Most of my professional life has been spent in three areas: economics/finance, education and music performance. My formal qualifications are as follow: Bachelor of Arts (honours, 1st class); Bachelor of Economics; Graduate Diploma of Education; and Doctor of Philosophy (Political Science). In recent years I have tried to bring together people from diverse fields to take advantage of analytical methods. For example, I have tried to point out the benefits of computational linguistics in the analysis of poetry. The upshot of this has been the development of a method of determining the difference between how established, or “professional” poets write poetry and how “amateurs write poetry. You can find the method here. An application based on the method can be found here.
It may seem that it is not possible to combine such diverse fields as the analysis of poetry (an ostensibly subjective enterprise) with computational linguistics (an ostensibly objective field). However, the point of this site is to show that data can inform all fields of human experience.
Another field in which I have successfully combined data analysis with a field not traditionally associated with data-driven decision making is the selection of cabinet ministers. My PhD thesis deals with the selection of cabinet ministers in the Australian federal parliament. Quantitatively oriented political scientists are well aware of the methods associated with political selection using data-driven methods. But the vast majority of the discussion of political selection and politics in general, is based on discursive and subjective methods. Politicians, journalists and many academics, particularly in Australia, do not use data-based methods to inform their opinions. My approach is to address traditional questions in politics using data-driven methods rather than subjective impressions. Consider, for example, the discussion of the role of factions, geography (state/territory of candidate), gender, and house (House of Representatives/Senate) in the selection of cabinet ministers. Without data-driven methods such discussions can only be based on supposition. By using data-based methods I have demonstrated that the role of these factors in the selection of cabinet ministers is minimal. Instead, there is a very small set of biographical/psychological variables that explain the vast majority of cabinet appointments. You can find the full analysis here.
This site will contain data, discussion and analyses which emphasise that data can be used to help in the understanding of a wide range of phenomena not usually considered to be amenable to data-driven analysis. There will also be coverage of fields that are analysed using data-driven methods such as economics and finance.However, my main aim with this site is to introduce people who do not usually think in terms of numbers and equations to the benefits of data analysis.