R vs. Python: The Data Science Wars
Choosing the right language for data analysis can be almost as complicated as actually learning the language. For many reasons, R and Python are two of the most popular: R is often praised for its great features for data visualization, as it was developed with statisticians in mind; plenty of programmers love multi-purpose Python for its so-simple-a-child-could-do-it syntax.
Why not just learn both?
The fact is, your time is limited. As data scientist and Dataconomy contributor Joshua Ebner says: ‘Learning a new programming language is a large investment in your time, so you need to be strategic about which one you select. The reason to focus on one programming language is because you need to focus much more on process and technique, not syntax. You need to learn how to think about data and how to solve problems using the tools of data science’.
How do these two languages relate to one another? What are the strengths of R over Python, and vice versa? Just like there’s no single best tool in a toolbox, there’s no single programming language that’s perfect for every data problem you want to solve. However, you need to be able to devote a significant amount of your time to truly master one tool. Spending 100 hours on Python or on R will yield considerably better results than splitting your time on ten different tools. In the end, your time ROI will be higher by concentrating your efforts.
The Data Science Wars
Data science online learning platform DataCamp‘s infographic provides a basic comparison between these two programming languages from a data science and statistics perspective, perfect for aspiring data scientists looking for the right language to start with.
And The Winner Is…
Even though the infographic suggests R and Python are equally good for budding data scientists making their first steps on the field, we believe R is the winner, at least for data science beginners, who are moving on from spreadsheets into programming languages. It is not only the most widely used language among data scientists, but it is also popular in academia, and in business. R also offers a simple approach to learning the key skills of data science: data manipulation, data visualization, and machine learning. After mastering the fundamentals data science in R, you’ll probably (want to) learn other languages to solve specific problems.
(Image credit: Michael Doherty)