• DSF Meetup with Peak.ai

    M1 4ET

    Join the Data Science Festival Manchester in partnership with Peak.ai for 2 epic talks this September. Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event on September 17th 2019, the ballot will be drawn on the 13th September 2019. Those randomly selected will then be e-mailed a Universe ticket for the event. If you get an allocated Universe ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable. PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT. Please click here to apply for a ticket: https://www.datasciencefestival.com/event/dsf-meetup-with-peak-ai/ SCHEDULE 6:00pm: Doors open 6:30pm: Tom Preece 7:15pm: Break 7:45pm: Robin Lester 8:30pm: Networking 9:00pm: Close Tom Preece - Model Interpretability: Unboxing Black-Boxes using Game Theory Summary: This talk will focus on model agnostic methods to interpret black-box models and explain why it is making predictions. Bio: Tom Preece is a Data Scientist at Peak, he has led and delivered several Data Science projects for Peak. Prior to that he has experience working in Hanoi and Shanghai, executing projects for clients across Asia and North America. Tom’s current Data Science interests revolve around machine learning interpretability and sequence modelling. Robin Lester - Training and operationalizing your ML models in Azure.​ Summary: In this talk we will take a tour of the Azure Machine Learning Service to train and deploy a machine learning model. Technologies we will cover are: - Azure Machine Learning Service - Azure Machine Learning SDK for Python - Microsoft Azure Notebooks - Azure Container Instances - AutoML - Azure Machine Learning Service Visual Interface​ Bio: Robin Lester is a Cloud Solutions Architect (CSA) at Microsoft in One Commercial Partner (OCP). Working in the industry for over 20 years, focusing on data tools such as SQL Server and AI technologies. Having worked as both a Premier Field Engineer and a CSA at Microsoft Robin has a deep technical background in everything related to Data and AI.​ Please bring your ticket on your phone or printed to this event. Address: Floor 1, NEO, Charlotte Street Manchester, M1 4ET

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  • DSF Meetup with the BBC

    M50 2LH

    Join the Data Science Festival Manchester in partnership with the BBC for 4 epic talks this July. From Predicting School Holidays Using Video Streaming Data to adapting to cope with our data science teams 300% growth, all the way to Learning from archives: how historical content can be used to engineer new content production tools. Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event on July 16th 2019, the ballot will be drawn on the 12th July 2019. Those randomly selected will then be e-mailed a Universe ticket for the event. If you get an allocated Universe ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable. PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT. Please click here to apply for a ticket: https://www.datasciencefestival.com/event/dsf-meetup-with-the-bbc/ SCHEDULE Please arrive by 6:15 PM as there will be a bag check in place for this venue. 6:00pm: Doors open 6:30pm: Dr Matt Crooks 6:50pm: Heather Mille​r 7:15pm: Refreshments 7:45pm: Adam Dicken 8:10pm: Tamsin Nooney​ 8:30pm: Networking 9:00pm: Close Dr Matt Crooks - Predicting School Holidays Using Video Streaming Data Summary: Matt will show how we can leverage user behaviour through various clustering and classification techniques in almost real time to deliver better recommendations to our users. Heather Mille​r - Experimentation in the BBC: Statistics For The Internet Age​ Summary: This talk outlines how we test at scale across the BBC, including the tools we utilise along with the type of content we test on a daily basis. The final part of this talk outlines some of the statistical challenges with testing at scale in real time, and some of the solutions we use.​ Adam Dickens - Best Practice as Code: How we are adapting to cope with our data science teams 300% growth Summary: Adam will present several ways we are utilising best practices from software engineering in our data science team, including; - Encouraging best practice with templates (packaging using Docker / virtualenv, testing, project structure, etc) - DRY data science - Making commonly used routines available as a library - Making automation easy Tamsin Nooney​ – Learning from archives: how historical content can be used to engineer new content production tools. Summary: There are now many tools to enhance video archives with extended metadata such as shot boundaries / types, face locations / identities etc. Here we will present examples of how large quantities of historical metadata can be used to develop new tools that would have been prohibitively difficult previously. In particular we will present work on Ed a system which we have developed to automate the coverage of live events with a multi-camera setup. We will show several examples of how the system has learnt how to frame, choose shot type and time changes by examining features in historical video such as face location, shot type, shot timing and audio features.

