Many independent coding platforms and communities in the open source would proudly state that Python remains the simplest and definitely the easiest of all programming languages.
In at least 20 international coding competitions, programmers have admitted to using basics of Python to write the most powerful programs for various projects. If we were to talk about Python versus the rest (which includes legendary rivals such as JAVA, C/C++, R, and MATLAB), the competition spices up. It all boils down to how quickly coders can program, and for what purposes.
In this article, we will do a quick round-up on why Python for data science is the best option for learners and experts.
I know Python, Hire Me!
Why would anyone learn Python if not to get a head start into Data Science! The first basic step to enter into Data Science is to get trained in coding long algorithms and pool them into massive data lakes. The starting point since 2013 – Python learning through open source online communities…
If you know Python, you have 42% higher chances of getting noticed with recruiters, and 22% higher chances of clearing practical interviews, compared to competitors who know other languages, but not Python.
In short, if you know Python, you can say from the roof top – “Hire Me, I have mastered Python!”
Myth: Intuitive Coding that anyone can Learn and Master
Most Python learners starting first with data science are wrapped within the illusion of Python’s easiness. Well, it is definitely intuitive – thanks to it’s English inclination that makes it easy to grasp and code initially. But, the easiness stops there. Thereafter, it’s all about understanding scientific understanding, statistical reasoning, mathematical equations, and tons of engineering concepts that could be applied for various applications ranging from designing website, mobile applications to driving advanced in-car experiences and AI-based chat bots and gaming panels.
Truth: Python in data science alone does not run the show. It works with other programming languages and styles, borrowed from more than 50 different sources and communities.
Python has the best version control capability compared to other leading languages used in Data Science. It is popularly said for Python – Write once, apply everywhere.
Best practitioners in the Python ecosystem rely on the coding versatility that the open source programming language provides.
Another reason why Python coders swear by the language is its scalability in Data Exploration Analysis, also called by its shot form, DEA.
DEA investigations for Data Science projects cling to combination with MATPLOTlib and SEABORN. If you talk about user experience and working with statistical tools for advanced AIOps projects, Python is the obvious choice due to ease of data interpretations, exploration, visualization and agility toward in-depth regression outputs.
Like a secret formula, no master data scientist would reveal what works best for him or her. But, the secret ingredient would most likely be Python, and libraries. Obviously, Python for data science comes out as a winner, unchallenged.