After a long hiatus, we are happy to announce the 34th PyData Cambridge meetup!

This is our first in person meetup since the pandemic started and we are happy to welcome everyone back! To kickoff the meetup once again we need your help. We need speakers, organisers, sponsors and of course, attendees.

Starting from September onwards we will need organisers to help with our meetup. Our code of conduct has always reflected the diversity we want in our meetup. To continue this and improve our meetup this has to start by changing things at the top. So we especially would like to welcome organisers coming from a more diverse background.

Please keep in touch with us on meetup or our email
: pydatacambridge@gmail.com

Many thanks to Raspberry Pi, who help us host the meetup.

Agenda

18:45 - Doors open
19:00 - Introduction
19:10 - "Lessons for Building Effective Data Science Teams" (talk) by Aidan Russell, Infogrid
19:55 - Interval / snacks provided
20:15 - " Intro to SOLID Principles with Python" by Federico Garza de Leon, Infogrid / PyData Cambridge Organiser
21:00 - End (Pub - Old Ticket Office, Station Square)

Code of Conduct

PyData is dedicated to providing a harassment-free event experience for everyone, regardless of gender, sexual orientation, gender identity, and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of participants in any form.

The PyData Code of Conduct governs this meetup. ( http://pydata.org/code-of-conduct.html ) To discuss any issues or concerns relating to the code of conduct or the behaviour of anyone at a PyData meetup, please contact NumFOCUS Executive Director Leah Silen (leah@numfocus.org) or organizers.

Talks

**Lessons for Building Effective Data Science Teams
**By Aidan Russell

Abstract
The effectiveness of Data Science teams in their ability to ship products and drive revenue for their company varies a lot across the industry, much more than other software roles. In this talk I will explore how the structure and attitudes starting from the hiring process through to team structures, project prioritisations, and interactions with other company functions can lead to dramatically different outcomes depending on how they are handled. I will present my own strongly opinionated view on the right way to build an effective Data Science team in the hope that poor outcomes for Data Scientists across the industry can be mitigated if we all agree to do things differently.

Bio
Aidan Russell has been the Head of Data Science at Infogrid for a little over 2 years. During that time the company as a whole has grown from 12 to 260 and the Data Science team from 1 to 15 (and continuing to grow). He holds a PhD in physics and has worked in data science since 2015 across a range of companies from early stage startups to 100 year old corporates (and picked up some strong opinions on the best way to run things along the way).

**Intro to SOLID principles with Python
**By Federico Garza de Leon

Abstract
SOLID is an acronym for five object-oriented design principles that can help your code development and quality. You’ve probably heard of them and you might have even read about the ideas at the core of these principles. Their actual meaning and application on a day-to-day base when developing might be less clear.
As a Data Scientist, working with Python, I have looked at how to apply the SOLID principles when developing Python. I hope to give you insight in why SOLID could be a part of your philosophy and toolkit as a programmer.

Bio
Federico Garza de Leon is a Senior Data Scientist at Infogrid. He holds a Masters in Nanoscience and a PhD in Condensed Matter Physics and has worked in the Data Science industry for 6 years. He is a co-founder and co-organiser of PyData Cambridge.