  In data science education, the importance of learning to solve real-world
problems has been argued. However, there are two issues with this approach: (1)
it is very costly to prepare multiple real-world problems (using real data)
according to the learning objectives, and (2) the learner must suddenly tackle
complex real-world problems immediately after learning from a textbook using
ideal data. To solve these issues, this paper proposes data science teaching
material that uses agent-based simulation (ABS). The proposed teaching material
consists of an ABS model and an ABS story. To solve issue 1, the scenario of
the problem can be changed according to the learning objectives by setting the
appropriate parameters of the ABS model. To solve issue 2, the difficulty level
of the tasks can be adjusted by changing the description in the ABS story. We
show that, by using this teaching material, the learner can simulate the
typical tasks performed by a data scientist in a step-by-step manner (causal
inference, data understanding, hypothesis building, data collection, data
wrangling, data analysis, and hypothesis testing). The teaching material
described in this paper focuses on causal inference as the learning objectives
and infectious diseases as the model theme for ABS, but ABS is used as a model
to reproduce many types of social phenomena, and its range of expression is
extremely wide. Therefore, we expect that the proposed teaching material will
inspire the construction of teaching material for various objectives in data
science education.
