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Python vs R – What’s the Difference and What Should You Study?

Person coding on laptop

Python and R are both open-source programming languages. Used in countless data science applications, they offer cutting-edge libraries and tools within their respective catalogues. Although many of their functions overlap, each language has distinct features that set it apart. 

Mastering Python and R can help you further your data science career due to their widespread use. In a 2018 survey of 24,000 data professionals, participants were asked which programming language they used most frequently. 83% of respondents cited Python and 36% cited R. This prevalence of Python and R means there are countless opportunities for data science professionals who can offer advanced knowledge of these programming languages. 

Open-source programming languages like Python and R are becoming progressively more essential across many sectors, creating an increased demand for competent programmers. According to Forbes, the following industries will have a particular need for programmers: 

  • Artificial intelligence
  • Computer security
  • Government and social services 
  • Healthcare 
  • Agriculture
  • Finance 
  • Machine learning 
  • Enterprise software
  • Education. 

In addition to giving you a choice of industries to work in, learning an open-source programming language can also increase your earning potential. Salaries may vary depending on factors like experience, location, and seasonal demand, but data from employment website. Indeed demonstrates the clear link between programming skills and above-average wages. 


What is the difference between R and Python?

There are several key differences between R and Python. Although they’re both open-source programming languages, R is primarily used for statistical analysis, which involves collecting, managing, interpreting and presenting data. On the other hand, Python provides a more general framework for data science applications. 

When it comes to R vs Python for data science, it’s necessary to take a closer look at the factors that set them apart in order to determine the most suitable option for your needs: 

Key Use

Python: As a general-purpose programming language, Python is highly effective at building out the core functionality of various applications. Its simple syntax allows users to build and update software with minimal effort.

R: Popular among statisticians and data miners, the key use of R relates to statistical software, data analysis and display. It offers a range of statistical techniques, such as linear modelling, classification, and time-series analysis. 

Ease of Learning 

Python: Mastering the basics of Python is easy due to how readable it is, particularly if you already have some experience with programming languages. 

R: The learning curve for R is a little steeper, as it’s unlike most other programming languages. However, your ability to learn R will depend on your background. If you’re familiar with statistical analysis, you may find it easier to get the hang of. 

In a survey by WP Engine, 900 developers were asked to rank commonly used programming languages from 1 to 10 in order of difficulty (with 10 being the most difficult). Python ranked at number 2, while R came in at number 6. 


Python: Due to its compatibility with multiple operating systems, Python is one of the most flexible programming languages available. You can find a library for almost any application in Python, from game development through to machine learning.

R: As it generally focuses on statistical applications rather than general-purpose programming, R is slightly less flexible than Python.  

Pros and Cons 

To summarise the differences between R and Python, these are the key pros and cons associated with each:

Python Pros 

  • Flexible 
  • Easy-to-understand syntax
  • Versatile library 
  • Compatible with multiple platforms and operating systems

Python Cons

  • Slow speed 
  • Not highly suitable for mobile development 
  • High memory consumption

R Pros 

  • Exceptional data wrangling and statistical analysis capabilities 
  • Highly compatible with other programming languages
  • Allows for easy reporting of results 
  • Continually being updated

R Cons 

  • Not suited to handling big data
  • Can’t be embedded into web applications 
  • Slow speed

Although neither R nor Python are perfect, they’re still invaluable skills to have for ambitious data science professionals. 


Should you learn Python or R? 

There’s no right or wrong answer when choosing between Python and R – it simply depends on your career goals and the type of programming work you’re interested in. 

To determine which programming language you should learn, ask yourself the following questions: 

  • Which language does your company/industry use?
  • What do you wish to use the language to do? (Statistical analysis, coding, debugging, data visualisation, or machine learning?)
  • What is the cost of learning the skill?
  • What is your background? If you have experience in data science or computer programming, you may be more suited to Python, whereas R is more suited to those with a background in statistics and analytics. 

No matter which language you decide to focus on, both Python and R can take your career to new heights. While an understanding of programming languages is important when working in the field of data science, having well-rounded hard and soft skills is also vital for those looking to progress their careers within the field.

Monash Online’s Graduate Diploma of Data Science features learning units on programming languages (including Python) so you can become a more confident programmer while learning other essential data science skills.

For more information, speak to one of our enrolment advisors today by calling 1300 289 184 or schedule in a time that suits you.