PyData London - 70th meetup

Venue: 1 Angel Lane, EC4R 3AB

Please note:

1. A valid photo ID is required by building security. You MUST use your full real names on your meetup profile, otherwise, you will NOT make it on the guest list!
2. This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event.

Tickets are assigned through a lottery draw about 1 week before the event.

If your RSVP status says "You're going" you will be able to get in. No further confirmation required. You will NOT need to show your RSVP confirmation when signing in.

If you can no longer make it, please unRSVP as soon as you know so we can assign your place to someone on the waiting list.

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Code of Conduct:
This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event. Please get in touch with the organisers with any questions or concerns regarding the Code of Conduct.

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As always, there'll be free food & drinks, generously provided by our host, Man Group.

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Main Talks
1️⃣ Having fun with Python - why it's the ideal language to originate data for innovative viz and analytics — Ned Stratton
When did we stop saying Coronavirus and start saying COVID? How many extra Waitrose branches in Leave areas would reverse the Brexit vote? When was the calmest era in recent UK political discourse? Believe it or not, you can get data and work it to answer these questions for free.
You just need to know how to extract it from the web (screen scrape or from an API) and clean it up a bit. How do you do this? Python!
My talk will be a whistlestop tour of how Python - through its available libraries for exploratory data analysis, web scraping, working with APIs and cleaning up textual data for analysis - can help you turn your shower thoughts or wacky questions into a full-blown data viz project.

2️⃣ Transformer Models in Recommendation System — Janu Verma
Sequential recommendation aims to predict users' next action based on the sequence of the users' past interactions. Initial works focus on modelling the transition between successive items using Markov chains. Owing to the success of neural networks, recurrent neural networks have been applied to sequential recommendation with significant improvements. Recently, transformer architecture has been shown to have superior performance for sequential modelling. The transformer architecture lends itself to efficient parallelization and is effective at modelling long-range dependencies. This talk will discuss applications of transformer models to recommendation tasks, covering earlier works and modern developments in training and deployment. We will show how to build a prototype for sequential recommendation leveraging transformer models.

Lightning Talks ⚡
💰⚖️ Quantifying Risk in Financial Fraud — Elena Dulskyte
When people talk about money laundering and terrorist financing, they frequently base their observations on cherry-picked data. This talk would cover how research papers often evaluate risks and proposes a new way to have ground_truth for risk modelling. Finally, it shows how it can be applied to detect fraudulent corporate networks.

Logistics
Doors open at 6.30 pm (get there early as you have to sign-in via building security), talks start at 7 pm, drinks from 9 pm in the bar. We will have reduced capacity for this event but there will be plenty of people to discuss data science questions with!

Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!

Follow @pydatalondon (https://twitter.com/pydatalondon) for updates and early announcements.