Join Data Science Festival London, in partnership with Zopa this February. The evening will consist of talks on building Analytical Data Warehouse with Amazon Web Services and Evaluating feature importance in machine learning models using Shapley values.
Those randomly selected and approved will then be e-mailed tickets for the event. If you do not receive an approval e-mail from us by the 25th of February 2019 you have been unsuccessful in getting a ticket for this event.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
Please click here to apply for a ticket: https://www.datasciencefestival.com/event/live/2018/dsf-meetup-with-zopa/
6:00pm: Guests arrive
6:30pm: Tadas Krisciunas
7:15pm - Break & Refreshments
7:45pm - Ross Young
8:30pm - Networking
9:00pm - Close
Tadas Krisciunas - Data Scientist at Zopa
Bio: Tadas is a data scientist at Zopa who is passionate about using machine learning and technology to bring more transparency and efficiency to retail finance. Prior to Zopa, he was an early employee of the fintech start-up Oodle Finance. He holds a master’s degree in Mathematics & Philosophy from the University of Oxford.
Summary: It is often talked about “the interpretability-accuracy trade-off”: deep learning, gradient boosted trees and other powerful machine learning methods can capture complex relationships in the data, but lack transparency and interpretability when compared to more traditional methods. In this talk, I’ll briefly review a few of the most popular techniques to measure feature importance in black-box models, with a highlight on a novel class of methods stemming from the game-theoretic concept of Shapley values.
Ross Young - Data Engineer at Zopa
Bio: Ross is a data engineer developing analytical data warehouse at Zopa. He has a PhD from the University of Edinburgh in Experimental Particle Physics, a field that first spurred his interest in big data. Prior to Zopa he worked at the BAE Systems as a data analyst.
Summary: Analytical data warehouse is the system that gathers data from a wide range of sources and consolidate them to inform decision-making in large organisations. In recent year, companies are moving over from traditional, on-premise architectures to cloud-based architectures such as Amazon Web Services. This can bring a number of advantages such as cost, scalability, and performance. In this talk, I will introduce some architectural ideas that can be leveraged by an enterprise on AWS to build and monitor their entire, end-to-end ETL cycle.