There are different programming languages that have evolved over time. And data science becoming more important than ever, one would wonder which programming language is best for it. Here, we can easily include Python, R, and SAS. Now let’s assess the features of these three programming languages Python vs R vs SAS and find out which one is the best to learn for data science.
Key Features of Python
- Coding has always been a smooth sail with Python. For those who are intimidated by the coding world, Python is very user-friendly. It has a simple English-like syntax which makes it a preferred choice. Besides, Python uses indentation instead of curly braces thus making the code easy to understand. In relation to other languages like C, C++ and Java, one can learn Python easily.
- Python is free and open source; it can be used free of cost. There are lots of free and open-source libraries in Python. These libraries are compatible with multiple operating systems including Mac, Windows, UNIX etc. There is no need to write code for everything; just you need to import and apply the functionality.
- Python is an interpreted language implying that it executes and shows the output of one line at a time. When there is line-by-line execution, debugging of the code also becomes easy.
- The object-oriented programming concept is basically about making reusable code with the needed degree of abstraction. Python supports object-oriented programming constructs including classes, inheritance, encapsulation etc. Thus one can develop powerful applications in Python.
- Python consists of the important capability to be extended thus leading to its multi-faceted nature. Once we compile the code in C/C++, we can apply that in our Python code. This code can be easily compiled and run anywhere.
- High-level programming languages like Python let the programmer to write codes that are not immensely dependent on the particular machine type. When the developer is writing code, he/she need not be worried over the architecture, memory management etc.
- The dynamically typed (which means that one need not declare the data type) nature of Python saves a lot of time and avoids those problems due to explicit typing.
- Graphical User Interface (GUI) is one of the key elements of Python. Python consists of several GUI libraries including PyQt5, Tkinter, Kivy, PSide2, etc. GUI is an amazing feature that lets a user to interact with the application and system in a convenient manner.
- Python can be used for machine learning too.
Key Features of R
- R is also an open-source programming language like Python. It is flexible to take into account the project’s unique requirements. Besides, you can help in the enhancement of R, customize its packages, and include more features.
- R is a comprehensive language in the sense that it delivers services both for statistical modeling and software development. R is an amazing language for data science and developing web applications. Besides, it is also an object-oriented programming language.
- R can carry out both simple and complex mathematical and statistical calculations with ease.
- Cross-platform compatibility is a major feature of R. It can be launched on any OS in any software environment.
- Just like Python programming language, R is also an interpreted language rather than being compiled. The R code is interpreted only one step at a time and straightaway converted into machine-level calls.
- R can take care of a multitude of structured and unstructured data. It also offers several data modeling and data operation features.
- Just like Python, R can be deployed for machine leaning also. It can be used for exploration and also for developing one-off models.
- R consists of a vast library of tools and can be used for the purpose of database manipulation.
- R is compatible with programming languages including C, C++, Java, .NET etc.
Key Features of SAS
- SAS is a comprehensive package for data analysis. You can use its analysis for both simple and advanced levels.
- The base SAS is a 4-th generation programming language (4GL) for the purpose of data access, data transformation, assessment and reporting.
- SAS studio is one of the highlights of SAS. SAS Studio can be conveniently accessed via any device and with any browser.
- SAS supports a multitude of data formats. It can read data from any type of file. It can even read data from files with missing data.
- The SAS Environmental Manager is a great monitoring and management system for SAS applications. It provides an integrated, operationally oriented group of administration and monitoring tools.
- SAS offers products and third-party approaches for safeguarding data and credentials that are exchanged in a specific networked environment.
Is Python or R or SAS good for data science?
Python is rising in popularity but that doesn’t mean that it is dominating the world of data science. Even R is widely deployed in data science. Data science comprises extrapolating useful details from huge stores of statistics and data. The problem of sorting these data is handled by Python efficiently because it is a general-purpose programming language. It lets you to develop CSV output for convenient data reading in a spreadsheet. R is primarily used for statistical analysis and also consists of library support for topics pertaining to econometrics and other crucial subjects related to data science. Tidyverse in R lets you to build an application ecosystem with the relevant strategy. When we talk about SAS, it lets data in any format including SAS tables and Excel worksheets. SAS also has the ability to manage data for extracting crucial details.
While these programming languages share some similarities, they also have their unique features. We cannot conclude which one is best for data science. While Python can be good for startups and small businesses, R can be used in Fintech companies, research work, social media analytics, healthcare, manufacturing industry etc. SAS is quite costly and hence those companies that can afford it can use it.