The winners of the CDRC’s 2018 Masters Research Dissertation Programme were recently announced at the Data Analysts User Group (DUG) Conference.
The CDRC-led programme provides the opportunity for students to work directly with an industrial partner and commercial datasets. This year, 20 students participated in the programme with the support of a range of partners, including Sainsbury’s, Shop Direct, Boots and M&S. Short summaries of the projects have been provided by the students and can be found here.
A panel of industry judges awarded three prizes based on the calibre and impact of the research. This year’s panel comprised Matthew Pratt (Javelin), Thomas Murphy (The Crown Estate) and Helen Parker (Tesco). The judges commented that the standard of work was very high and noted that a wide range of new techniques for handling big data were well implemented across the programme.
Lenka Hasova (University of Liverpool) and Boots
Investigation of prescription flows between General Practices and Boots pharmacies via spatial interaction modelling techniques
Lenka’s study applied a range of different model specifications to estimate and analyse the distribution of GP-Boots pharmacy prescription flows in Merseyside, North-West England. The judges commented that the analysis was thorough and logically presented. Crucially it also tackled an important study area and her methods could be used to assist and improve the provision of pharmacies.
A short summary of Lenka’s research can be found here.
Sophie de Kok (UCL) and Sainsbury’s
Predicting customer quality based on early shopping behaviour in online grocery retail
This project considered the exploration and evaluation of models for estimating the longer-term value of customers based on transaction data. The judges described Sophie’s submission as an advanced piece of analysis that was coherently presented. The research was well executed and clear benefit to the sponsor
A short summary of Sophie’s research can be found here.
Thomas Statham (University of Liverpool) and A National Broadband Provider
Forecasting network faults with Bayesian spatiotemporal statistical models
Thomas looking into means of forecasting network faults that might occur across the broadband infrastructure. This considered both time-series data provided by the sponsor and local sociodemographic factors. The judges commented that the dissertation demonstrated a thorough understanding of statistics and forecasting which were evidently well applied to tackle a complex issue in a novel way.
A short summary of Thomas’ research can be found here.
Kahina Ait Ouazzou (UCL) and Movement Strategies
Analysing Customer Behaviour for Shopping Centres in London Using GPS Data
A ballot at the DUG conference was held to award the poster prize. Kahina’s project developed a conceptual framework on how to use GPS data to capture customer behaviour for shopping centres. It considered anonymised GPS data for Greater London in 2017. Through the identification of shopping centre visits and home locations from GPS data, it was possible to analyse customer behaviour at a high spatio-temporal granularity and define the retail catchment areas.
A short summary of Kahina’s research can be found here.
Photo Credit: Jan Wright Photography