Teaching Programming for First-Year Data Science

Abstract

This paper describes experiences in teaching Python programming as part of a large-enrolment first-year subject, that is a foundation for an undergraduate major in data science and it is also taken by a wide variety of non-majors across the arts, sciences and business fields. The paper focuses on some central design decisions about the content, sequence, approach, and tool support, and we reflect on how they have worked out and what we changed, as we have taught the subject to about 2500 students over four offerings (including two which were entirely on-line due to the pandemic). Particular aspects that are the focus in this paper include: teaching programming through a sequence of common patterns/idioms for data exploration and analysis, rather than in the language-feature focus that is traditional in programming classes; read-then-write-until-correct presentation for each pattern; explicit presentation of a notional machine for execution, in stages as more language complexity is experienced; starting with core Python before then covering use of libraries such as pandas, matplotlib and scikit-learn.