Over the last few years, data science has become a buzzword, and today various industries rely on data science experts to draw insights from the unprecedented flow of data.
This process is not something that can be done manually or by traditional methods or tools of data processing.
Rather, there are many advanced tools and programming languages available for data science on the market today that are used to obtain, clean, and analyze the data.
In which python is one of the very popular programming languages that is used by every data scientist to understand the various functions of data science. Click here to learn data science classes in Pune
And if you’re one of those people who has recently stepped into the field of data science, a question may be running through your mind: Is Python necessary for data science?
This question can only be fully answered when one knows what Python is and what is its role in data science.
So, let’s start with a short overview of Python to know more about whether Python is necessary for data science or not and if so, then how.
Python is a general-purpose and high-level programming language that has a special reputation and position among all other programming languages because 66% of data scientists use Python on a daily basis.
It was released on February 20, 1991, and quickly became popular due to its simplicity and easy-to-read and understood syntax.
Therefore, compared to any other, This is an easy-to-use and open-source programming language that is used not only in the field of data science but also for the development of both desktop and web applications.
Its popularity can be gauged from the fact that there are currently more than 137,000 libraries available to make the data science experience even smoother, and they are growing every day.
Is Python Necessary for Data Science?
Well, there is no doubt that python is one of the most popular and necessary tools in the field of data science that is used to make the data meaningful in a better manner.
It is a programming language that is involved in every step of the data science lifecycle as most of the libraries and packages available in Python are specifically designed for data science-related jobs.
As compared to any other programming languages, Python offers many interesting and useful packages such as NumPy, SciPy, and pandas which are very helpful in analyzing, exploring, and visualizing data.
However, without Python, it is not only impossible to perform data-related science tasks, but it is also not easy to get a job because Python’s skill is highly valued in the job description.
What Are the Reasons for Learning Python for Data Science?
By now you know what is python and how important it is for data science. Besides, you should also know some of the basic reasons that how Python is different from others and why learning it is an important skill.
It is very flexible and allows developers to create something different and creative. Python is the only language that gives programmers so much freedom and flexibility that anyone can create any kind of application and run codes.
Easy to Read, Write, And Learn
It is very easy to read, learn and write because it requires a shortcode to write while in the other, we have to write a lot of lines of code to get something done.
It is a highly versatile tool and is used in many fields and tasks including data mining, product development, data science, AI, machine learning, gaming, web development, and many more. It is a programming language that allows us to do anything.
Fastest Growing Programming Language
According to Stack Overflow, a very popular Q&A platform, Python is one of the fastest-growing programming languages in the world. This is growing very fast with a growth rate of 27% year over year.
High Demand for Data Science Jobs
Because It is used everywhere in data science, every company wants to hire data scientists who have years of experience and deep knowledge of Python.
Large Supportive Community
There are approximately 8.2 million developers who use Python for the code and development of applications.
This means the more users there are, the more people contribute information, projects, and solutions.
And if someone gets stuck somewhere, that’s normal, then platforms like Github and Stack Overflow are available to help with a large community.
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