Programming Languages for Data Science

Careers in data science are the most promising in the coming decades. Data scientists are expected to be knowledgeable in at least one programming language. There are more than 256 programming languages available today. Each language has its capabilities, some languages work better for building games, while others work better for software engineering, and others work better for data science.

Python is the most dominant data science programming language because of its ease of use,  simple syntax, and enormous libraries that deal with data science applications, making it easy to adapt for people who do not have an engineering background. Python is also a diverse programming language with many capabilities outside of data science. However, it hardly holds a monopoly in data science. Other languages such as R, Scala, Java, and SQL are heavily used. This opens the debate of whether a single language can be enough to solve all data science problems or data scientists must be knowledgeable in many programming languages. 

In this discussion activity, please reflect on your perspectives on whether a data scientist should be knowledgeable in many programming languages or not and which language(s) s/he must master and why?

Please do your best to make your reflections a learning opportunity for others in the class. While your responses will be subjective in nature, write them in a manner that helps others make connections to their experiences.  You can refer to these sources for additional information are attached