Data Science

R Vs Python Vs SAS for data science

July 8, 2022, Learn eTutorial

R  Vs  Python  Vs  SAS for data science

As we all know we all live in a digital era and it is mainly dominated by software. Have you ever thought about how these softwares are built? Yes, it is very much clear that all these software are built with the help of different programming languages. In this module let us compare three programming languages “ R, Python, and SAS” and let us find out which language is best among these three for studying data science. All languages have their own pros and cons. So now let us compare the languages R, Python, and  SAS  based on the cost, difficulty in studying, capabilities in data handling, capabilities in graphics, what are advanced tools, and job scenarios, let us find out in which fields these languages are frequently used and support is given for customers. Apart from that in this module, we will also discuss the applications of Python, R, and SAS.

A brief introduction to  R, Python, and SAS

  1. R:

    R programming language is mainly used in the fields of academics and research. It is having an open-source nature because of that itself the latest techniques will be released very quickly and it is very cost-effective.
  2. Python:

    Python is a programming language which is created by Guido van Rossum and it was first released in the year 1991. In various fields of development, this language is used because of that itself it is an object-oriented and scripting language. The very main features of the language python are it always supports GUI programming, it is a high-level language, very easy to code, large standard libraries are available, it is an object-oriented language, and it's free and open source.(link).Nowadays it also supports sports libraries such as  NumPy, scipy, and matplotlib. It also has functions that are useful for all statistical operations and for building models.
  3. SAS:

    SAS is a kind of software that is very much suitable for mining, altering,  as well as to retrieve data that is obtained from different sources. We can also perform a statistical type of analysis on it. It supports many statistical functions, it has a very good GUI which will mainly help people to learn very quickly and efficiently. Amazing technical support is also provided by this language. Apart from all these things this one is very expensive as well as it will not always support the latest statistical functions.

Let us compare R, Python, and SAS using a few attributes :

1.Cost/ Availability

When we are comparing R, python, and SAS it is understood that SAS is a kind of commercial software. SAS is very expensive compared to R and python. Because of its cost, this cant is used by an individual whereas some private organizations hold the highest market share by investing in SAS. So if you are a part of such organizations it will be very easy to access or else it is very difficult. R and python are not expensive which means they are completely free.    

Language COST
Python Low
R Low
SAS High

2. Difficulty level of languages in studying 

SAS is very easy to learn compared to Python and R. On many websites, so many tutorials are available which are provided by different universities. Comprehensive documentation is available for SAS. R is a kind of low-level language because of that itself even for simple procedures longer codes are needed. Learning code is very important. Learning python is easy when it is compared with R and difficult when it is compared with SAS.

Language Difficulty in learning
Python Very easy compared to python and R
R Very difficult compared to SAS and Python
SAS Very easy compared to R and it is difficult when it is compared with SAS

3. How good are these languages in handling data :

R, Python, and SAS are very good at handling data. Now we all have advanced versions of these languages and when we are comparing these languages based on their data handling capabilities all 3 languages have the same capabilities for handling data.

4. Graphical capabilities :  

Python and R is having very high graphical capabilities compared to SAS. The graphical packages in SAS is very difficult to understand as well as less graphical capabilities compared to R and Python.

5. Job 

Language Difficulty in learning
SAS SAS is mainly used by big organizations. Small companies never use SAS because of its cost. It is very expensive and start-up companies cant afford it.
R and Python These two languages are mainly used by small and start-up companies

6. Advanced tool

Language Difficulty in learning
SAS SAS is having less advanced tools compared to R and Python.
R and Python R and Python is having more advanced tools compared to SAS

R, Python, and SAS which is the best programming language for data science 

The programming languages which are mainly used for data science are Python, JavaScript, SAS, Scala, R, and SQL.  All these languages are good for data science still among this python is the best programming language for data science. Among all these languages more scalable is python which means python is more flexible for the programs not only that it contains many varieties of libraries that are very suitable for data science. 

Main reasons why python is considered the best programming language for data science.

  • It is very flexible for the programmers
  • A variety of data science libraries are available such as NumPy, Pandas, and Scikit Learn.
  • All over the world, many programmers who are specialized in the python language are continuously contributing their findings and it helps in the growth of the python language.
  • Python is mainly used for machine learning applications,  audio, and video application, system administration applications, command-line applications, blockchain applications, Business applications game applications, and many more.
  • Programming is very easy in python compared to other languages because python language always uses very elegant syntax and is very easy to read the programs.
  • Data science always needs some key features to work with and python have a variety of libraries that will provide all essential features for data science.