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  • DSF Meetup with Dentsu Aegis Network

    117-119 Portland St

    Join Data Science Festival Manchester in partnership with Dentsu Aegis Network in June for two great speakers. Badri Srinivasan - Head of Platform Engineering at Data2Decisions​ and Dr Ben Wright - Head of Data Science, Dentsu Data Labs at Dentsu Aegis Network will offer two talks. Registering here enters you for a ticket for the Data Science Festival Event at Dentsu Aegis Network on June 11th 2019, the ballot will be drawn from the 6th June 2019, we have space and tickets are live until the day before the event . Those selected will then be e-mailed a Universe ticket for the event, with the joining details. If you get an allocated Universe ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable. PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT. Please click here to apply for a ticket: https://www.datasciencefestival.com/event/dsf-meetup-with-dentsu/ SCHEDULE 6:00pm - Doors open 6:30pm - Badri Srinivasan 7:15pm - Refreshments 7:45pm - Dr Ben Wright 8:30pm - Networking 9:00pm - Close Badri Srinivasan - Head of Platform Engineering at Data2Decisions Summary: Serverless – the future infrastructure Bio: Experienced engineering & technology director with 10 plus years of excellent career records and managing business operations and promoting organizational growth. Extensive experience leading research and engineering teams and motivating them for performance. Holds strong proficiency in client management and public speaking. Result-oriented professional with abilities to adapt to new technology and overcome problems Dr Ben Wright - Head of Data Science, Dentsu Data Labs at Dentsu Aegis Network Summary: How to lend £1.5million using Gradient Boosting. Credit decisions are traditionally made using financial scorecards which use the old fashioned method of Logistic Regression to calculate the risk of somebody paying back a loan. A more modern approach using machine learning was used for a lender in the car finance sector with some rather impressive results. Bio: Head of Data Science Ben graduated with a degree in Mathematics from Manchester University in 2006 and obtained a PhD in Pure Mathematics in 2010. He spent 7 years with Innovative Technology, designing machine learning algorithms to distinguish between real and counterfeit coins and banknotes; this included spending time advising the Bank of England, the Federal Reserve and European Central Bank on how to produce machine readable features on banknotes. More recently, Ben has headed up the Carfinance247 Data Science team, using modern machine learning algorithms to solve real world business problems, such as how to correctly price a loan with respect to the risk appetite of the business. Ben is a keen potholer and cave diver, and a qualified member of the Cave Diving Group. Address:[masked] Portland St, Manchester M1 6ED

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  • DSF Meetup with The Co-op

    2 Federation St

    Join Data Science Festival - Manchester in partnership with The Co-op in April for an evening of Data Science for Social Good! 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 29th of March 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: https://www.datasciencefestival.com/event/dsf-meetup-with-the-co-op/ SCHEDULE: 5:30pm: Doors open, refreshments available 6:15pm - 6:20pm: Intro to Coop, Matthew Doubleday - Head of Customer Data Analytics 6:20pm - 6:35pm: Kim Cowell & Tracy Coomber 6:35pm - 6:55pm: Steve Fisher 6:55pm - 7:15pm: Lindsay Pellow & Sophie Wozmirska 7:15pm - 7:45pm: Break & Refreshments 7:45pm - 8:30pm: Chris Testa-O'neill 9:00pm: Close Kim Cowell & Tracy Coomber - Data Scientists - CCTV Steve Fisher - Head of Retail Data Science & Analytics - Retail Health Lindsay Pellow & Sophie Wozmirska - Head of Data Science & Analytics - Community Wellbeing Index Chris Testa-O'neill - Data Scientist at Microsoft

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  • DSF Meetup with Booking.com

    35 Fountain St

    Join Data Science Festival - Manchester in partnership with Booking.com in March 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 March 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: https://www.datasciencefestival.com/event/dsf-meetup-with-booking-com/ SCHEDULE 6:00pm - Doors open 6:30pm - Dima Goldenberg 7:15pm - Refreshments 7:45pm - Nick Burgoyne 8:30pm - Networking 9:00pm - Close Dima Goldenberg - Data Scientist and Team Lead at Booking.com Summary: How Data Scientists Survive Agile? Bio: Dima Goldenberg is a Data Scientist and Team Lead in the Personalization track at Booking.com. He joined Booking.com in 2017 with the establishment of the Machine Learning Development Center in Tel Aviv and worked on different customer-facing machine learning based projects such as customer retention and destinations recommendations. Dima has a master’s in Industrial Engineering from the Tel Aviv University where he specialized in Big Data and Data Science, conducting his thesis research on "Influence Maximization in Social Networks". He started his data science path as a data specialist in the IDF and expanded his professional experience in internet, intelligence and semi-conductors industries together with a vast teaching experience of data-topics within the army, academia and private initiatives. Nick Burgoyne - Data Scientist at BookingGo Summary: Lessons learned by introducing data-science to BookingGo

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  • DSF Meetup with Auto Trader

    Autotrader Manchester

    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: https://www.datasciencefestival.com/event/live/2018/dsf-meetup-with-auto-trader-2/ SCHEDULE 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.

