Data science is a huge concept, just like it is with the tools it offers. As a matter of fact, the tools that allow data analysis is pretty limited when it comes to market shares, although these tools provide a ton of utility and functionality for the user.
In general, data science involves several sub-concepts that make it what is popularly known as ‘data science’. It involves data wrangling, collecting raw data, ‘cleaning up’ data, readying it up for further processes, analyzing it (which in itself involves coding and designing functions), releasing the results and finally, preparing it to be reproduced for all to see. It is indeed one of the most intuitive technical fields and the best thing is, data science has allowed itself to become an applied field in almost all walks of life, starting from simple theoretical statistics, to almost unimaginable concepts like analyzing the number of financial transactions that are recorded daily (this figures goes in zillions).
In light of this, data science has, ever since its inception or its application in modern day computing, been dominated by at least three tools we know and use today daily. Similar to Open Office, which is actually a free version of Microsoft Office, occupying considerable space in the office market, data science is practiced through three tools – SAS, R language and Python. All these tools are programming languages, and these are used to execute code that is manually written and performs several statistical and data related output. We shall compare some of the features provided by these three tools in brief below.
Python is a old language, developed way back in 1991 by a software engineer and is still used as a very popular coding language. It was developed keeping in mind ease of use, and is still one of the simplest programming languages, used by several developers, engineers and coders to create software. A checklist number of software in the market have been created by this language, and recently has gained a lot of attention for it to be used in data science. Due to its ease, Python has developed several libraries – NumPy, SciPy, Matplotlib to name just a few, that allow for complete statistical functionality. Its swiftness, CPU efficiency and ease of use, indeed are its biggest points when it comes comparison.
Another tool that still occupies the largest market share, is SAS, built as a standalone statistical language in the 1990s and is still evolving today. SAS is a purely statistical tool, unlike Python, which is a general purpose language that in recent times, has evolved to include statistical functionality as well. SAS however, is even easier to learn and provides for some intuitive GUI, absent in other statistical languages, although it misses out when it comes to intuitiveness and powerful processing. It also has a very heavy price tag, a thing that has allowed it to lose more than half of its market share as it was during early 2000s. Unlike Python however, SAS has a brilliant customer service and has released a freeware too that can be well be used for everyday activities.
The last one, which is similar to SAS and Python too, is the R language. R language is by far, been considered the standard ‘lingua franca’ of data analysis. It was like SAS, designed by statisticians, to be used only by statisticians, and has absolutely no horizon when it comes to software design, like Python is. R language however, is currently the most popular tool for conducting data science, owing to zero cost, powerful functionality, flexibility and intuitiveness. Its core design has even allowed for Microsoft to acquire it, and the company is now helping it evolve further, at least when it comes to the filed of big data. R language, which was actually designed to be used in academics, has become the primary language in almost all of the corporate sector. It however has its own downsides, some being its steep learning curve and somewhat lack of simplicity, but these get neutralized by its capabilities to reproduce unchallenged results and not to forget, its active community of millions of users, ready to help out.
All in all, it is basically the individual preference for the data scientist that matters here since all three are great tools to use daily.