The Proliferation of Data Science Programming Languages: Explanatory Study Technological Development, and Common Features

Data science or big data has become a major field of study at different universities. Numerous courses and different programs have been introduced to prepare students for this field. At the same time, newer programming languages have emerged and are being taught to respond to market demand in the same field. To many, conversations about programming languages often lead to a discussion about general-purpose programming languages like Java, C++, Python, and other similar languages. However, some of the newer programming languages are developed for special purposes and are called special-purpose programming languages. Among those are languages developed for data science purposes, named “data science programming languages” and have features distinguishing them from general-purpose programming languages. This paper discusses the expansion and proliferation of programming languages that have been developed for data science. The paper gives background information on the technological development that led to the growth and expansion of data science languages. It then explains the common features among these languages that distinguish them from general-purpose programming languages. The paper is intended for professionals experienced in working with general-purpose programming languages and may not have working knowledge of Data Science Programming Languages. It helps explain the main features that Data Science programming language offers, which may not be readily available in earlier programming languages