Data Scientist or an Analytics profession is the calling of recent times. This is a new breed of professionals who possess the technical skills required to solve the complex problem and also are inquisitive enough to come up with problems that need a solution. This, in turn, assists corporate to come up with predictive analysis to spot trends and help come up with realistic solutions. Data scientist or analyst work with high volumes of data to drive to conclusions. They are part mathematicians, part computer scientist, and trendsetters. Huge volumes of unstructured data or Big Data cannot be ignored but is considered as a gold mine that helps increase the revenue of organisation across different fields like, financial, IT, retail, Hospitality, education, in short, the whole spectrum.
Earlier data scientist started their careers as either statisticians or data analysts. But with the evolution of big data, there has been a growth in their roles too. Data is no longer just associated with IT, it requires systematic analyses a creative curiosity and most importantly, knowledge of tools to translate great ideas and information into a simple presentation for the non-technical audience responsible for taking the decisions.
And thus, for an individual who is in the process of advancing his career in data science, comes the struggle to choose the right tool for the job. It is an ongoing battle as to which programming language is best suited for data analysis. And although in recent times there are many options that are available, the traditional question is primarily always between SAS or R, with Python as the new entrant which cannot be ignored.
Main difference between both programming tools –
Open v/s Closed – SAS is a closed source; it requires licences and approvals, hence it does not support transparent functionalities. R programming language and even Python coding program are open sources and as opposed to SAS contains detailed transparency of all of its functionality.
Cost – SAS is one of the most expensive tools to existing. Since R is an open source software, it can be downloaded for free by anyone.
Learning – SAS is fairly easy to learn, especially if one has the basic SQL knowledge, it has a stable GUI interface. Tutorials of SAS are also available on various sites. R is a low-level programming language and hence it requires complex codes for shorter procedures, one needs deeper insights of coding in R.
Accessibility – Almost all advanced features need licenses for their new products on SAS, increasing the cost and accessibility. Whereas R allows to access or upgrade to the advanced features easily.
Graphical Capabilities – SAS has basic graphical capabilities, but it is only functional. With reference to this factor, R has the best graphical capabilities when compared with the SAS.
Let us further understand the Description of the Tools –
SAS – SAS is considered to be the leader in the data analytics field, it is an integrated software solution. This software also has a lot of good features like GUI and excellent technical support. It is generally used to perform tasks such as data entry, retrieval and management, for report writing, to conduct statistical and mathematical analyses, for research in operations and project management. SAS is one of the oldest and trusted programming tool used by big global corporate especially in the field of finance. Some reputed companies that use SAS are Barclays, HSBC, PNB Paribas, Nestle etc…,
R – R is a programming tool for statistical computing and graphics, it offers a wide range of techniques. Since it is an open source tool, it is highly extensible. It is a simple and effective programming language and it is more than just a statistic system. It is generally used to perform tasks such as visualise data, Machine Learning etc…, R is also used by reputed companies but is usually popular with startups and mid-sized organisations.
So to conclude if one has a goal to become a business analyst professional and is planning to join a bank or the financial services where the company is using SAS and might want to fund the course or will partially fund the learning, then you should take up SAS and maybe later learn R once comfortable with SAS. Remember learning SAS programming course might be fairly easy, but is very expensive and if one wants to join a start-up where SAS is not used, then to have the skill is of little use. In such a scenario it is better to learn R, also it is advisable to learn R if you have a statistic or a programming background.
Having knowledge in R and SAS is imperative if you want to excel in the profession of Data Science.