I am a civil engineer and social scientist with a PhD in business. For my day job, I manage the data science team at a water utility in regional Australia. My responsibiity is to “create value with useful, sound and aesthetic data products”.
My approach to data science is practical as I solve real-life problems. Data science needs to be useful, sound and aesthetic to be able to create value.
Useful means that somebody can make a decision that increases value for an individual, an organisation or society overall. Sound means that the analysis is valid, reliable and reproducible. Last but not least, data products need to be aesthetic so that they are easy to understand.
Euler Problem 32 returns to pandigital numbers, which are numbers that contain one of each digit. Like so many of the Euler Problems, these numbers serve no practical purpose whatsoever, other than so...Read More
The netCDF format is popular in sciences that analyse sequential spatial data. It is a self-describing, machine-independent data format for creating, accessing and sharing array-oriented information. ...Read More
A solution in R for Euler Problem 30: Digit fifth powers. Find the sum of all the numbers that can be written as the sum of fifth powers of their digits. Numberphile has a nice video about a trick to ...Read More
A geographic bubble chart is a straightforward method to visualise quantitative information with a geospatial relationship. Last week I was in Vietnam helping the Phú Thọ Water Supply Joint Stock C...Read More
This article describes how to use Virtual tags to analyse SCADA data. Virtual tags provide context o SCADA or Historian data by combining information from various tags with meta data about these tags....Read More