Data analytics is a practice of systematic evaluation of data, with the help of statistical techniques and tools such as SAS, R, Python, etc. Business analytics has become the best support for every organization. Organizations use this practice to make data-driven decisions, whether by skilled professionals or by machines that can make automated decisions
Unstructured and raw data are useless and they need to be excavated. To make data useful and to extract the insight from it, there are some useful languages such as SAS, Python, and R. today, large as well as medium-sized companies implement data analytics techniques to operate their system.
So, to make it useful, these data need to be mine and interpreted. There are several programming languages like Python, SAS, and R to filter the data and make them useful. Many giant IT companies rely and operate on data analysis.
All three languages excel to perform the job of data analytics. Among these three, which language is better is a truly tough question to solve. Each has its sole features, exceptional to perform various tasks. Professionals who want to build their career as a data analyst have a bit of confusion that with path finds good for making their career. So, here is a small effort for their guidance.
First, know about the three important languages
SAS is the most proven software leader in the field of data science. It is popular for its diverse attributes and functions. It is probably the easiest language that people with no prior programming knowledge can understand and learn. It provides huge technical supports. Large companies like HSBC, Nestle, Barclays, etc use this software to make data analytics tasks. As most of the big companies use SAS, there are immense opportunities to make careers in this field globally. But it is not open-source software so; beginners find it tough to start with SAS.
It is a responsive and object-oriented language with high interactive skills and hands-on experiences. It is popular for its simple, clear, and easy to understand features. For its simplicity and versatility, it is used by beginners as well as professional data scientists. While there is tough competition in the SAS market, Python provides less competition yet immense facilities in the job field. As it is an open-source language, it is highly cost-effective to use for data analytics purposes. Startups, as well as businesses looking for budget-friendly solutions, have good support with Python.
R is almost equivalent to SAS and a popular open-source platform. Usually, research fields and academic sectors use R as their software solution. It takes more time to learn coding in R. if not applied correctly. It provides top graphical capabilities and produces a vibrant and responsive graphic interface. It is open-source software so; it is cost-effective and highly extensible with the latest techniques.
For data handling factors, you can say that all three of SAS, Python, and R are equally trustworthy and pretty good to provide the best data evaluating skills.
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