Engage in advanced scholarship and contribute your own research to the existing knowledge in your field. In a research masters you can contribute to the existing knowledge in your field by defining and managing a research project alongside some of New Zealand’s leading scholars. In a taught masters you can deepen your knowledge in small classes with these leading scholars and complete a smaller research project such as a dissertation. A 120- or 240-point programme with research and taught options, depending on your chosen subject. The structure of your programme depends on the subject that you are studying. The Department of Statistics is the largest statistics department in Australasia. We provide you with a wide choice of research topics. Our research areas include biostatistics and novel regression methodologies, statistical computing, the analysis of survey data, statistical ecology and bioinformatics, forensic statistics, statistics education, experimental design and quality improvement, operations research and stochastic processes, and Bayesian statistics. Postgraduate study opportunities in Statistics You can study Statistics as a postgraduate specialisation in either the Faculty of Science or Faculty of Arts programmes. Statistics applies to almost any field, ranging from scientific research to business management and media analysis. From designing an experiment to evaluate the effects of a new treatment for a disease, to analysing a set of data gathered by an ecologist - the diversity of applications of statistics is immense. There are different options to consider when choosing your postgraduate qualification in Statistics. While the taught masters is a programme with a professional focus and essential to work as a professional statistician, the research masters is an advanced programme to develop your research skills with the intention to progress to doctoral study in Statistics. Areas of research interests open to exploration include: Analysis of survey data Astrostatistics Bayesian statistics Biostatistics and novel regression methodologies Case control sampling and extension Experimental design and quality improvement Forensic statistics Human genomics and bioinformatics Linear and non-linear statistical modelling Medical statistics Sample survey theory Statistical computing Statistical computing and the “R” statistical programming language Statistical ecology and bioinformatics Statistical genetics Statistics education Operations research and stochastic processes Non-parametric statistics