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Statistics is concerned with the process of making sense out of data. It is the study of uncertainty and is concerned with the process of decision making in the face of uncertainty. As our ability to collect, accumulate and access data increases so does the Volume (amount), Variety (of types, sources and resolutions of data), Velocity (speed of data generation and handling) and Veracity (amount of noise and processing errors) of the data sets we wish to analyse and extract valuable information from. Variety creates wide or high-dimensional data sets that may require specific analytic approaches in order to distinguish useful patterns or develop predictive models for decision making.

This course covers some of the statistical concepts and methodologies appropriate for the analysis of large and/or high dimensional data sets. Students will learn the mathematical foundation of a number of statistical methods, the benefit and limitations of each method, how to correctly apply these methods using statistical software and how to assess the effectiveness of given analyses for given data sets. Students will also learn how to perform statistical analyses in the statistical software R. This will require students to master the writing of R code.

On successful completion of this course students should be able to:

1. Demonstrate integrated understanding of high-dimensional data sets.
2. Apply the knowledge of high-dimensional data sets in the evaluation and choice of appropriate statistical methods.
3. Apply the knowledge of a range of computational methods and diagnostic techniques to test hypotheses and evaluate and interpret the output correctly and in context.
4. Effectively implement R software to perform analysis using different statistical methods.
5. Synthesise and interpret analyses and communicate the results of analyses to a diverse audience to aid decision making.