Data Science Rosetta Stone: A Tutorial of and Translation between Data Science Programming Languages
Elaine Kearney · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2019
Data Science and Machine Learning enable researchers both in academic and industrial fields to analyze data and consequently make important decisions as well as predictions. Different programming languages are used in the pursuit of Data Science. However, it is difficult for researchers to switch between languages or learn a new language which is needed in working across interdisciplinary fields and when working with colleagues from diverse backgrounds. This paper discusses a resource created to demonstrate the commonalities between four different programming languages commonly used in Data Science: MATLAB, Python, R, and SAS. The work for this project came out of research completed both at the Australia New Zealand Banking Group (ANZ) headquarters in Melbourne, Australia, and at the University of North Carolina at Chapel Hill (UNC-CH). This research resulted in an online set of tutorials, which we refer to as the Data Science Rosetta Stone, which demonstrates common data science tasks in the same progression for each programming language. We demonstrate that all of these languages achieve similar results, though with different syntax and/or simplicity of code, and efficiency of execution.