Embedding Data Analysis into the Undergraduate Actuarial Science Curriculum

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

Evaluating the Accuracy of Bluetooth-Based Travel Time on Arterial Roads: A Case Study of Perth, Western Australia

Bluetooth (BT) time-stamped media access control (MAC) address data have been used for traffic studies worldwide. Although Bluetooth (BT) technology has been widely recognised as an effective, low-cost traffic data source in freeway traffic contexts, it is still unclear whether BT technology can provide accurate travel time (TT) information in complex urban traffic environments. Therefore, this empirical study aims to systematically evaluate the accuracy of BT travel time estimates in urban arterial contexts. There are two major hurdles to deriving accurate TT information for arterial roads: the multiple detection problem and noise in BT estimates. To date, they have not been fully investigated, nor have well-accepted solutions been found. Using approximately two million records of BT time-stamped MAC address data from twenty weekdays, this study uses five different BT TT-matching methods to investigate and quantify the impact of multiple detection problems and the noise in BT TT estimates on the accuracy of average BT travel times. Our work shows that accurate Bluetooth-based travel time information on signalised arterial roads can be derived if an appropriate matching method can be selected to smooth out the remaining noise in the filtered travel time estimates. Overall, average-to-average and last-to-last matching methods are best for long (>1 km) and short (≤1 km) signalised arterial road segments, respectively. Furthermore, our results show that the differences between BT and ground truth average TTs or speeds are systematic, and adding a calibration is a pragmatic method to correct inaccurate BT average TTs or speeds. The results of this research can help researchers and road operators to better understand BT technology for TT analysis and consequently to optimise the deployment location and configuration of BT MAC address scanners

INTERDISCIPLINARY, INDUSTRY-BASED WORK-INTEGRATED LEARNING (WIL): TRANSFORMATIVE EDUCATIONAL EXPERIENCES FOR STUDENTS, TEACHERS AND INDUSTRY PARTNERS

The evolving workplace demands a shift from discipline-specific knowledge to capabilities that are transferrable across diverse contexts. Working in interdisciplinary teams, where students are active partners in shaping their learning through engaging in collaborative problem-solving enables profound personal and professional development. The challenge the students were presented with arose from a collaboration with Lab Tests Online Australasia (LTOAU). The industry partner requested assistance with improving web-design, assessing health literacy of online content, user engagement strategy and optimised use of Google analytics. The Curtin University team comprised students and academic supervisors from science, commerce, and media communications. LTOAU actively supported students to scope and define the problem, providing iterative feedback as students progressed. Solving real-world problems in interdisciplinary teams inspired students to take ownership of their learning, consider multiple perspectives, and establish a shared learning culture within a diverse team. All stakeholders described positive, immediate and long-term outcomes from the intense collaboration that took place over the duration of the project. The process for designing and implementing interdisciplinary project-based WIL in partnership with industry will be presented. The industry partner and students will outline personal development the interactive experience enabled. This presentation will highlight transformational learning afforded though interdisciplinary problem-solving

Some invariance properties of the minimum noise fraction transform

The Minimum Noise Fraction (MNF) transform is widely used in the remote sensing and image processing communities, because it is usually better than the Principal Components (PC) transform at compressing and ordering multispectral and hyperspectral images in terms of image “quality”. The MNF transform is also invariant to invertible (i.e. non-singular) linear transformations of multispectral/hyperspectral data, a property not shared by the PC transform. This general invariance property of the MNF transform is proved. Three examples of the general invariance property are provided and discussed: (i) invariance to scaling, (ii) invariance to certain types of background correction, and (iii) invariance to different types of noise

Statistical downscaling of rainfall data using sparse variable selection methods

In many statistical downscaling methods, atmospheric variables are chosen by using a combination of expert knowledge with empirical measures such as correlations and partial correlations. In this short communication, we describe the use of a fast, sparse variable selection method, known as RaVE, for selecting atmospheric predictors, and illustrate its use on rainfall occurrence at stations in South Australia. We show that RaVE generates parsimonious models that are both sensible and interpretable, and whose results compare favourably to those obtained by a non-homogeneous hidden Markov model (Hughes et al., 1999). © 2011

Predicting stillbirth using LASSO with structured penalties

Using a structured fusion penalty in regression models containing only categorical explanatory variables yields patterns of indicator variables that are simpler and more easily interpretable than regressions produced using the more commonly-used LASSO and group LASSO penalties. We construct logistic regression models for predicting stillbirth from categorical explanatory variables and demonstrate that using a structured fusion penalty produces regressions that are easier to interpret yet yield similar predictive ability