Treffer: A Pedagogical Model for Teaching Data Analytics in an Introductory Information Systems Python Course.

In this paper we answer the call of Sheppard (2012) and Brunner & Kim (2016) and present a model for teaching data analytics in an introductory information systems class using the Python programming language. The pedagogy follows an active-learning strategy in which students are assumed to have no statistical or Python programming training prior to class. The learning outcomes include: 1) Data: write code to import and manipulate data; 2) Visualization: write code to generate useful and theoretically sound data visualizations; 3) Feature Engineering: write code to generate, condense, or recombine variables (i.e., "features") of any type (numeric, categorical, ordinal, text) to provide the best possible predictive performance; and 4) Prediction: write code to estimate the effect/weight of a set of feature variables on a label variable. The course structure is detailed and student evaluations are presented. [ABSTRACT FROM AUTHOR]

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