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Details

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

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Main speakers:

Tom Parslow (https://tomparslow.co.uk/) on Web based interactive visualizations in D3.JS

D3.JS is a powerful JavaScript library for producing rich interactive data-driven visualizations for the web. While Python has incredible tools to manipulate data and can even produce charts and graphs for the web, if you want to have complete control you need to use JavaScript. As the plentiful examples at d3js.org (http://d3js.org/) and bl.ocks.org (http://bl.ocks.org/) show, D3 is capable of some pretty impressive stuff!

Nicholas Tollervey (http://ntoll.org/) on mu, a Python IDE for the BBC Micro:Bit

Mu is an editor for beginner programmers who use the BBC micro:bit. This talk explains the philosophy and design decisions we made when creating this tool. It will include a live demo. What could possibly go wrong?

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Lightning Talks:

Karolina Mosiadz (https://twitter.com/authorea) on Data-driven, interactive science with IPython Notebooks

At Authorea (http://www.authorea.com), we want to change the way scientists communicate and share their research. This includes giving all the information behind figures a place to live: by letting readers and reviewers access your data and code, your results can be easily reproduced and extended.

In this talk, we will showcase how to create beautiful interactive plots with iPython, and make your data reproducible and expandable.

Charlie Muirhead on CognitionX: A Community and Directory for All Things AI

Building a reliable reputation system for the assessment of resources (people and software / hardware) to apply AI to a business problem. In this talk we'll touch on how we'll be doing this and introduce the activities we run today - weekly events, daily news briefing and hosting expert blogs and forums on the CognitionX platform.

James Ritchie (https://twitter.com/JMSRTCH) on Writing your own scikit-learn package

Why you should think about implementing an algorithm for scikit-learn, how to go about it, and tips for avoiding all the mistakes I made!

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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 4th!