After a ravaging year, businesses and public sectors are putting huge efforts into increasing internal data dependency. The use of data has been proven to make processes and operations more efficient than before. The key to access the massive benefits of data is increasing data internal data literacy. A programming language easy to learn and use is the key to success in bringing about the literacy of such proportions. Python stands as a skyscraper among huts in terms of ease and comprehensibility.
It was created as a general-purpose coding language in the 1980s and launched for the masses in 1991. Python is a well interpreted, and high-level language with a design philosophy emphasizing code reliability. The language constructs are easy to understand and use with an object-oriented approach. Due to the easy nature of the language, python is easy to learn and can be used for micro to large scale projects.
This article will concentrate on understanding the reason why python is popular among budding data analysts and a weapon of choice for increasing data literacy.
1. Python is easy to learn
Due to having a clear syntax with high readability python is widely used for the development of applications and programs. Compared to other languages like c++ and java, python is easier to comprehend. For newbies, it is safer and easier to start with python, because of these features. As the easy syntax and high readability inspire confidence in a new learner while entering the world of coding.
In the case of data analytics, Python is widely accepted as the language of choice due to its easy to understand nature. Using python, coders can significantly reduce the duration of the learning period and start contributing very soon with better efficiency compared to other languages.
2. Awesome support infrastructure
Python boasts a large number of libraries for specific areas of operations. Since its inception, python is being used extensively for academic, research, and industrial purposes. The fairly long duration of service allowed for the development of such libraries.
Python is known for the ease of access and high readability; and the presence of these libraries makes the user experience, very different than usual free services and tools. Additionally, Python comes with access to user-contributed codes, mailing lists, relevant documentation, and supplements as a form of support. Something very unique for a programming language.
3. A flexible language
Python is unbelievably flexible. Due to the presence of extensive technical support infrastructure, python can be deployed for a plethora of tasks. Machine learning-powered tools and web services are the areas in which python excels. Data mining tasks and data models are executed in a faster and smoother manner due to the flexibility of python. Because Python is such a cool language to learn, the budding data analysts prefer python over the complications of using other languages.
4. Extended pack of analytics tools and wide range of specific libraries
After the collection of data, the inbuilt analytics tools of python allows instant analytics. Python comes with data analytics tools able to penetrate data and decipher the patterns more efficiently than specialized tools even. Python, due to its easy nature, correlates information faster and provides critical insights at a pace impossible to attain by other languages.
Python boasts a collection of completely open-source libraries, free to use. Libraries like Pandas, SciPy, StatsModels are constantly evolving and a robust source of solution for an array of problems. Nothing can be better than a continually developing support infrastructure with little to no expenses.
5. The community
Python has been present in the scene since the 1990s. Due to this long time in action, python is favored by multiple generations of coders. The python community consists of all kinds of people willing to help each other for better individual outcomes. Due to the presence of all kinds of people from different professional backgrounds and age groups, troubleshooting becomes easy.
Conclusion
Due to the widespread support network and ease of access, coders of all skill levels are favoring python for data analysis. Data analytics determines the strategies and tactics of modern-day ventures hence an important component of operations. Efficient execution of analytics with a greater speed is valued by businesses of all classes and genres.
The gradual lean of the commercial and public sectors towards analytics will eventually usher in the age of data analytics. The recruitment of adept manpower will inevitably arise. The situation demands skill and knowledge of python or any other programming language able to serve the need of commerce in the next decade. Given the predicted future of analytics, learning python can be an effective survival strategy.
