EVENT - Tuesday
LocationQueen's Building, University Walk, Bristol BS8 1TR, UK
When6:00 PM - 9:00 PM
DSF Meetup with LV
Join Data Science Festival – Bristol in partnership with LV in February for 2 epic talks by 2 data enthusiasts.
Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event with LV on February 18th 2020, the ballot will be drawn on the 14th February 2020. Those randomly selected will then be e-mailed a Universe ticket for the event.
If you get an allocated Universe ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
Please click here to apply for a ticket: GET TICKETS
Richard Angell – Lead Data Scientist
Summary: The realities of data science no one talks about
Data science is massively growing across all industries but despite the buzz, many data science projects in the industry fail to make it into production. This talk will cover many of the challenges, and solutions, faced by the Data Science Team at LV = GI as they grew to become a crucial part of the business. Data science in practice is not just building trendy models; to have an impact we need to ensure that the models are used and continue to perform. Therefore we need to overcome challenges in 3 key areas; data, technology and people.
Bio: Richard Angell is the lead data scientist at LV= GI. Richard has 6+ years experience working in the insurance industry as a data scientist. Whilst at LV= GI he has been helping to lead the team as they implemented their first model to now having 10 live across the business, as well as growing the team to 20+.
Daniel Lawson – Lecturer in Data Science at University of Bristol
Summary: Data Science vs Data Industry: the competition between meaning and performance. “Data Science” is the convergence of statistics, machine learning, and computing that arose in no small part because electronic data capture creates vast datasets that can be mined for both knowledge and productivity. The academic/industry divide is smaller than ever, but there is a limit to how far it can go because the underlying goals are different. Industry, Government, and Commerce all use data to ultimately make decisions, whereas Science ultimately aims to produce knowledge and understanding. These different goals lead to different trade-offs between performance and interpretability, between black-box and model-based approaches. Much of the recent convergence of methods has revolved around phrasing understanding as prediction problems. But, some questions also need a model which aids in understanding the mechanisms underlying a process, improving causal reasoning, counterfactual prediction, and prediction of complex-systems. We discuss how training based in data science or statistics can complement each other and give examples spanning genetics to cyber security of how additional insight into meaning can improve prediction performance.
Bio: Daniel Lawson is a Senior Lecturer in Data Science in the Institute of Statistical Sciences at the University of Bristol, where he teaches a “Data Science Toolbox” in a Masters course for the Mathematics of Cyber Security. He has experience of real-world data science and machine learning in industrial by consulting for cyber security, and predictive modelling for the NHS. Alongside this, he has worked on modelling for genetics and epidemiology. His research covers the intersection of modelling and predictive approaches.
Address: Bill Brown suite, Queen’s Building on Woodland Road, University Walk, Bristol BS8 1TR.