Data management is critical if raw data is to be turned into useful information. However, the vast amounts of data available need the correct treatment if accurate conclusions are to be drawn. The converse problem must also be treated in the same way: sparse data needs judicious treatment if the information it yields is not to to be treated rashly.

I am experienced in design of experiment techniques. These factorial and centre-composite approaches allow the most parsimonious data collection, which keeps cost and time inputs to a minimum, whilst  maintaining the validity of the findings. Using these findings and applying response surface methods I also have the tools required to fully optimize executed processes.

Using traditional statistical techniques I can access data sets for trends and distributions, provide appropriate trend-fits and assess the quality of the models chosen. Using more contemporary techniques based on information entropy I have the ability to analyse data for difficult to find information, or to ensure the efficacy of the information.

Applying statistical techniques to scientific images I have the ability to maximize the utility and information held within and ensure that the conclusions drawn are astute.