Python has entered the big league of data science programming language very recently. Until 2015, it was largely used for mobile application development and coding for small scale automation. Python Data Science projects in 2018 attributed to 55% of the total global budget set aside for AI and machine learning applications.
In 2019-2025, this budget could grow by 250%, featuring in more powerful automation and machine learning applications at the back of very simple data analytics.
So, what exactly makes Python so easy to work with?
There are hundreds of open source data analytics programming languages. However, nothing matches the simplicity and scalability of Python. Data Science Python projects at IBM, Google and Baidu are reasons why huge community of developers and analysts are working 24-7 to make Python much friendlier to deploy for basic mathematical and data architecture.
Here are five very easy to understand reasons why Python programming language for Data Science is best option available.
- Python has over thousand fundamental open source components that can be easily studied and applied to algorithms
- Python lists are very simple to understand, making it the most user-friendly algorithm to manipulate and derive better results
- It has a very powerful memory management and object-oriented distributed system. Its standard library is very simple, procedural and functional to even leverage auto-completion of coding and syntax highlighting.
- Python supports many high-level virtual machine languages that could be open source or built with Java. For example, Jython, Pyjs, Cython and RPython are the various implementations of Python with other languages like JavaScript, C/C++ and R Language.
- All the top 300 big data companies and data aggregators like Google, Wikipedia, Bing, Yahoo, IBM, Amazon, Facebook, Twitter and CERN deploy Python for various aspect of programming, including for Natural Language Processing, Image Processing and Text Classification.
In the last two years, Python has been used by a community of programming application developers to create other programs for AI and Machine Learning.
In Python Data Science course, you are most likely to come across with macro-based programming languages that were built with Python and scripting languages.
3D animation packages, messaging encryptions, Email coders and visual effect composers like NUKE and GIMP are largely built on Python. In taking AI to the next level, Python has been a major component. Its integration with TensorFlow, Keras, RedHat Linux and Fedora has been intensely studied worldwide.
In cyber security and data visualization techniques, Python programming remains the most sought after skill after SQL, NoSQL and SAS, Tableau platforms.
As we inch closer to making AI part of every business, the role of Python would only grow in scale and prominence. It’s time to coil with Python as a great career option.
