In big data analytics, there are wide ranges of tools and techniques that are required and used in order to plan analytics to its desired goal that is to find insights from data. The entire idea of data analytics is based upon a very simple principle – the simple notion of business, knowing what the consumer wants in order to sell more. As the amount of data to account for grew exponentially, things kept getting harder. Data analytics has an important task to play in multiple areas concerned with the betterment of the public. This has brought more stability to the stock exchange, developed digital security way stronger than it previously was and has brought revolutionary change in the healthcare domain.

Python can develop a lot of ground breaking stuff and is often recommended as the first programming language to learn. It is meant for the serious business and has the firepower to take up projects involving critical matters like Natural Language Processing. Unlike most of its predecessors, it uses familiar characters for coding. The characters are easier to remember and hence make it easier to apply. This language banks upon its simplicity and accessibility. The simple syntax is easy to understand and programming novices find Python very useful and so do experienced professionals. Developers who have worked with Python for a considerable amount of time are totally engrossed by the easy going nature of the language.

Python programming is used in various quantitative fields such as finance, oil and gases, physics, for decades. A lot of companies, both new and old are making it a point that their data analyst should be skilled in Python big data analytics. The primary reason behind it is the easy accessibility of this open source language. Some of the other reasons are as follows-
● Better processing speed
● Accessibility in all platforms
● Operates equally well with structured & unstructured data
● Efficiently handles machine learning algorithms
● Possess large libraries that are vastly capable

Python functions in a multiple range of software systems. Social media, E-commerce, digital security and global marketing sectors use Python for applied data science and analytics. It comes with a great processing power and speed. Additionally, being open source, Python is affordable for the startups and small scale enterprises that might not have the budget to integrate SAS. It provides both economic balance and superlative services which is used by the sorts of IBM, NASA, Quora, YouTube, Dropbox etc.

The Python libraries are immensely voluminous and provide an extensive support for analysts and programmers. The libraries make it easier to apply a lot of machine learning algorithms or statistical analysis. They often save you from writing long codes that makes the handling of web requests much easier. The libraries also feature certain data visualization, data mining and natural speech recognition tools. Some of the Python libraries deserve a special mention while there are others which are very useful depending upon your requirements. Requests library is one of the most used and most adored libraries. The Robobrowser library is one of the newly added library and has already made a mark for its effectiveness in simulating a browser. These tools being free and easily available, play a crucial part in Python’s coming up as a major language.