The data science world is becoming one of the most important and widely popular sectors out there and it has gained the attention of many professionals. One wonders as to which is the best language to use for data science — Python or R? While both have become increasingly important, let’s find out which of these is the most apt for your need!
Purpose
R is a language that was created with a focus on data statistics, graphical models and data analysis. On the other hand, Python is all about productivity and code readability. So, at first sight, it seems that the two languages are working alongside each other to deliver great results. However, R is used mostly in research and academics, whereas Python is more suitable for programmers who want to do data analysis.
Usability
R is suitable for statistical models. It also has stylesheets, but it is not used by everyone. On the other hand, Python makes coding as well as debugging a lot easier thanks to the syntax. The overall code indentation affects the meaning and each piece of functionality is written the same way, whereas you can find it written in multiple ways in R.
Flexibility
Using complex formulae is quite simple in R and you can do a wide range of statistical tests. Python is quite flexible too, especially if you want to try out something that was never done before with this specific language.
Ease of learning
Unlike other languages, R is harder to learn when you get into the basics. However, once you have everything nailed down, the process is a lot simpler and you can accumulate the knowledge you want without any problem. This is not hard for experienced programmers but it can take a toll on beginners. Python on the other hand is very focused on readability and simplicity, and therefore it can help you learn even if you are not accustomed with data science.
Task
R is easier for beginners in the data science sector because it helps obtain statistical models with complete accuracy, while Python is really good for implementing algorithms. So, using either Python language or R can definitely help you with any type of project that pertains to data science.
Data handling
The R language is suitable for data analysis because it can handle a large number of packages and use plenty of formulae. Though it can handle basic data sets easily, it requires the use of packages for the larger data sets. While Python may not be the best choice for data handling, it has improved exponentially over time and therefore it can really deliver a very good set of results as well.
Support
There’s no denying that the more support that each language gets, the more value it can deliver. R has a lot of data analysis support starting with RDocumentation, R-Help and Stack Overflow. Python has the latter as well as many mailing lists designed to help you get the information and results you want, as fast as possible. This makes it a great tool with interesting value-adds.
Pros and Cons
R has amazing graphical capabilities and it can deliver stats in a visual form with ease. It also provides a very powerful and capable ecosystem along with offering you numerous tools that can be accessed at any given time without any major issues. While it’s very powerful when it comes to statistics, it does have its own downside as well. R is slow and has a steep learning curve which might hamper the overall benefit that it can provide to users.
Python on the other hand is great for general purpose, delivers good graphical capabilities and makes the entire graphics experience very good on the whole. However, it is not as mature as R in the data science world and that can bring down its overall capability marginally.
And the winner is…
If you are looking for a winner, you need to know that both of these tools are built for stellar data science applications. They are advanced and open source with great online communities. The champion therefore is up to you — depending on what best fits your needs in terms of the problems you aim to solve, the resources you spend in learning the language and the tools used in your world of data science!
