Experts who are not Python developers can also learn this language at a considerable speed, thereby maximizing the time spent analyzing data and minimizing the time spent learning this language.

Before using Python for data analysis, you need to download Anaconda from Continuum.io. This package has everything you might need to do data science in Python. Its disadvantage is that downloading and updating are done as a unit, so updating a single library is time-consuming. But it’s worth it, after all it gives you all the tools you need so you don’t have to struggle.

Now, if you really want to use Python for big data analysis, there is no doubt that you need to become a Python developer. This doesn’t mean you need to be a master of the language, but you need to know Python’s syntax, understand regular expressions, know what tuples, strings, dictionaries, dictionary comprehensions, lists, and list comprehensions are – that’s Just the beginning.

Various class libraries

After you master the basic knowledge of Python, you need to understand how its data science class libraries work and which ones is what you need. Highlights include NumPy, a basic library for advanced mathematical operations, SciPy, a solid library focused on tools and algorithms, Sci-kit-learn, for machine learning, and Pandas, a suite of functions for manipulating DataFrames Tool of.

In addition to class libraries, you also need to know that Python does not have a recognized best integrated development environment (IDE), and the same is true for R language. So, you need to try different IDEs yourself to see which one better meets your requirements. IPython Notebook, Rodeo and Spyder are recommended to start with. Like various IDEs, Python also provides various data visualization libraries, such as Pygal, Bokeh and Seaborn. The most essential of these data visualization tools is Matplotlib, a simple and effective numerical drawing library.

All of these libraries are included in Anaconda, so after downloading, you can research to see which combination of tools better meets your needs. You can make a lot of mistakes when doing data analysis with Python, so be careful. Once you are familiar with the installation setup and each tool, you will find that Python is one of the best platforms for big data analysis on the market.

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