Python vs. R for Data Science 2023: Demand Breakdown.

Python vs. R for data science: In data science, Python and R are the two most widely used programming languages and thousands of people around the world question which one they should use.

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In this article, I am breaking down the demand for these two languages based on LinkedIn search results that were carried out in two to three days.

This search was done for Python and R jobs worldwide and in the top 5 countries. It was impossible to differentiate the demand based on data science only, so I considered all jobs (Mostly developer jobs, specifically data science jobs, or data science-related jobs).

Since Python and R are widely used in data science, we will get similar results even if we consider all jobs.

This breakdown covers the Python vs R demand based on the following aspects:

1. Python vs R Worldwide demand

2. Python vs R Demand in Top 5 countries

3. Python vs R Demand based on Exp Level – Internship, Entry Level, Associate Level, Mid-Senior Level, Director Level, and Executive Level

4. Python vs R Demand based on Job Type – Full-time, Part-time, Contract, Temporary, Volunteer, Internship, Others

5. Python vs R Demand based on Location - On-site, Remote, Hybrid

6. Python vs R Demand based on Exp Level combined with On-site Location

7. Python vs R Demand based on Exp Level combined with Remote Location

8. Python vs R Demand based on Exp Level combined with Hybrid Location

9. Python vs R Demand based on Job Type combined with Internship openings

10. Python vs R Demand based on Job Type combined with Entry Level openings

11. Python vs R Demand based on Job Type combined with Associate Level openings

12. Python vs R Demand based on Job Type combined with Mid-Senior Level openings

13. Python vs R Demand based on Job Type combined with Director Level openings

14. Python vs R Demand based on Job Type combined with Executive Level openings

Let’s have a look at the graphs for each of these aspects:

Figure 1

We can clearly see that, there is a very high demand for Python users across the world as compared to the demand for R users. Python users' demand is approximately 4.5 times higher than the demand of R users, and we can conclude from it the demand of Python users in data science will be also higher than R users.

Figure 2

The above graph shows that the demand for Python and R in the United States of America (USA) is the highest which is followed by China, Germany, India, and the United Kingdom. But the top 2 countries that have a high demand for R users are USA and Germany.

Figure 3

This graph is a little surprising because Associate Level is almost equal to Entry Level experience level positions but the demand of users in both the programming languages is high only for Entry Level and Mid-Senior Level experience positions.     

Figure 4

It is straightforward from the above graph that the demand for Full-time jobs is high but the contractual position jobs are also rising and I believe the reason is a lot of people try to make their career in freelancing.

Figure 5

Due to Covid-19 remote jobs were increasing but as we moved to normal conditions on-site openings are again at large but hybrid position jobs are rising (Hybrid position jobs are the jobs in which you work remotely and go on-site when required). I believe this will be the future of data science and the rest of the tech industry as it is time and cost-effective.

Figure 6

If you are a Python user and want to work in a physical location then Python demand is high for Entry Level and Mid-Senior Level positions. The same is the case with R.

Figure 7

If you are looking for remote jobs then the demand of Entry Level jobs is less than the Mid-Senior Level jobs but it is only for jobs that require Python users, if you are an R user then the number of jobs is very low.

Figure 8

For the Hybrid location, the demand for Mid-Senior Level jobs is the largest but it is closer to the demand for Entry Level jobs for both the programming languages. There is one more that can be noticed here, the demand for Associate Level jobs is also high as compared to On-site and Remote locations.

Figure 9

Now, look at the demand graph based on Job Type and Internship jobs. The demand for Full-time internship jobs is the highest followed by Part-time internship jobs for both programming languages.

Figure 10

The above graph shows the demand for Job Type combined with the Entry Level positions. Again, the demand for Full-time Entry Level jobs is the highest but the second highest is Contractual Entry Level jobs for Python and Part-time Contractual Level jobs for R.

Figure 11

This graph is the same as the previous graph. In this graph, the demand for Full-time Associate Level jobs is the highest but the second highest is Contractual Associate Level jobs for Python and Part-time Associate Level jobs for R.

Figure 12

This graph is also the same as the previous graph. In this graph, the demand for Full-time Mid-Senior Level jobs is the highest but the second highest is Contractual Mid-Senior Level jobs for Python and Part-time Mid-Senior Level jobs for R.

Figure 13

The above graph shows Python vs R demand based on Job Type and the Director Level Jobs. The demand for Full-time Director Level jobs is the highest followed by Part-time Director Level jobs for both programming languages.

Figure 14

This graph also shows a similar interpretation as the previous graph. It shows Python vs R demand based on Job Type and the Executive Level Jobs. The demand for Full-time Executive Level jobs is the highest followed by Part-time Executive Level jobs for both programming languages.

If you have any questions related to the graphs used in this article, please comment and if you know anyone who is searching for jobs in Python or R then share it with them.

Note: This article is based only on one job search engine that is LinkedIn so do not generalize the information given in this article. Also, do not think that you should only choose Python to work in data science a lot of jobs require R as well. The decision to choose Python or R should not be only based on the demand of jobs, you can have a look at this article by DataCamp to decide which one you should learn if you’re new to data science.

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