edStat represent data statistic differently - in a visual way.
I analysed data from the Department of Education to work out what areas has the highest dropout rate and correlate with 2011 Census Community Profiles.
for example - data shows Ashburton (S) Code LGA50250 has the highest dropout rate in 2010 & 2013, representing the trend of dropouts in the community, thus worth checking out using the ABS 2011 Census Community Profiles - http://www.censusdata.abs.gov.au/census_services/getproduct/census/2011/communityprofile/LGA50250
I wanted to demostrate using opendata that integrates with the Cloud applicaition such as PowerBI can turn static data into a more meanful charts. There are a lot of improvments can be done from here onward, for example being able to identify areas which has highest dropout rates and determain if it remaind as a trend. Furthermore we are able to investigate and understand reasons behind drop-outs - for instance if its a remote area, does it have ease access to enducation, close to public transports...etc. Once we are able to know what's causing it, we should be able to improve it by injecting more resources to the area.
I have also use enrollment data(https://data.gov.au/dataset/higher-education-enrolments) to see if I can further explain in details. However, due to the limited information I was only able to pin point there are more female graduates/postgraduate than male counterpart.