Another R vs Python debate?
Nope, it’s not.
Far from it.
I understand your concerns.
If you are reading this story, you have probably come across a few reviews or participated in similar discussions.
Deciding between two popular programming languages to do data science has never been this heated among professionals.
The debate is endless and to some extent, needless.
You have probably read a handful of resources on Quora, Medium, or YouTube about which one to learn.
It did not help but get you more confused because there was never really a clear winner.
I found myself in the same boat years ago. I read tons of articles on which language was better.
The more I tried to see which one was the best based on the reviews and comments, the more confused I became.
It actually got worse that I spent more time reading reviews and comments than learning and solving problems with what I already knew.
The excellent advice never came until I found a simple solution that worked.
This post will uncover the myths surrounding which of the two popular data science languages you should learn and why.
#1 — Find Out What Your Community Uses
Figuring out what your team uses and how your work will be utilised down the pipeline will help you decide
The easiest way to choose is to find out what your community uses. You won’t go wrong doing this.
For example, I have a background in Public Health. When I researched this industry, both in academic and in the profession, most professionals or experts I have met use R. I am not saying Python is not great, but that’s what the community uses.
So this tells one thing.
If I intend to join the workforce one day and probably have a mentor or provide mentorship to other budding data scientists, then R is my best bet.
Once I realised this fact, I did not think twice. I stopped reading reviews about which one language was better and focused on learning by solving real projects.
If you have a Computer Science or Engineering background and work with a data team, it might be better to choose Python.
Python is a multi-purpose language. Asides from data science, it can also be used for web development, artificial intelligence, and the like. So your technical teams might prefer to have scripts written in Python.
Figure out what your team uses and how your work will be utilised down the pipeline. This will make your choice easier.
#2 — Start With One
For starters, learning both at the same time is not a great idea
You have probably read articles, reviews, and comments asking you to learn both. I would not recommend this for beginners.
Sometimes, learning something new is not fun.
Therefore, I would highly recommend learning one first, not both. If you attempt to learn and understand both while starting, you might get confused, and this will impact your progress.
Therefore, focus on one when starting.
Which one should you learn?
Go back to step 1.
#3 — Slowly Pick Up The Other
Be a master at one, then slowly pick up the other in your spare time
You have discovered what your community uses and picked it up quickly.
This is fantastic.
Your next move once you become terrific (I would recommend at least a year working daily and solving problems), you can slowly see what the other language has to offer.
Get comfortable and ensure you are building real projects with one before you pick up the other language. Don’t just learn the basics and assume you are good enough.
It’s easier to pick up another language once you become very comfortable with one language.
For example, I worked with R a lot, but I have picked up Python. I am getting so comfortable with Python that I started learning another Python framework like Django for web development. It’s that easy.
Final Thoughts On The R Vs Python Debate
Data Science is fascinating, but the argument about the tool to use for data science is unnecessary and confusing.
You will do yourself a huge disservice if you keep looking for the best.
The best one is the one you stick to and use to solve your problems and add value to your work or organisation.
You don’t have to spend months seeking the best. This time can be spent learning to become better or solving problems.
— Stop reading comparisons
— Start with what your community use
— Develop your skills using this language by building projects and solving problems
— Become comfortable with the other language by slowly picking it after mastering the first language