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

As always, there'll be free beer and pizza, generously provided by AHL.


Main speakers:

Daniel Moisset ( on Bridging the Gap: from Data Science to Service

Recent years have brought an explosion of algorithms, models and software libraries for doing data science that allow unprecedented possibilities for solving problems. But providing a data science service as a consultant or a company involves more than just tools. In this talk, I will share the most useful lessons that I learned while working at a company providing these services.

Ole Moeller-Nilsson ( on Finding planets with Python
Planets are a hot topic. NASA, other US institutes and European organisations have been in the news frequently with discoveries of new planets - the news of the discovery of 7 earth like planets the last weeks was difficult to miss. After a short summary of the research that is going on in this area I will talk about a Python/C project I worked on to make exo-planets visible in noisy imaging data. There are some interesting “tricks” that show the importance of understanding the data and its collection process. My goal is not just to get everyone excited about this area but also show how you can access the vast amount of publicly available astronomical data, a large part of which has never been looked at by anyone, and analyse it with Python.

Lightnings talks:

Won Bae Suh on Data Evangelism in the Python Community
The emphasis on data science and machine learning, however relevant, may be drawing resources and people away from promoting Python for its own sake. From experience, data and python evangelism goes hand to hand by promoting Python as a multi-function toolkit, rather than stressing the wonderful libraries developed by the Community. I will share my lessons from teaching Python 3.0 to students with zero programming background and tips on how we can infuse new blood into the Python Community.

Elena Nemtseva on GraphFrames - Graph queries in PySpark GraphFrames speeds up big data prototype development by allowing us to perform mixed graph and non-graph data analysis in the same pySpark code: * data wrangling (e.g. Spark SQL, user defined functions)* graph pattern queries (e.g. "(person1)-[friends of]->(person2)" )* graph algorithms (e.g. shortest path)* statistical techniques (e.g. ALS for product recommendations)

Katharine Jarmul ( on PyData Berlin 2017


Book signing!

We have several authors over this month. If you have physical copies of these books then bring them along - in the pub after we'll do a signing:

High Performance Python by Micha Gorelick and (co-org) Ian Ozsvald - Micha is over from the US to speak at QCon, we'd both be happy to sign in the pub if you bring your physical copy

Mastering Social Media Mining with Python by (co-org) Marco Bonzanini - Bring your copy to the pub!

Data Wrangling with Python by Kat Jarmul and Jacqueline Kazil - Kat is over from Berlin, bring your physical book - Kat will also speak briefly about PyDataBerlin (ask her questions if you want to attend!)



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 in good time 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 ( for updates and early announcements. See you on the 7th!