DSF Day 1 - Data Science at Scale hosted by King

DSF Day 1 - Data Science at Scale hosted by King

All tickets have now been allocated to this event. If you have not received your universe ticket via e-mail you have been unlucky this time around. Please note, you will need to show ID on arrival so please have that ready with your ticket. Tickets can be digital or printed. Anyone without a ticket will not be able to enter the venue, doors for entry will close at 6:45PM. Registering on Meetup does not give you access to this event.

Join us for an evening of tech talks focused on Data Science at scale. Featuring two King speakers discussing real-world data science problems from the game analytics space.

The evening will also include Rafah El-Khatib who joins us from the advanced analytics on machine learning applications department at ING. Rafah will discuss selecting predictive features to input into a model when you are dealing with data at scale.

Please click here to apply for a ticket: https://2019.london.datasciencefestival.com/event/dsf-day-1-king/

Schedule:

6.00pm - Doors open

6.30pm- Rupali Singhal & Piergiorgio Calzi

7.15pm - Drinks food & networking

7.45pm – Rafah El-Khatib

8.30pm - Networking

9.00pm - Close

Rafah El-Khatib - Data Scientist at ING

Summary: Feature Selection Best Practices - LOFO and a Survey of Key Feature Importance Packages. Selecting predictive features to input into a model is key to ensuring that the input data is not noisy and is time-effective in cases where the original number of features or dataset are large. In this talk, I will present a survey of key feature importance packages and explain their strengths and weaknesses, and I will present an in-house open-source feature importance package called LOFO (leave-one-feature-out) and its fast approximation (FLOFO, or Fast LOFO). The LOFO importance calculates the importance of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, cross-validated, based on the chosen metric.

Piergiorgio Calzi - Data Scientist

Summary: Lesser known tips and tricks for Pandas. Any Python user performing data analysis has used Pandas at a certain point. A handful of functions can get you by and allow you to achieve surprisingly good results throughout the data preparation stage. However, and perhaps less often, we face more challenging data preparation tasks. This talk will highlight some of the less common pandas features. Not exactly a 101 but maybe a 102.

Rupali Singhal - Marketing Analyst

Summary: Data Rich Marketing at King

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 at King on April 8th 2019, the ballot will be drawn on the 1st April 2019. Those randomly selected will then be e-mailed tickets for the event, with the joining details.

If you get an allocated 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.