Start in analytics: Python or R

In 1991, Guido van Rossum created the Python programming language—simple, readable, and designed for a wide range of tasks. Two years later, in 1993, Ross Ihaka and Robert Gentleman created R, an improved version of S, a statistical computing language from the 1970s. Despite Python’s unwavering popularity, R continues to evolve and is still used to solve many problems in analytics. 

Unobvious influence

Python and R have an interesting history of collaboration. R was created for scientists and analysts as a language for working with data and conducting statistical research; Python is a general-purpose language, primarily aimed at programmers.

Ten years after the introduction of both languages, in the early 2000s, Python increasingly replaced R in solving analytical problems. However, the most popular Python libraries for working with data were initially inspired by the way this process is implemented in R. Nowadays, many who immediately begin learning and using Python are unaware of the significant influence R has had on how we use DataFrames in Pandas and plot graphs in Matplotlib.

With the advent of specialized libraries for working with data in Python, it gradually began to displace R. The language initially allowed for a wider range of tasks, and as this range expanded, it naturally took a dominant position in the data analysis market.

Is a complete replacement possible?

Python hasn’t completely replaced R yet, at least not yet.

1. R is, in some ways, simpler. For those who focus exclusively on data analysis—especially in academia—its capabilities are more than sufficient.
2. R offers tools designed specifically to solve problems in highly specialized fields—bioinformatics, ecology, economics, and geophysics. For example, there are solutions for analyzing patient clinical data and identifying disease risk factors, for modeling population sizes, for assessing biodiversity in different regions, and for analyzing sociological survey results and macroeconomic indicators. In this niche, R maintains its dominance, regardless of Python’s success.

However, Python is not a static system; it continues to evolve, and similar tools are also appearing in it.

As for the future development of both languages, it’s predicted that Python will strengthen its position as the primary analytical tool among programming languages ​​with each passing year. AR will remain a niche tool for solving specific problems. 

Where to start in analytics: R or Python

This question is often misinterpreted, focusing on which language is easier to learn. But the focus should be on something else—on what’s most likely to advance your career.

Yes, R is indeed slightly easier to learn, especially for someone with no programming experience. But if your goal is to start a career as a data analyst at a large IT company or startup, Python is the language of choice. Even a cursory review of job postings is enough to determine which language is of most interest to employers.

Of course, there are exceptions to the rule.

For example, if you work at the intersection of applied and scientific fields—bioinformatics, medicine, economics, or sociology—and want to develop further in this field, then R may be the language you need for your research. On the other hand, solving problems in these same fields often requires Python, so even in such cases, it’s best to start learning Python and add R as needed.

What else should I learn to work effectively with data?

The range of data science tools doesn’t end with R and Python: Scala, a language designed for data engineering and Big Data processing, is also available in this area.

An analyst is unlikely to encounter the need to use Scala. However, if you work with big data, Hadoop, and Spark, or would like to develop in these areas, Scala can be useful for tasks at the intersection of analytics and data engineering.

But Python can also come to the rescue here, as it’s perfectly integrated for working with Spark and big data. So, learning Scala, if at all, is worthwhile, only as a supplement, for deeper immersion and broadening your professional horizons. 

In very rare cases, you might also encounter languages ​​like Julia and Matlab, which are used in research projects. However, these are such exotic tools that there’s no need to prepare or learn them in advance.

The best choice to start

To summarize all of the above, we can say that in 99% of cases, the choice will be Python.

Perhaps the only area where you might need the R language exclusively is academic data analysis. Due to R’s deeper integration with statistics, it’s more convenient for processing scientific research data and creating visualizations for scientific publications. If you’re planning a career in an applied field or at the intersection of fields, learning R only makes sense as a supplement—once you’ve mastered Python.