Data analysis is one of the fastest growing fields in information technology, but which language does it best? R and Python are the most common languages used within the field, and which language is best depends solely on the developer and their needs. Here are the major advantages and drawbacks of working with either language.
Python: A Flexible, General Purpose Language
Python is often touted as being extremely easy to use. It’s considered to be simple but powerful, with a very low barrier to entry. Nevertheless, it can also be a very sophisticated language, in the hands of experienced programmers. Python isn’t used only for data analysis. Inspired primarily by C, Python can be used for general purpose programs as well. This makes Python a solid choice for programmers who are looking to broaden their horizons. Most programmers who already experienced with other languages will be able to easily and intuitively transition to Python.
On the other hand, Python’s simplicity can occasionally be limiting. Though virtually anything may be possible in Python, more complex tasks may need to be completed in a fairly circuitous way. Python is also less often used in the areas of statistical analysis. This will be restrictive to those who are trying to acquire a position within the data science industry.
R: A Focused, Complex Language
R is a language that is designed entirely around statistical analysis. It’s a powerful, complicated language that is dissimilar to many other programming languages. It is often considered to be half programming and half equation. R is far more frequently used for statistical analysis than Python, especially in academic sectors. It is a specialized tool that is designed for a specific purpose. Knowledge of R is rarer in the IT sector as a whole, which makes it an ideal choice for those who want to bolster their data analysis resume.
On the other hand, R is not ideal for any other type of programming; it resides firmly within the domain of data science. R is also often considered to be very difficult to learn, especially for those who are coming from a programming background rather than a data analysis background. R additionally doesn’t put a premium on performance. It is generally slower and less efficient than Python, because performance is not prioritized.
Any data analysis project should be equally possible in either R or Python. In many cases, it’s more important that a developer work with the language that they are more familiar with. For those who are interested in focusing specifically on the field of data analysis, R is better suited to statistical analysis and derivation. For those who are looking for a more general purpose language, Python may be ideal. Regardless, R and Python specialists looking for a position within the data science industry can contact Software Specialists today regarding our new and open job listings.
