EVENT - Tuesday
When6:00 PM - 9:00 PM
DSF Meetup with Auto Trader
February 5th 2019. Join Data Science Festival – Manchester in partnership with Auto Trader in February for great talks.
Those randomly selected and approved will then be e-mailed tickets for the event. If you do not receive an approval e-mail from us by the 4th of February 2019 you have been unsuccessful in getting a ticket for this event.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
Please click here to apply for a ticket: GET TICKETS
6:00pm Guests arrive
6:30pm – Dr Jane Jin
7:00pm – Rob England
7:30pm – Break for refreshments
8:00pm – Dr Andrew Crosby
8:45pm – Networking
9:00pm – Close
Dr Jane Jin – Data Scientist at Auto Trader
Summary: Customers always have choices of how to advertise their cars at our website. Now with the help of data, we learn if our services are priced fairly so we bring them better value for every pound spent.
Rob England – Data Scientist at N Brown Group
Summary: Storytelling with R : the plot thickens. We have the tools to build a wide variety of charts and graphs from our data, and with a few tweaks we can use them to tell more of the story more clearly. This talk finds inspiration from some heroes of data vis, and uses it to design some non-standard charts, in R, that bring out insights from real business data.
Bio: Rob is part of the N.Brown Data Science team. N.Brown is an online fashion retailer trading through brands including JD Williams and Simply Be. Rob’s main focus at work is developing the in-house econometric models which try to work out the long-term payback from marketing investment in TV, Press and digital advertising. He also has an interest in data vis. and has been combining this with his attempts to learn R.
Dr Andrew Crosby – Data Scientist at Auto Trader
Summary: Auto Trader is in the privileged position of having access to an unrivalled range of data relating to the UK’s automotive market, one of which is our set of vehicle images. In this talk I’ll discuss some work that we did as part of a company hack to explore how we can use image recognition models trained on this data to tackle a range of interesting problems.