The second Insurance Data Science Conference at RiskLab (ETH Zurich) followed on from its first edition at Cass Business School (London) and five iterations of the R in Insurance conferences. This one-day conference was an exciting international event that brought together industry and academia in the fields of insurance and computer sciences.
This one-day conference was an exciting international event that brought together industry and academia in the fields of insurance and computer sciences.
The 2nd Insurance Data Science conference will take place at ETH Zurich, 14 June 2019.
Following the launch in London at Cass Business School in 2018, we are inviting you to submit proposals for talks by 15 February 2019 to: [email protected]
We are looking for abstracts in the fields of insurance data science, statistical learning and machine learning. This includes technological developments, and mainly focuses on applications in insurance pricing, reserving, risk assessment and modeling, customer analytics, capital management, catastrophe and econometric modelling.
The first Insurance Data Science event was held at Cass Business School last week, 16 - 17 July 2018.
The conference followed on from five iterations of the R in Insurance events, which have the aim of bringing together practitioners and academics together to discuss and exchange ideas and needs in the sector.
Expanding the remit from R in Insurance to Insurance Data Science has also attracted talks on Python and Tensorflow.
On Thursday evening Michael Betancourt gave an insightful and thought provoking talk on Principled Bayesian Workflow at the Baysian Mixer Meetup, hosted by QuantumBlack.
Michael is an applied statistician, conslutant, co-developer of Stan and passionate educator of Bayesian modelling.
What is a principled Bayesian workflow? It turns out that it mimics my idea of the scientific method:
Create a model for the ‘small world’ of interest, i.e. the small world relevant to test an idea, e.
The abstract submission deadline for the Insurance Data Science conference at Cass Business School on 16 July 2018 is closing soon. You have until the 9th of April to submit your abstract.
Please send your abstract to [email protected]
We like to see proposals for talks that demonstrate how data science is used in insurance, e.g. in risk assessment, customer analytics, pricing, reserving, capital management, catastrophe and econometric modelling.
If you are looking for inspiration review the past R in Insurance conferences.
Last Tuesday we got together for the 4th Bayesian Mixer Meetup. Product Madness kindly hosted us at their offices in Euston Square. About 50 Bayesians came along; the biggest turn up thus far, including developers of PyMC3 (Peadar Coyle) and Stan (Michael Betancourt).
The agenda had two feature talks by Dominic Steinitz and Volodymyr Kazantsev and a lightning talk by Jon Sedar.
Dominic Steinitz: Hamiltonian and Sequential MC samplers to model ecosystemsDominic shared with us his experience of using Hamiltonian and Sequential Monte Carlo samplers to model ecosystems.
Two Bayesian Mixer meet-ups in a row. Can it get any better?
Our third ‘regular’ meeting took place at Cass Business School on 24 June. Big thanks to Pietro and Andreas, who supported us from Cass. The next day, Jon Sedar of Applied AI, managed to arrange a special summer PyMC3 event.
3rd Bayesian Mixer meet-upFirst up was Luis Usier, who talked about cross validation. Luis is a former student of Andrew Gelman, so, of course, his talk touched on Stan and the ‘loo’ (leave one out) package in R.
Last Friday the 2nd Bayesian Mixer Meetup (@BayesianMixer) took place at Cass Business School, thanks to Pietro Millossovich and Andreas Tsanakas, who helped to organise the event.
Bayesian Mixer at Cass
First up was Davide De March talking about the challenges in biochemistry experimentation, which are often characterised by complex and emerging relations among components. The very little prior knowledge about complex molecules bindings left a fertile field for a probabilistic graphical model.
We had our first successful Bayesian Mixer Meetup last Friday night at the Artillery Arms!
We expected about 15 - 20 people to turn up, when we booked the function room overlooking Bunhill Cemetery and Bayes’ grave. Now, looking at the photos taken during the evening, it seems that our prior believe was pretty good.
The event started with a talk from my side about some very basic Bayesian models, which I used a while back to get my head around the concepts in an insurance context.
There is a nice pub between Bunhill Fields and the Royal Statistical Society in London: The Artillery Arms. Clearly, the perfect place to bring people together to talk about Bayesian Statistics. Well, that’s what Jon Sedar (@jonsedar, applied.ai) and I thought. Source: http://www.artillery-arms.co.uk/Hence, we’d like to organise a Bayesian Mixer Meetup on Friday, 12 February, 19:00. We booked the upstairs function room at the Artillery Arms and if you look outside the window, you can see Thomas Bayes’ grave.
