Can you compare Sas, R, and Python programming tools and describe their use in Data Analytics?
"While there are many data analytics tools available, Sas, R and Python are probably the most popular and widely used. Of these, I prefer Python. This is due to its large number of user-created libraries and modules, its ease of use, and its robustness in areas such as statistical operations and model building. R is also open-sourced but is more popular with the academic and scientific community. Sas is by far the most widely used data analytics tool and has an easy-to-use graphic interface and probably the strongest statistical functions. Sas' only drawback is its licensing cost, which can be prohibitive for smaller organizations."
This is a Technical Question that seeks to determine your technical capabilities and knowledge of the common tools used by Data Scientists. By specifying these tools, the interviewer is indicating that these are what their organization uses and expects you to be competent in. You should be able to compare these and state their purpose in analyzing data, even if you don't regularly use them.
"Sas, R, and Python are probably the most commonly used tools for data analytics. Sas has a wide array of functions, a user-friendly graphical interface, and strong reporting features. R's strength is that it is an open-sourced tool and is widely used in academic and research environments. Python is also an open-sourced product but is more widely used and supported. It is easy to learn and interfaces well with other tools. The best part about Python is its large portfolio of libraries and modules."
