| Summary: | The recent initiative by the Actuaries Institute to incorporate a data science/analytics unit at the Honours or Masters level, as well as the changes to the undergraduate curriculum that will start in 2020, means that actuarial science educators need to consider embedding data analysis and analytics throughout the undergraduate curriculum. At Curtin, we have been trialling ways of doing just that in a coherent fashion, from first-year to third-year units, so that students see data analysis as an integral part of becoming a practicing actuary. We were motivated by:
1. The current funding reality, which dictates that units in one degree course serve as units in other courses. At Curtin, for example, we have introduced two new majors: Data Science, and Applied Statistics, both of which have data analytic and computational components, and so many units common to these courses have to do double- and even triple-duty;
2. The fact that many of our actuarial students are finding work as data analysts, and could become even more competitive in the marketplace were they to learn additional skills in computing and data analysis; and
3. The need to modernize the actuarial science curriculum, even before the changes that will take place in 2020.
In this talk, I will outline some of the ways in which we have modified tuition and assessment patterns in several units to incorporate computing, data analysis, notions of reproducible analyses, computer-based assessments, and project work. In addition, I will point out what has worked well, as well as some of the barriers we have faced in integrating components of data analytic and computational thinking and practice throughout the entire actuarial science curriculum.
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