The importance of good coding practices for data scientists
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Many data science students and practitioners are reluctant to adopt good coding practices as long as the code "works". However, code standards are an important part of modern data science practice, and they play an essential role in the development of "data acumen". Good coding practices lead to more reliable code and often save more time than they cost, making them important even for beginners. We believe that principled coding practices are vital for statistics and data science. To install these practices within academic programs, it is important for instructors and programs to begin establishing these practices early, to reinforce them often, and to hold themselves to a higher standard while guiding students. We describe key aspects of coding practices (both good and bad), focusing primarily on the R language, though similar standards are applicable to other software environments. The lessons are organized into a top ten list.
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