Python vs R vs SAS - Which is the best Tool in Big Data?

Comparison

R on the other hand is probably the least intuitive of the three; it’s a low-level coding language that requires a strong working knowledge of code, where even relatively simple statistical procedures require extensive lines. It is generally the preserve of research departments and statistician. Python sits squarely in the middle from an ease of use point of view. It’s without a doubt the most widely used language of the three, but this is because of its use in software engineering and web development - a high level language, it’s easy to pick up and use, but falls short of SAS GUI ease of use.

It’s worth mentioning here that one of the real strengths of Python and R, is that they are open source languages. There are some limitations, which we’ll touch on, but the open source aspect of these languages - Python in particular - is that there seems to be a package for nearly everything, and this ecosystem of packages has developed and continues to develop at an incredible rate, as data science matures.

On the data visualisation fronts for example, Python has matplotlib, seaborn and vispy which are powerful tools for graphical analysis. R has ggplot, lattice, ggvis amongst others. We’d argue that R provides the best out of the box data visualisation capability, and a combo we’ve often come across is Python used for data manipulation, and R used for the data vis.

Open-source ecosystems improve the breadth and capability of these languages, which often threatens to leave SAS a number of releases behind. Recently, in a bid to catch up with the AI and ML toolsets offered by Python and R, SAS pledged to invest $1 billion toward AI over the next three years. TensorFlow and Keras are now hugely popular Python libraries which provide deep learning capabilities, and KerasR has been released as an interface to the original Python package. It really isn’t an understatement to say that Python has become a phenomenon in the field of data analytics.

As we mentioned though, there are certain upsides to the enterprise offering of SAS. Whilst there might be developmental lags, SAS makes up for it with all round quality of the provision of its product. SAS practitioners have access to incredibly rigorous documentation and can count on 24/7 support for customer queries. There is also consistency across the toolset, whereas Python and R might have multiple competing packages or libraries, which might output different statistical results given the same input.

This consistency and support are incredibly important for larger corporates, in the insurance or finance industries, for example, who rely on predictable and unified analysis in order to make fully auditable decisions. Users of R and Python, on the other hand, tend to have to rely on Google or active communities to field queries!