It provides a variety of data analytical functionalities.Įach tool and technology has its advantages and disadvantages. SAS is a paid and expensive software, Still, It is the leader in the data analytics field. Therefore, there are huge career opportunities for R and Python experts in the field of data analytics. These can be afforded by start-ups as well as big corporates. R and Python are open source technologies. It is predicted that it will become the most popular among the three. Python has recently seen tremendous growth. Popular with statisticians and individual programmers due to flexibility and repositories. It is used by most of the huge corporations for some aspect of their functioning. The most popular tool in the corporate world. KerasR package is provided to interact with python Keras.ĭeep learning is majorly supported by frameworks like Tensor Flow, Keras, and others. It is well tested before launching.So there are hardly any chances for errors.īeing open-source, Extensive testing is not done so there are chances for errors in these updates. The coding part is not much worry.īeing licensed software, a new version is launched periodically. A high level of analytic modeling is supported.Ī high level of analytic modeling is supported.Ī medium level of analytic modeling is supported.Ĭomponents can be picked directly because of its Drag and Drop feature. SAS is the leader for Analytics features. Many visualization tools are available for free. SAS Visual Analytics tool is available but it is costly. Hence career opportunities will be more as start-ups and big organizations both use it.
Python is free and open-source so start-ups can afford it. R is free and open-source so start-ups can afford it. Career opportunities will be available for big and selected organizations. Thousands of contributors for Python, hence any up-gradation is easily available to its customers. Thousands of contributors for R, hence any up-gradation is easily available to its customers. Only the SAS developers are allowed to produce any new features hence it takes a lot of time. SAS is accessible only by the SAS Institute. But, Jypter Notebook is popular to work on python. You should have good knowledge of coding in R.Įasy to learn but do not has GUI. It has a good stable GUI.īeing a low-level language, lengthy codes are required for short procedures as well. The online community of its users is available for help but not instant help.Įasy to learn if you know SQL. No technical support as it is open source. But, R has a big online community which can be a helping hand.
SAS as a licensed software provides good dedicated technical support to its users.
SAS is the most expensive software in the world.īest graphical capabilities among the threeīeing a closed source, you need to buy products for SAS advanced features.īeing open-source, you can upgrade new features for free. Open Source so support transparent functionality. Comparison among SAS, R, Python FactorsĬlosed Source so does not support transparent functionality. Let’s try to figure out the comparison among three Python, SAS, and R So that you can decide which option is better for different scenarios. It is a low-level language and highly extensible. Amazon’s recommendation engine is an example of it. The machine is trained to perform or behave in a particular way based on past data trends. Machine learning has also become popular. The visual appearance of a data pattern is easy and handy for businesses to understand the market trend and make a better decision. These visualizations are in form of bar charts, pie charts, scatter graphs, histograms, and others. Data Analysis is used to show the trend and patterns of existing data or markets. Therefore, Data Analysis has become an essential part of the industry. Big e-commerce and other companies are making huge profits based on the principle of data analysis. The business decisions are taken based on past data and trends. Data mining and filtering are required for well-structured data which is used for business decisions. The issue which is faced by the industry is that it is not always structured. The volume of data has been significantly increased. Data is the fuel to the digital world nowadays.