Title: What is the best programming language for computational sciences: No need to choose, be a polyglot
Authors: Michele La Rocca - University of Salerno (Italy) [presenting]
Abstract: Early in their careers, a common question for students and data scientists is which programming language is best to learn. The question is somewhat misleading: every programming language has its strengths and weaknesses. Often, R and Python are compared with conclusions that, in some cases, point towards Python in others towards R. However, the correct answer to the question is not R *or* Python, but R *and* Python. Besides R and Python, Julia is receiving more and more attention from the data science community, again with significant strengths and some weaknesses. Especially at the beginning of their careers, computational scientists should be multilingual and learn complementary programming languages to cover future needs in different fields of application and career perspectives. The knowledge of any programming language is exposed to some degree of obsolescence. At the beginning of a career, the focus should not be on coding but rather on programming, especially on programming paradigms (OOP, functional programming, etc.) that have a higher degree of resilience.