Plate notation is a useful visual method for describing graphical models, but the software can be awkward. Here we demonstrate daft-pgm, a solution using pure Python.
Posts in data-science
It's 2016 and most financial services companies are at least starting to implement a data science capability, here's nine questions to define the maturity of yours.
Several years into the fintech revolution, the insurance world is waking up to the disruptive possibilities of new technologies. So what's hype and what's actually useful?
Over the past year we've refined this simple model to help map, evaluate and improve our clients' data science capabilities, it might work for you too.
To follow our post on technical user groups, here's a hat-tip to meetups & conferences throughout UK, Ireland and Europe that we enjoyed attending in 2015.
In this technical article we explain why and how to use Singular Value Decomposition (SVD) for feature reduction: making large datasets more compact whilst preserving information.
Data science doesn't just lead to insights and products: here we define SPEACS, a generalised analytical process that highlights the many business benefits at every stage.
Converting postal addresses into geospatial lat/lon coordinates - aka geocoding - is cheaper and more accessible than you might imagine, and enables powerful statistical analyses.
We're recently back from R in Insurance in Amsterdam where we heard several interesting talks, delivered one of our own, and met some really great people.
Like any collaborative business effort involving research & development, a data science function should be built carefully in order to enable the best expertise and technologies.
We're delighted to attend and support R in Insurance this year: a leading international conference for researchers and practitioners of actuarial science and financial data analysis.
Science is all about making & testing models to describe the real world, and it's always important to remember that the map is not the territory.
There are huge opportunities available to the life insurance industry to apply new statistical modelling techniques, recalling their original role in helping to advance data analysis.
The practicing data scientist will be familiar with a wide range of software for scientific programming, data acquisition, storage & carpentry, lightweight application development, and visualisation.
The term 'data science' has been around now for about five years with many explanations, discussions and occasional breathless over-excitement in the technology and business press.
In the summer of 2013, Jon Sedar and Michael Crawford got chatting in the pub after Hadley Wickham's great lecture at the Dublin R user group.