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(Volume: 2, Issue: 8)
How to choose a scientific programming language for research?
Scientific programming languages are very necessary to implement and prove the competence or usefulness of any research in an associated application. They allow the research data to be manipulated, analyzed or visualized and aid in performing numerical computations. Furthermore, they are only needed to build any mathematical models or simulations and to make any comparative analysis. So, what are the top programming languages available at present to support the data scientists and the engineering research? Is there any best among them? How do you choose a specific programming language for your research?
Let’s have a glimpse at it...
Top programming languages
Presently, there are so many scientific programming languages available. Each one of it is equipped with its own features, tools and libraries to implement the research. A few hand-picked programming languages, which master engineering research based on their huge usage statistics can be seen below.
Python: It is an open-source, object-oriented programming language, which has the high usage statistics, as per the IEEE Spectrum’s 10th annual rankings of the Top Programming Languages, 2023. Python has secured this top usability by incorporating extensive and ubiquitous libraries (for instance, Numpy, TensorFlow and Keras) to accomplish complex data analytics tasks, numerical computing and machine learning. As Python has a simple and readable language syntax, it not only benefits the researchers and academicians, but provides support to web-developers as well.
R: It is an open-source, high-level programming language from the R Core Team. It mainly supports statistical computing and graphics, even for large datasets through its RStudio. Since R enables the data interpretation in a graphical form, it has become the renowned software for academicians and business personnel.
MATLAB (Matrix Laboratory): It is one of the high-level programming languages known from the past to support numerical computing. Most of the academic researchers have employed MATLAB to develop their complex mathematical approaches.
The Best Programming Language
Knowing the popularly- used programming languages, a researcher may seek for the best one to use in his/ her research. Generally, there are two indices to rank the programming languages, namely, the PopularitY of Programming Language (PYPL) and the TIOBE Index. The former decides the rank of a programming language by analyzing the frequency of searching its tutorial in Google. On the contrary, the latter index uses courses, skilled engineers across the globe and third-party vendors to make the rating, which is again based on the search results from renowned search engines like, Google, Yahoo, Amazon, Wikipedia, Bing, Baidu and YouTube. Both the indices can assist a researcher in finding the trending programming language in use. As per these indices being recorded until October 2023, Python is the most widely- searched and used programming language. However, each and every scientific programming language is unique in handling different tasks better than the rest of them. Further, the beginners trying to learn the programming languages can opt for the simplest, rather than the best. Hence, the term ‘best programming language’ can be replaced by ‘widely-used programming language’.
How to choose a programming language?
There are several factors, which are needed to be considered before choosing a programming language for the research and they are:
Nature of the research domain: Choose a programming language that best-suits the research domain. For instance, Python excels in data science and web development, while R supports statistical modeling and MATLAB aids in numerical computing.
Availability of required tools and libraries: Opt for the programming language, which has almost all the libraries and the packages needed for the implementation in an inbuilt sense. If so, specific tasks like, statistical analysis or data handling and visualization can be carried out with ease.
Interoperability: Check whether the chosen programming language is compatible to work with other tools or services, as interoperability might impart performance improvements.
Cost-effectiveness: Opt for the open-source programming languages that are cost-effective. However, if the tools or services from proprietary programming languages are mandatory to improve the results, either leave out or interoperate with the open-source programming languages.
Computing speed and memory: Choose a programming language based on the computing speed and memory requirements, which are essential to implement the research.
Ease of learning and utilization: Make a choice on the programming language, which is personally easier to understand and use. Level up to other languages, once you are confident with them.
Future scope: Select the programming language that can be promising in future, enabling your programming codes to exist and guide other researchers across years.
Hence, The Research Seer’s Rationale on “How to choose a scientific programming language for research?” is:
“Choose a consistent and compatible programming language for the chosen research criterion”
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