Python is a high-level, object-oriented programming language that offers a variety of features which makes it the most popular programming language today. Stack Overflow states that “Python has a solid claim to being the fastest-growing major programming language”. Python programming is powering the global IT market. It is a general-purpose programming language which is the beauty of Python. It is multi-purpose and can be used for a variety of applications. This allows you to learn and explore a variety of work domains. Not only this, but Python also has a wonderful reputation in the global market as well, and hence you can land a job in high-paying countries as well. The different fields where Python is being used extensively are as follows:
- Web development
- Data science
- Finance and trading
- Basic game development
- Computer graphics
- System automation and administration
- General and application-specific scripting
- Machine learning and artificial intelligence
- Mapping and Geography
- System integration
Python is easy to learn and is beginner-friendly. Python was invented with a vision to create an easy to understand language. Therefore, Python is recommended for beginners and experts too. It does not require any prior coding experience. The English-like syntax has made it feasible for people coming from diverse career backgrounds to grasp the knowledge and understanding of Python. Here we discuss briefly why Python is easy to learn for the beginners:
- Easy to learn, read, and use: The syntax of Python is concise and readable. A complete beginner can understand the objective or working of the code by just observing the code. The main focus of developing Python was to enhance readability. As a result of this, you can focus on other aspects of the code and even learn other skills along with Python. This reduces mental stress. It requires less time and effort to perform the same type of function or task than other programming languages. Python maintains the core functionality of readability as we keep scaling up.
- Less syntax memorization: You don’t need to memorize lots of sections of code used at different places. This reduces the chances of making mistakes in the code. This saves a lot of time. You can focus on more advanced concepts that can build your programming skills.
- Proper programming practices are built-in: While learning new concepts, it must align with industry standards. There are many programming practices to be followed but it always will come down to basics. The practice of indenting your code will allow you to stay organized and make your code easily readable.
- Very minimal setup time: Python is an open-source programming language. You can get started with Python without entering into the complex steps. You can download it from the internet and get started by running a few basic setup commands. One important feature of Python is that it displays the error at the runtime, so you need not compile your program again and again to check errors. This is a very helpful feature for beginners who are likely to make more mistakes in the early phase of learning.
Python is quite an old programming language though it gained popularity after 2000, its popularity continues to grow today and is predicted to increase in the future as well. Many freshers who are starting their career might be apprehensive to learn a language that is around 30 years old. They might think that the demand for Python might fade away with time. This is a common scenario with most of the technologies, they come and go with time. The specialty of Python is its scalability and flexibility. It has adapted to the changing requirements. To justify this point, let us consider the application of Python in data science. Data science is not new for anyone today. It is extremely popular and has opened doors to many new opportunities and possibilities. It has entered the lifestyles of common people. It has revolutionized the world and still, its complete potential remains unexplored. Every company and organization is switching to data-driven decision making to compete in the global market. Not only that, but they’re also are a number of examples of applications of data science around us. A few years back it was almost impossible to think about intelligent systems, but it has been possible with the advent of data science. Here, we list down some common examples of applications of data science:
- Personalized healthcare recommendation
- Cybersecurity
- Search recommendations
- Advertisement recommendations
- Predicting incarceration rates
- Traffic prediction
- Weather forecast
- Stock market predictions
- Food delivery apps
- Ecommerce
- Social media recommendations
- Government agencies use it to store information on people like ID details.
- Google Assistant and Amazon’s Alexa
It is possible to teach yourself Python. There are many tutorials and resources available online that can be referred to. There are chances of moving from one programming language to another and getting confused with the vast amount of resources available. There are many training programs offered which covers everything from scratch to advanced level. The classes are taken by instructors who have experience and knowledge of the relevant field. These courses are designed according to industry trends. There are hands-on projects and coding sessions that are reviewed by the instructors. Some reputed training institutes have tie-ups with many good companies who hire candidates from these institutes. Python Certification is an added benefit if you want to get into a good, high-paying job in Python programming. It distinguishes you from other competitors during the recruitment process. Many recruiters consider certification for selecting candidates through the first screening process. The companies hiring Python developers are as follows:
- IBM
- Amazon
- edX
- NASA
- Bloomberg
- Cisco
- American Express
- JP Morgan and Chase
- Mphasis
- Accenture
- Nokia networks
The above list mentions the top tech giants, but even startups and government organizations are also offering great opportunities for career growth and skill development. Data science has put the focus on Python due to its versatility and rich collection of libraries for statistics, data visualization, compatibility with big data frameworks, machine learning libraries, and many multipurpose applications. The different hot job profiles being offered to the Python programmers by top companies are as follows:
- Python full stack developer
- Python engineer
- Python back-end architect
- Python back- end developer
- Python full stack developer
- Python apps developer
- Python back-end engineer
- Data scientist
- Data analyst