I had a great time at the R/Finance conference in Chicago last Friday/Saturday. Some brief takeaways for me were:
From Emanuel Derman’s talk: It is is important to distinguish between theories and models. Theories live in an abstract world and for a given set of axioms they can be proven right. However, models live in the real world, are build on simplifying assumptions and are only useful until experiments/data proves them wrong.
I will be speaking at the Bay Area User Group meeting tonight about Communicating Risk. Anthony Goldbloom from Kaggle and Karim Chine from ElasticR will be there as well. The meeting will be at Microsoft in Mountain View.
Later this week I will give a similar presentation at the R in Finance conference in Chicago. Please get in touch if you are around and would like to share a coffee with me.
Last Thursday I had the pleasure to attend the Tokyo R user group meeting. And what a fun meeting it was! Over 40 R users had come together in central Tokyo. Yohei Sato, who organises the meetings, allowed me to talk a little about the recent developments of the googleVis package.
Thankfully all talks were given in English: Takashi J. Ozaki presented on Visualisation of Supervised Learning with arules and arulesViz.
Today Diego and I will give our googleVis tutorial at useR!2013 in Albacete, Spain.
googleVis Tutorial at useR! 2013
We will cover:
Introduction and motivationGoogle Chart ToolsR package googleVis
Concepts of googleVisCase studiesgoogleVis on shiny
Last week I had the honour to give the opening keynote talk at the Talking Data South West
conference, organised by the Exeter Initiative for Statistics and its Applications. The event was chaired by Steve Brooks and brought together over 100 people to discuss all aspects of data: from collection and analysis through to visualisation and communication.
Building interactive relationships with data and colleagues
The programme was very good with a variety of talks such as How data collection from smart phones can improve agronomic decision making in potato crops by Robert Allen or Spatial data and analysis in the improvement of aquatic ecosystem health and drinking water quality by Nick Palling.
Building R packages is not particular hard, but it can be a bit of a daunting endeavour at the beginning, particularly if you are more of a statistician than a computer scientist or programmer. Some concepts may appear foreign or like red tape, yet many of them evolved over time for a reason. They help to stay organise, collaborate more effectively with others and write better code. So, here are my slides of the R package development workshop at Lancaster University.
Following on from last week’s post, here are my slides on using googleVis on shiny from the Advanced R workshop at Lancaster University, 21 May 2013.
googleVis on shiny
Last week I was invited to give an introduction to googleVis at Lancaster University. This time I decided to use the R package slidify for my talk. Slidify, like knitr, is built on Markdown and makes it very easy to create beautiful HTML5 presentations.
Introduction to googleVis
Separating content from layout is always a good idea. Markup languages such as TeX/LaTeX or HTML are built on this principle. Ramnath Vaidyanathan has done a fantastic job with slidify, as it is very straightforward to create presentations with R.
This evening I will talk about Dynamical systems in R with simecol at the LondonR meeting.
Thanks to the work by Thomas Petzoldt, Karsten Rinke, Karline Soetaert and R. Woodrow Setzer it is really straight forward to model and analyse dynamical systems in R with their deSolve and simecol packages.
I will give a brief overview of the functionality using a predator-prey model as an example.
This is of course a repeat of my presentation given at the Köln R user group meeting in March.
David Chan from City University is organising an interdisciplinary symposium on tackling the ‘Big Data’ challenge on 1 March 2012.
It is an open seminar trying to bring together academics and practitioners from across industry to tackle the challenges posed by “big data” - the growing amount of information that needs to be stored, searched, analysed and visualised in the digital age.
The event will take place in the Oliver Thompson Lecture Theatre, Northampton Square, London EC1V 0HB.
The London R user group met again last Wednesday at the Shooting Star pub. And it was busy. More than 80 people had turned up. Was it the free beer and food, sponsored by Mango, which attracted the folks or the speakers? Or the venue? James Long, who organises the Chicago R user group meetings and who gave gave the first talk that night, noted that to his knowledge only the London and Chicago R users would meet in a pub.
On 7 September 2011 I attended the London R user group meeting. It was a very good turn out with about 50 attendees at the Shooting Star, a pub close to Liverpool Street Station. The session started at 18:00 with four presentations, followed by drinks sponsored by Mango Solutions. The slides of the presentation are available on londonr.org.
The first presentation was given by Lisa Wainer from UCL Department of Security and Crime Science about crime data analysis using R.