Did you enjoy the show earlier this month? You can catch a clash of champions as two programming giants go head-to-head – have a read of our last blog! This week we’ve got something extra exciting in store; in what’s tipped to be another epic showdown. It’s Python vs R. So, once again we ask you to sit back and hold your hat cause we’re underway for the second event!
Round 1… Use cases
Our first contender needs no introduction. Python’s use cases are vast within web and software development. Programmers use it for data analytics, machine learning, and even design. On the other hand, R’s uses are in statistical computing/analysis, data analytics and scientific research. It’s one of the more popular languages, with data analysts and marketers using it to clean, analyse, and report data.
Round 2… Data Scientists prefer…
Given that Python is the more popular language, it makes sense for data scientists to prefer it because of the abundant resources available to help streamline programming. Additionally, as data scientists delve deeper into machine learning and AI, Python allows for easier collaboration with software engineers who don’t particularly favour R (Sucky, 2021), resulting in better communications with colleagues.
Python has gained in popularity due to its code readability, speed, and functionalities. All of which make it great for mathematical computation and figuring out algorithms. Individuals with a background in software engineering may prefer Python as the coding and debugging is made easier with the simple syntax.
Round 3… Data Analysts prefer…
Although Python is the more popular language, R is just as viable for data analysis – exploring data sets is easier done via R in the early stages of analysis and exploration. It would then make sense to switch to Python when it’s time to ship some data products, as it’s used to implement algorithms for use in production.
Furthermore, R is great for exploratory data analysis as stats models are made with a few lines of code. Data analysts prefer R for when data analysis tasks need analysis on individual servers. Also, R is widely considered to be the best tool for creating reporting visuals and has many functionalities for data analysis (DataCamp, 2020).
In conclusion, there is no outright winner from this showdown. Both Python and R are equally viable for both data scientists and data analysts. It’s up to the individual, meaning the winner is subjective. Did you enjoy this blog? Stay tuned for our next update in a fortnights’ time!