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Understanding The Use Of Python Programming In Data Science
Why Is Python Popular Among Data Scientists?
Python may be quite popular as you can use it in a range of projects but would you consider Python for projects related to data science?
The well-known advanced programming language Python is primarily used in artificial intelligence, web development, automation, and data science projects. It’s a generic programming language for procedural programming, object-oriented programming, and functional programming.
Python has evolved over the past couple of years and has earned the reputation of being the ideal programming language when it comes to data science. The fact is tech companies of all shapes and sizes have been using Python for their data science projects is a further affirmation and indeed testament that Python is hugely in-demand.
Anyone reading this article would gain knowledge regarding the reason behind Python’s popularity as a data science programming language now as well as in the years to come.
How To Use Python?
As mentioned previously, Python essentially is a generic programming language that is useful for all sorts of projects.
When it comes to, website development, Python’s application uses Flask or Django for a website’s back-end. Django, for example, runs the back-end of Instagram, which incidentally is among the huge implementations.
For game development, though rarely used, you can use Python along with etcetera, arcade, kivy, and pygame, not to mention mobile application development, where Python provides several application development libraries like KivyMD and Kivy which can be used to develop the multiplatform application as well as multiple libraries including PyQt, Tkinter and so on.
The core focus of this article is Python as an application for data science. Undeniably Python is the smartest data science programming language and you are about to know why.
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What’s Data Science?
Oracle’s viewpoint is that data science is a combination of several fields; statistics, artificial intelligence, data analysis, and scientific methods for extracting value from information. It entails the preparation of data to be analyzed, including manipulating, aggregating, and sanitizing the information for performing data analysis even further.
Data science can be applied in varied industries and can effectively provide solutions that go a long way in unraveling the mysterious universe. Data science In the healthcare sector, enables doctors to use old data for decision-making, for example, for proper diagnosis and treatment of diseases. In the education industry too, dropout rates in schools are predictable, enabled by data science.
Python’s Syntax Is Simple
If there is anything else contributing to seamless programming then it has to be the intuitive syntax. Python simply uses a line for running your initial program.
The syntax of Python is awfully simple, and therefore programming is comparatively faster and easier. Unconventional functions can be written without even importing libraries before writing simple code, unlike similar programming languages. Minimal errors and easily identifying bugs are Python’s advantages.
The complexity of data science hinders progress and therefore Python is in demand to facilitate progress by using its broad community. If you have a query simply search and you will get the answer that you are looking for. Stack Overflow, for example, is a well-known website with queries and replies to programming issues uploaded.
Seldom you may have a fresh issue, but if you do, then you can post your specific query in the group and there would be favorable responses and your issue would be resolved.
Python Provides All Libraries
Python provides the entire range of libraries that you would ever require relevant to data science without having to refer to a similar programming language that may not have a comprehensive library. The libraries are user-friendly, therefore your experience would be unlike any other.
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This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2022 Avik Chakravorty