Since the year 1950, the world has seen the emergence of more than a few programming languages.
Over time, people started to communicate with machines in these multiple languages.
As a result, plenty of wonderful software applications were born and many existing complex problems were solved.
But, as we moved into the future, the battle for the toughest and more robust language began.
While some of these were able to make it to the world that we know today, others faded.
Furthermore, new technologies and digitization swept the world off its feet.
This liberated the data which hitherto had no records or wasnt being captured.
On account of these new jobs are emerging that require programming language to accomplish the goals.
One such job is that of a data scientist, which more and more organizations are investing in today.
This is where data scientists come into the picture.
Based on these insights, companies form strategies and make business-critical decisions.
Insights from the data are the reason behind massive innovation that transform industries.
Raw data can be a nightmare at times.
They have all the noise and attributes that might be totally irrelevant to the goal of the organization.
Therefore, a data scientist needs a set of tools in an efficient and easy to implement programing language.
40% off TNW Conference!
It is becoming a popular career choice among people.
Furthermore, data scientists point out that obtaining and cleaning the data forms 80 percent of their job.
While there might be multiple tools out there to assist in this job, Python is the most preferred.
There are more than a few reasons behind it.
The popularity of the language Python is at its peak.
Developers and researchers are using it for all sorts of reasons.
There is no other language right now that does it better than Python.
Statistics suggest that Python is officially the most widely used programming language in the world today.
Why Python for Data Science?
One of the best features of Python is that it is an open-source language.
This means anyone can add to the existing functions of Python.
Apart from these they also need to implement algorithms on a daily basis.
Python makes all these tasks a hassle-free affair for data scientists.
Busy professionals who often have limited time to learn anything new.
Python, therefore, comes handy with its easy to learn and easy to understand capabilities.
Phenomenal scalability
Python excels when it comes to scalability.
It is much faster than languages like MATLAB, R, and Stata.
Data Science libraries
Pythons data science libraries make it an instant hit among data scientists.
This article was written byJames Warner,Business Intelligence Analyst.
it’s possible for you to read the original piece onMediumhere.
