Most data scientists refer to either Python or R as their “go-to” programming language. Both have vast software ecosystems and communities, so either language is suitable for almost any data science task.

So the question is, which language should an aspiring data scientist learn first? Long story short, the answer is usually Python. However, each language has its own strengths and weaknesses to consider before diving head first.

Additionally, it’s important to note that Python and R are not the only programming languages or tools that can be used for data science. Some others include Scala, SAS, Julia, MATLAB, and much more.

What is Python?

Python is an object-oriented, high-level programming language with an easy-to-learn syntax. It was introduced in 1991. The 2008 revision, Python 3.0, has made many older libraries built on Python 2 not forwards compatible. Most of the data science job can now be done with five main libraries: Numpy, Pandas, Scipy, Scikit-learn and Seaborn.

The main advantage of Python is the implantation of machine learning on a large scale. Additionally, it makes makes replicability and accessibility easy. And if you need to use the results of your analysis in an application or website, Python is the best choice.

What is R?

R is a programming language and environment for statistical computing and graphics. R was based on S, which was introduced in 1976. Therefore, R can sometimes be considered as outdated. However, new packages are being developed every day, allowing the language to catch up to the more “modern” Python.

The cutting-edge difference between R and other statistical products is the output. R has advanced tools to communicate the results. For instance, Rstudio comes with the library ggplot2, a visualization tool that can generate box plots, violin plots, dot plots, strip charts, and much more.

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