John Sandall on SixFifty's Story: Why Are UK Elections So Hard To Predict?
When Theresa May announced plans on April 18th for the UK to hold a general election it was met with much cynicism. However, as self-confessed psephologists (and huge fans of Nate Silver's FiveThirtyEight datablog), we instead were thrilled at the opportunity. SixFifty is a collaboration of data scientists, software engineers, data journalists and political operatives brought together within hours of the snap general election being announced. Our goals:
• Understand why forecasting elections in the UK using open data is notoriously difficult, and to see how far good statistical practice and modern machine learning methods can take us.
• Make political and demographic data more open and accessible by showcasing and releasing cleaned versions of the datasets we're using.
• We also hope that by communicating our methodology at a non-technical level we will contribute to improving statistical literacy, especially around concepts fundamental to elections and polling.
About the speaker:
John Sandall spends his days working as a self-employed data science consultant and his nights working as a Data Science Instructor at General Assembly. Previously he was Lead Data Scientist at mobile ticketing startup YPlan, quantitative analyst at Apple, strategy/tech lead for non-profit startup STIR Education, co-founder of an ed-tech startup, and dabbled in genomics at Imperial College London.