Choosing the right language is the first thing you must do when it comes to working on predictive analytics. There are plenty of options, but R and Python appear to be the most popular ones. Most developers choose between these two languages when they develop applications for predictive analytics.
Both programming languages are available on all major operating systems such as Linux, Microsoft Windows, and Mac OS X. While we can’t say that one of them is the right choice, they both have their pros and cons. It is up to you to analyse them and decide which one matches your needs. Nonetheless, we took a look at certain factors in order to make things easier for you.
When it comes to data analysis, R is a more popular in the developer community. That is because it comes with an Integrated Development Environment that was created for data analysis. The R programming language was in fact created for data analytics, and it is also used to teach statistics in high schools and colleges from all around the world. Python can also be used for data analytics, but the obvious choice here remains R.
If you look at its popularity you can also see that R is used more when it comes to data analytics. Nonetheless, Python has its advantages as well. For example, if you want to implement algorithms for production use you might want to choose this one. Additionally, Python appears to be a better choice if your data analysis will be used in production databases or web apps.
You don’t have to worry about the availability of these two programming languages as they are bot available for free. There are no costs for either one. Python is usually better when it comes to code reliability, while R is recommended for statistics and data analysis.
Learning how to use it
If you are a beginner, you might want to take into account other factors. For example, Python is usually considered more mainstream, and it can represent a better option for amateurs. On the other hand, R can be considered quite complex for those who are not familiar with programming.
Before you choose a language you might want to see how many resources are available for each one. The community provides assistance for those who want to master these languages, but you need to make sure that there are enough resources for your learning process.
Regarding this topic, a bit of research has already been done. If we take a look at Google search results we can see that there are a lot more results for R compared to the results offered for a Python search. To compare the two, when searching for “linear regression”, there were 6.48 million results for Python and 77.1 million for R.
If we are talking about the disadvantages of Python, we must mention the fact that it has its flaws when it comes to mobile development. Additionally, the fact that it is quite simple to learn Python might make it difficult to pick up other programming languages in the future. Python might not be the best when it comes to data analysis.
One of the biggest cons of the R programming language is the fact that it can be quite difficult to learn. If you need to learn it as fast as possible, you should know that R requires some time. Additionally, its memory management department could be improved. However, if the community continues to grow, this is an issue that will be fixed.
Nicole Hicks a graduate of UFT. She’s based in Toronto but travels much of the year. Nicole has written for NPR, Motherboard, MSN Money, and the Huffington Post. Nicole is a financial reporter, focusing on technology, national security, and policing.