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Note: Please use your full real names where signing up, otherwise we have problems with building security.

Main speakers:

Vlad Kazantsev and Katya Nerush on Clean Code in Jupyter notebook:
Some of the most horrible code I wrote was in Jupyter notebook, because "this is not used in production". But this is fundamentally wrong. I will to review some fundamentals of clean code and how it is applicable to research notebooks and data products. Using those principles will make your Jupyter notebooks more reproducable, re-usable, correct and will speed up moving prototypes to production.

Dimitris Loukas on Learning Scrapy: How to write a book about your favourite Python framework
How much code do you need to write to make a book that is both easy to read and where every example runs fine now and in the future? Writing a book is creating a product. A properly engineered book will work hard for you delivering excellent learning experiences to people all over the world for years after its release. For some people, writing a book might be one of the best and most impactful ways to contribute to their favourite open source projects. For communities, supporting authors and helping them get their books right might be a brilliant investment.

In this presentation, I will share my experience writing "Learning Scrapy", shed some light on the process and hopefully inspire you to get more involved on the writing initiatives of the projects you support.

Lightning talk speakers:

Lev Konstantinovskiy on America's Next Topic Model or why is my hair blue:

Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model. I will also explain why my hair is blue.

Steve Holden on Python Equivalences:

This lightning talk will detail some common language invariants such as x.method() == x.__class__.method(x) that it can be handy to know to understand how the Python interpreter works.

Logistics:

Doors open at 6.30 (get there early as you have to sign-in via AHL's security), talks start at 7pm, beers from 9pm in the bar. We normally have > 200 folk in the room so there's plenty of people to discuss data science questions with!

Please unRSVP if you realise you can't make it. We're limited by building security on 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. See you on the 5th!