Using Python for Big data analysis

It is no exaggeration to say that big data has become an integral part of any business communication. Desktop and mobile search provides data to marketers and companies around the world at an unprecedented scale, and with the advent of the internet of things, large amounts of data for consumption will grow exponentially. This consumer 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 screening data and finding results that businesses can really use falls to software developers, data scientists, and statisticians. There are many tools to assist with big data analysis, but Python is the most popular.

Why Choose python?

The biggest advantage of Python is its simplicity of use. The language has an intuitive syntax and is a powerful multi-purpose language. This is important in the big data analytics environment, and many companies are already using Python, such as Google,youtube, Disney, and Sony DreamWorks. Also, Python is open source and has a lot of libraries for data science. So the big data market is in dire need of Python developers, and experts who are not Python developers can learn the language at a considerable speed, maximizing the time spent on analyzing data and minimizing the time it takes to learn the language.

Before using Python for data analysis, you need to download anaconda from Continuum.io. This package has everything you might need to study data science in Python. The disadvantage is that both the download and the update are in one unit, so it is time consuming to update a single library. But it's worth it, after all, it gives you all the tools you need, so you don't need to tangle.

Now, if you really want to do big data analysis with Python, there's no doubt you need to be a Python developer. This does not mean you need to be a master of the language, but you need to understand the syntax of Python, understand regular expressions, and know what tuples, strings, dictionaries, dictionary derivations, lists, and list derivations are--this is just the beginning.

Various class libraries

Once you have mastered the basics of Python, you need to know how it works and what you need for the data Science library. The key points include NumPy, a base class library that provides advanced mathematical computing capabilities, SciPy, a reliable class library focused on tools and algorithms, Sci-kit-learn for machine learning, and pandas, a set of tools to provide operational dataframe functionality.

In addition to the class library, it is also necessary to know that Python is not recognized as the best integrated development environment (IDE), and the R language is the same. So, you need to try different ides and see which one is better for your needs. It is recommended to start with the Ipython Notebook,rodeo and Spyder. Like all kinds of Ides, Python also offers a wide variety of 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 class library.

All of these libraries are included in the Anaconda, so after you've downloaded them, you can look at what combinations of tools will be more satisfying to your needs. You make a lot of mistakes when using Python for data analysis, so be careful. Once you are familiar with the installation settings and each tool, you will find that Python is one of the best platforms available for Big Data analytics on the market today.

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