Python has emerged as one of the most popular languages for data science and analytics these days. Python is a unified language capable of running embedded systems, data mining, mobile app development, and website construction. The growth of the IT industry has increased the demand for data scientist and Python has emerged as the most preferred programing language. Here is the list of reasons on why you should learn python for data science:

It’s a popular data analysis tool:

Python is one of the most preferred languages by the data science community as 35% of the data scientists are using python and numbers are expected to increase in coming years. It helps to explore the concepts of machine learning in the best possible ways. Since machine learning is all about probability, mathematical optimization and statistics and Python made them easy.

Python is easy to learn:

Python is the most popular general-purpose programming language when compared to other languages like Java and C++ and it is very easy to learn when compared to other data analytics tool. When it comes to using it is one of the most used data analytics tool compared to SQL and SAS.

Extremely scalable:

Python has emerged as the scalable language and is faster to use in Matlab and Stata when compared to R. even YouTube has migrated to python because of its scalability and flexibility during problem-solving solutions. The successful and skilled data scientist has managed to develop various applications successfully using python.

Availability of data science libraries:    

Reason for the growing rise in using python is its availability of data science libraries for aspiring candidates.  Another important thing is these libraries have been upgraded continuously. If a developer is facing issue a year ago means now it can be treated successfully with the upgradation of new libraries. Here is the list of libraries available in python:

NumPy:

it is used to perform scientific computing with python and it’s a high level of mathematical functions allows operating multi-dimensional arrays and matrics

SciPy:

it works with association with NumPy and offers effective numerical upgradation and integration

Apart from these, there are other libraries like pandas, Matplotlib helps the developers to develop different applications without many hurdles.

Final words:

Python is very simple, powerful and innovative which helps the data scientists be versatile and stay top of their game.

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