Introduction
As part of Theory and Method Challenge Fortnights 2024, the Turing welcomes a team of 12 experts from across the UK and beyond to collaborate and tackle foundational challenges in data science and AI. Join us on Thursday 20 June at Kings College London for a public lecture from international experts and team members Andrew Gelman, Jessica Hullman and Hadley Wickham.
Further information about Theory and Method Challenge Fortnights (TMCF)
About the event
The steady expansion in the availability and reach of observational data has prompted much-needed introspection into data analysis practice. When using data to answer questions, it is not simply a case of choosing from a set of appropriate statistical procedures or "rolling out" some research design template. Effective data analysis requires analysts to make decisions within a wide space of analysis options and to engage deeply with the processes and mechanisms being represented through data. This public lecture features three internationally-standout scientists from academia and industry.
Talks will cover how to challenge and interrogate in data-based research, techniques for imagining uncertainty and variation in observational data, and how data-driven analyses can be put into production.
If you'd like to request any reasonable adjustments or have any questions about this event please contact the Academic Engagement team.
Talks
'Beyond the black box: Toward a new paradigm of statistics in science'
Standard paradigms for data-based decision making and policy analysis fail and have led to a replication crisis in science - because they can't handle uncertainty and variation and don't seriously engage with the quality of evidence. We discuss how this has happened, touching on the piranha problem, the butterfly effect, the magic number 16, the one-way-street fallacy, the backpack fallacy, the Edlin factor, Clarke's law, the analysts's paradox, and the greatest trick the default ever pulled. We then discuss ways to go beyond the push-a-button, take-a-pill model to a more active engagement of data in science.
'Data Analysis as Imagination'
Learning from data, whether in exploratory or confirmatory analysis settings, requires one to reason about the likelihood of many competing explanations. However, people are boundedly rational agents who often engage in pattern-finding at the expense of recognising uncertainty or considering potential sources of heterogeneity and variation in the effects they seek to discover. Taking this seriously motivates new classes of interface tools that help people extend their imagination in hypothesising and interpreting effects.
'Data science in production'
This talk will discuss what it means to put data science "in production". In industry, any successful data science project will be run repeatedly for months or years, typically on a server that can't be worked with interactively. This poses an entirely new set of challenges that won't be encountered in university classes, but that are vital to overcome if you want to have an impact in your job.
In this talk, Hadley discusses three principles useful for understanding data science in production: not just once, not just one computer, and not just alone. Hadley discusses the challenges associated with each and, where possible, what solutions (both technical and sociological) are currently available.
