Data Scientists, because of the versatility and range of their skills, can suffer from the paradox of choice when it comes to choosing a career. According to Barry Schwartz, a strategy for good decision-making when there is an abundance of choices will involve these steps, executed in a way that takes careful consideration of all … More What Kind of a Data Scientist Are You?
Apparently, everything that has the word Blockchain attached to it is now news-worthy. Or at least that’s how I think this relatively small R&D collaboration between AUSTRAC and Swinburne University of Technology got a mention a couple of days ago in ZDNet, a pretty respectable technology site that has been around forever. https://www.zdnet.com/article/austrac-trialling-blockchain-to-automate-funds-transfer-instructions/ Coding the … More Legislation as Code
There is a lot of work in the data management community that looks at data quality as a first-class problem, capable of being solved largely as an independent problem that can then benefit many downstream systems. There is certainly a lot going for this line of thinking. My own view, drummed into me by a former NASA scientist who I … More The Data Quality Trap
It’s not always easy for a technically competent data scientist to make the transition to a data science leadership role. Here are some interview questions I use to assess whether a candidate has successfully made that transition. Can you tell us a little bit about yourself and why you applied for this role? What is … More Interview Questions for a Data Science Leadership Role
I am reading Robert Kuok’s autobiography and these two paragraphs from the chapter Vintage 1963 jumped out at me. “Success in futures depends on your feel for the market, your instincts and rhythm. I would talk to different brokers. Each company had bright, young English traders. One or two would be a little cunning, but, … More Robert Kuok’s Trading Algorithm from 1963
A challenge with fraud-detection problems in many cases is the lack of any meaningful collection of labelled data for supervised-learning approaches to work. Two things practitioners do to tackle the problem are statistical profiling, usually via domain-specific business rules, and statistical outlier detection, sometimes augmented with non-trivial models of what constitute “normal” behaviour. There is … More A Framework to Detect Waste and Fraud in Health Insurance
Since starting my part-time appointment as an associate professor at the Australian National University, I have been thinking about spending more time on fundamental research. As Don Knuth counsels, “if you find that you’re spending almost all your time on theory, start turning some attention to practical things; it will improve your theories. If you … More Large-Scale Distributed Analytics: A Research Program