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  • DSF Meetup with Thought Works

    ThoughtWorks

    Join us for a Peak technical talk on Forecasting, specifically around forecasting during special events. Hear examples from customers at Peak and connect with Manchester based Data Scientists. Join Data Science Festival - Manchester in partnership with ThoughtWorks in November for great talks. SCHEDULE 6:00pm - Doors open 6:30pm - Kirsty Parsons 7:15pm - Refreshments 7:45pm - Praneet Chandra 8:30pm - Networking 9:00pm - Close Kirsty Parsons - Data Scientist at Peak Summary: A look into how Forecasting is used at Peak. In particular forecasting customer orders, enabling warehouse optimization and minimising product wastage, particularly in and around events. Bio: I studied my MSc in Data Science at the University of Lancaster 2016/2017. After completing a placement at Peak last summer, I now work at Peak as a Data Scientist, predominantly focussing on Forecast and Optimisation solutions. Praneet Chandra - Head of Product at Peak Summary: In today’s world data and artificial intelligence (AI) is the key to competitive advantage and can make what’s impossible today, possible tomorrow whether its streamlining the operations or growing at scale. One major road-block that market perceives today is that current business systems are not built for building and deploying AI solutions. At Peak we have a built a World's First AI Platform as a Service(PaaS) that allows Data Scientist(s) to rapidly build and deploy AI solutions that leverage complex data and turns it into powerful actionable insights without having to figure out tough pieces like infrastructure, scalability or data acquisition. Bio: Praneet Chandra is Head of Products at Peak, He has been at Peak for more than a year where he is on a journey to create a new category AIS(Artificial Intelligence System). Prior to Peak, Praneet worked with Gainsight (Customer Success Category Creator), Mxit (World's First Mobile Chat App) and Deloitte.

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  • DSF Meetup with Auto Trader

    Autotrader Manchester

    Join Data Science Festival - Manchester in partnership with Auto Trader in October for great talks. This Meetup will give you an insight on how Auto Trader get the most out of their data. You will learn more about how they approach AB testing to deliver the best product, how their Data teams are structured how how they use R (with examples). Hope to see you there! SCHEDULE 6:00pm Guests arrive 6:30pm - Victoria Arbidans 7:00pm - Lauren Rodgers 7:30pm - Break for refreshments 8:00pm - Paul Owens 8:30pm - Networking 9:00pm - Close Victoria Arbidans - Senior Data Analyst at Auto Trader Summary: As a company, Auto Trader are not afraid of change. We are constantly testing and iterating on our advert details page to give our customers the best products and features to help sell their cars quickly, whilst creating a better online experience for our consumers. This talk will give you an idea of how Auto Trader do AB testing, the journey of where we’ve come from and where we would like to end up. You’ll see some recent examples of where R has been used to improve this method. Bio: Victoria is a senior data analyst at Auto Trader, currently using R to analyse the online buying experience. This ranges from consumer behaviour and searching patterns, to car advert performance and how it varies dependent on product and retailer type. Lauren Rodgers - Data Scientist - User Generated Content & R: tripadvisoR Summary: An insight into my Masters project which highlighted the benefits of user generated content and natural language processing for making informed business decisions. Bio: Lauren studied my undergraduate in BSc Mathematics at UCLan, followed by my MSc in Data Science at the University of Sheffield. Lauren was then employed by Ladbible Group as a junior progressing to Data Scientist for 2 years, and now she works at Peak as a Data Scientist (~9 months). Paul Owens - Senior Data Analyst at Auto Trader Summary: As a company, Auto Trader are not afraid of change. If we see an opportunity to work better and faster, then that is exactly what we will do. In the last few years, R is something that has helped us do this with more than ever before. This talk will show you the journey of where Auto Trader has come from to how we get the most out of data today. You’ll see how our data teams are structured, and some specific examples of where R has been used, like AB testing. Bio: Paul is a senior data analyst at Auto Trader, currently using R to analyse how advertising packages perform for different types of car retailers. This can range from quick and agile analysis to bigger, data science-led projects, with plenty of Shiny and Markdown thrown into the mix.

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