Python on an Overdrive: Where is Data Science Heading to?

Python and data science are intertwining with each other with an unprecedented rate of delivery. Every day, around the world, 75% of the analysts and AI engineers are continuously working with a range of Programming languages to build machine learning algorithms to manage data science projects. All that collection of data, analysis, and reporting takes a lot of computing power and storage capacity. This is all taken care of using the world’s fastest growing and most productive Open Source Language program-

Python. Like a Swiss Army Knife, Python Data Science projects are good enough for every object-oriented programming project.

Here are some of the key things you should understand about the Python Data Science and how it applies to the modern computing universes.

Python is No Knock on the Door; It’s a Barge Straight into the Center of AI and ML Applications

According to Facebook’s own official product team announcements, the Company has been using Python for all their data analysis and social media aggregation since 2014. Major reason – Python Data Science was already projected to take over the world of Super Computing with its ease of coding, seamless integration and democratic applications that matches every AI ML aspirations.

Python was created with the sole aim of being ‘Fun to Code’ with. And today, it has more or less managed to live up to that aim, even 30 years after it was first conceived in the labs of creator Guido Van Possum.

So, why Really Scientists Prefer to Work with Python?

Given its freedom of coding and applications, data scientists and analysts from all over the world are bind by one common thread—Python’s fun scripting. Which mean, anybody from the coding community can write a code and allow others to take a look at how it delivers on the promises! So, if data scientists were to look for a new project in Data Science, they can simply visit libraries containing Python sentences and templates, formalize a new script and apply to their ongoing projects.

For this reason and many others, Python is much loved by Data Scientists. Plus, it doesn’t really matter which field or specialization of engineering you belong to. Or, for that matter, even it doesn’t matter if you know how to write a code. As long as you know how to use English and write logical English sentences, you can possible learn Python coding and Python structuring in less than eight months.

Let’s See, What’s the Next Growth Map for Python?

Because of Python’s extensibility and generic applications, it is inevitably becoming the center of attraction for data analysis and so much more in AI ML schemes.

Python’s success recipe is its 80k+ Python libraries that are hosted and available freely through the Python Package Index (PyPI). And, it’s growing at 15% every year!

Some of the best libraries that you will come across working in Python Data Science projects include –

  • SciPy
  • PyBrain
  • PyTables
  • Bokeh
  • PyMC
  • IronPy
  • NumPy
  • PyLearn2, and so on…

Meeting Python community of coders and data scientists often would further help adoption of Python as the universal AI ML growth catalyst in 2019-2024.

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