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Work experience is a valuable experience for all students. It can be hard for students to find work experience and sometimes challenging for them to understand the job market. Students at Innovation North have an opportunity to undertake a Placement year between L5 and L6 and this is something we wish to actively promote, support and encourage. Innovation North had a website which held the details of the work opportunities, it was very dated, contained no images and was cumbersome to search.\ud \ud The project was to create a new website for Innovation North students holding details of all work placements (short, long term, voluntary, paid). Specification for the site included search facilities, providing information and giving reflections from students who had undertaken work opportunities.\ud \ud The website has been developed by three INN students. It has been live since September 2009.

By comparison with modern parametric Bayesian statistics, practicable and robust methods for exploration and data analysis in nonparametric settings are underdeveloped. The rapid development of non-Bayesian methods and ranges of ad-hoc non-parametric tools for data mining reflect the need for a non-parametric Bayesian approach to exploring and managing data sets in even moderate dimensional problems. We address this issue by presenting multivariate Polya tree based methods for modelling multidimensional probability distributions. To address the issue of "partition dependence" -- an outstanding limitation of Polya trees and other partition-based models -- we develop Randomised Polya Trees. This new framework inherits the attractive hierarchical, multiscale structure of Polya trees but "rubberises" partition points and as a result smooths away discontinuities in predictive distributions. Some of the theoretical aspects of the new framework are developed, followed by discussion of methodological and computational issues arising in implementation. Examples of data analyses and prediction problems are provided to highlight issues of Bayesian learning in this context, and to indicate future research directions.