Knowledge graph data modelling with TerminusDB by Cheuk Ho Storing data in a tabular format is not always ideal. Taking advantage of strong data in knowledge graphs can make handling complex data structures possible and data visualization easier. In this talk, you will get all the basics to start modelling data by building schemas.
How you can nurture future talent to become high performing data practitioners by Lisa Carpenter The sheer amount of knowledge, skills and behaviours that you need to work in data can be dizzying! How can we make sure that as data professionals we’re still developing and that we’re helping the next generation of data professionals skill […]
Using Obsidian to Supercharge Your Research by Jacqui Read Generate new ideas by using Obsidian to organise your notes. Are your notes and research working for you or collecting dust? Find out how to connect your thinking, connect your notes, and find those new connections that spark inspiration.
Fully Automated Fact-Checking by Alex Joseph At Full Fact we have been developing technology to help increase the speed of fact-checking. Recently we have been working on a tool that can automatically fact-check claims from the UK media without any human input. This tool is capable of extracting the key information from a claim before […]
Developing WebAssembly UDFs for in-database Machine Learning by Akmal Chaudhri WebAssembly (Wasm) is a binary instruction format for a stack-based virtual machine. Wasm enables developers to use existing code libraries (e.g. C/C++) as part of their application development process. Wasm is not just for the web and today moves in exciting new directions. One primary […]
In many experiments, the unit of randomisation is not equal to the unit of analysis. A simple example is an A/B test where users are randomly assigned to either treatment or control, but the metric of interest is a session-level click-through rate. Another example is an experiment randomised at the city-level, but the metric of […]
Machine learning models output predictions based of patterns learned from data. Before we can use the data to train a machine learning algorithm, we perform extensive transformations of the variables, which are commonly referred to as feature engineering. Feature engineering includes procedures to impute missing data, encode categorical variables, transform or discretise numerical variables, put […]
With the incredible growth of machine learning over recent years has come an increasing concern about whether ML systems’ objectives truly capture their human designers’ intent: the so-called “alignment problem.” Over the last five years, these questions of both ethics and safety have moved from the margins of the field to become arguably its most […]
This session, hosted by David Loughlan, covers 4 lightning talks, followed by a Q&A panel discussion. How ASOS’s Data Science teams adapted to the new COVID world (Reda Kechouri) In this lightning talk, Reda Kechouri will tell the story on how ASOS’s data science teams responded to the COVID health crisis back in March/April. Beyond […]