The comprehensive transformation of programming languages has witnessed a gradual shift since the 90s. A domain dominated by COBOL, C, C++, and FORTRAN in the 90s, was forever changed when Python was released in 1991 — leading to a holistic transformation of the sector by rising popularity and surpassing rivals. However, the larger Information Technology (IT) industry stands at a crucial juncture of another transformation with the advent of Artificial Intelligence (AI) and Machine Learning (ML), an aspect that has increasingly been made efficient by the productive disposition of Python.
A prominent sectoral survey among developers in 2021 revealed that Python was the 4th most popular programming language. A similar report next year claimed that Python had surpassed sectoral competitors Java and C to claim the top spot as the most popular programming language. This rising popularity is also reflected in the number of learners aspiring to acquire knowledge about this programming language. This claim is backed by Google searches, where Python has established itself as the highest searched language on the search engine. Furthermore, the worldwide AI sector is estimated to increase by a staggering 13 times by 2030, while the global Machine Learning market is projected to become $225.91 billion by 2030 with a CAGR of over 36%. This highlights the growing emphasis on upskilling in Python to drive career aspirations.
Enhancing productivity
Python is often addressed as the best programming language to develop AI systems and ML. While there are considerable differences between AI and ML projects owing to stack technology, the demand for deep research is often foundational in this domain. Learning Python helps developers establish a sound hold to develop new-age technologies such as AI and ML by raising general efficiency in contrast to their peers. By using Python, developers are empowered to concisely deal with developing, deploying, and maintaining solutions with access to the top libraries, independent platforms, extensive global communities, and more.
Furthermore, the fact that code written by Python remains simple even by the standards of developers who are beginning their careers. In Python, the syntax remains uncomplicated, supplementing the pace of developing solutions faster than other programming languages. The subsequent time buffer helps developers test the algorithms to guarantee all features are met before it is deployed.
Access to highly efficient libraries
One of the contributing factors to upskilling in Python is the vast libraries and frameworks that become accessible to developers. By using the readymade solutions offered by the libraries, developers are able to accelerate general development while ensuring the highest possible quality. For example, the NumPy and SciPy libraries help developers undertake numerical and analytical computing tasks and complete operations involving mathematical integration. The Pandas library helps developers to efficiently analyze and manipulate data — supplementing the scope of work with structured data in a seamless way with features like data cleaning, grouping, and filtering.
Additionally, Python offers data visualization libraries like Matplotlib and Seaborn, Machine Learning libraries like Scikit-learn, and deep learning frameworks such as TensorFlow and PyTorch that are considered particularly helpful in developing deep learning models, training and deploying AI models.
Enhanced scope of work
One of the major aspects behind the rising popularity of learning Python is it helps to develop affordable solutions, directly translating into a significantly raised scope of work. It remains an open-source programming language and does not have any license fees included in it. This aspect has also attracted numerous IT corporations to adopt it to offer affordable AI and Machine Learning solutions for bootstrapped clients. Furthermore, its extensive community and free tools help developers to ensure considerably reduced time for the development process which effectively decreases the associated pricing. Coupled with this, the interpretation and maintenance of Python code also helps to save costs and time — all aspects that have bolstered Python’s claim as the ideal programming language to develop AI and Machine Learning solutions. The subsequent increased scope of work also attracts scores of developers around the world to upskill their repertoire in Python to boost career aspirations.
Practical applications
Complementing and nurturing innovations define Python’s increased popularity among developers. Using deep learning frameworks such as TensorFlow and PyTorch allow developers to create and train complex neural networks to complete processes such as image recognition, NLP as well as generative modeling. By upskilling in Python, developers are empowered to work to develop an extensive range of AI and ML processes such as Natural Language Processing, Recommendation Systems, Autonomous Vehicles, Predictive Analysis, and many more.
The comprehensive benefits of learning and upskilling in Python by developers are far from limited to short-term gains, but also long-term advantages such as accelerated development, heightened innovation, scalability, flexibility, and holistic career growth. The IT industry, both in India and abroad, is leveraging the benefits of Python as part of their immediate and prolonged hiring plans — making it imperative for young developers to learn the programming language and supplement the sectoral push in innovating new-age technologies like AI and Machine Learning, transforming the future.
