core differences between R & python programming languages

1. R and Python Programming Languages: 5 Core Differences We have seen sports lovers split hairs and sometimes even come to blows about who was better between Pele and Maradona, or between Ayrton Senna and Niki Lauda! To the programmer, the debate about which is between R and Python may not become as heated; yet, for any programmer or one who is serious about making a career in programming, a comparison (mainly of the differences) between R and Python is very much in order. The reason: both are programming languages that are used extensively in the tech world with their own characteristics, purposes and benefits. Let us get down to exploring five areas of divergence between R and Python: Uses: The area in which the two differ the most from each other is in their uses. Being very versatile and flexible, R packages can support statistical developments. This means it is ideally suited for areas that require statistics and computation, such as genetics, finance, and psychometrics. On the other hand, since Python is object-oriented and has a very easily readable code, it is ideal for the areas concerning data science, such as deep learning and Machine Learning. It is also excellent in helping to build services or tools, which is why it is used by social media sites. Prior knowledge: Another area in which R and Python vary from each other is in the requirements for prior knowledge. To work in R, the programmer needs to be proficient in mathematical areas such as statistics, linear progression and algebra, and probability, while Python requires the programmer to be aware of topics like data analysis, data visualization, Machine Learning and the like. Language structure: R is considered a command line interpreted language. This is logical and understandable, when one considers its basic characteristics and its uses. On the other hand, the big thing about Python is that its syntax is in English, which means it does not require any complex computer programming language or commands to understand. Data organization: With more than four-fifths of the world’s data being unorganized and unstructured, especially those derived from the social media, the packages that Python offers, such as PyPI, Scikit-image and NLTK, help in structuring this data. This is reinforced through its stack of libraries, for which it is very popular. With R, the scene as of now is that

2. although it also helps in data structuring, its current support has a long way to go before it can catch up with that of Python. Ease of learning: Although this may not strictly qualify as a point of significant difference, it is taken here, mainly because most programmers are agreed in their viewpoint that learning R is tough as hell! This is till they get the ropes, after which they can advance their learning. On the other hand, python is something you can make your six-year old learn during her vacation. Conclusion: As we have seen, R and Python are two giant programming languages that serve their own unique purposes. They are both used in their respective areas with vigor and purpose. For any programmer who wants to make a mark in her field, it is necessary to know how they differ from each other, so that taking a decision about which to choose becomes easier. We hope this blog has helped you do this. Do you feel there is something amiss in this article? Want to add your thoughts or perspectives? Please feel free to do so. We love to hear from you. Before we leave, we would like to leave you with a wholesome list of courses on Python and R. We hope you will find them useful.