Should I Learn R or Python? … It Doesn’t Matter

[This article was first published on R-Projects – Stoltzmaniac, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Should I learn R or Python for data science?

I am asked this question regularly, both online and in person. There is a simple answer: it doesn’t matter. There are pros and cons to both which have been written about extensively so I reinvent the wheel by making a list here (do a quick search in Google and you’ll find tens of thousands of relevant results).

The fact is, you’re asking the wrong question.

You should be asking yourself, ‘what am I looking to accomplish?’

Once you answer this question, everything else should fall into place nicely. All of the articles you look up will certainly point you in the right direction.

Here are some examples:

  • I want to know how diverse my stock portfolio is, so I will build a statistically based stock portfolio
  • I want to know about what my competitors are doing online, so I will scrape the web for information about business competitors
  • I want to know what my local government is spending money on, so I will find the data and do some analysis

If you are learning a language to put on your resume and get a job you’re doing something wrong!

There is no shortage of information on in-demand skills found in online job listings. You may look up trends to see what’s more popular, but results vary based off of how you look for the information. Consider the following charts and ask yourself, what conclusions could I draw that would lead me to any sort of decision?

From this basic web search data, you might determine that python is the most popular language (side note, these are all dwarfed by C++ and others). Does that mean you should learn python first? Maybe, but probably not. Learning the most popular language could potentially have a negative impact because there may be less scarcity in the market for that skill. The reality is, you have already narrowed your search down to the most popular languages in data science and that is what’s important. No matter which choice you make, you will be fine! There may be a job or two which requires proficiency in one language or the other, but you will always be able to find something interesting with either language under your belt.

It’s time for the good stuff!

The only way to make the decision for yourself is to find something you’re interested in and get started building something. When you care about what you’re doing, you will become great whatever you choose! Here are few keys to success:

  1. Set aside time to learn every single day (and learn how to learn)
  2. Read a lot! Books, blogs, articles and studies encourage you to think about new ways to do things
  3. Work with open source data
  4. Create a GitHub account
  5. Use real data and make something that matters to you

It’s time for you to get out there and do something that matters.

Every project you complete will give you a sense of pride. Humans thrive from a sense of accomplishment. Once your hobby turns into a skill, you will have no problem finding a job, learning a new language, creating a strong sense of self-worth and making something you’re proud of!

Have fun!


To leave a comment for the author, please follow the link and comment on their blog: R-Projects – Stoltzmaniac. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)