Suppose you want to write a data science tool to do feature engineering. Your experience may go like this: - Expectation: you can focus on state-of-the art techniques for feature engineering. - Reality: you keep having to make you codebase more complex because a new dataframe library has come out and users are demanding support for it.

Or rather, it might have gone like that in the pre-Narwhals era. Because now, you can focus on solving the problems which your tool set out to do, and let Narwhals handle the subtle differences between different kinds of dataframe inputs!

Learn Python for Data Science in this Beginners’ Day Workshop Would you like to learn to code but don’t know where to start? Taking your first steps in programming can seem like an impossible task so we’ve decided to put on a workshop to show beginners how it can be done and share our passion for the world of data science!

Apply to be a student https://forms.gle/2cvNyRK8c8pNnpnz5

Smart meters have the potential to not only provide information to individual householders about their energy consumption, but to identify patterns of usage across the entire energy system. At Nesta, we have been analysing smart meter data to uncover information about energy consumption habits, and how household appliances, physical property characteristics and demographic factors influence energy usage - as this can help develop energy-saving initiatives. In this talk we will present the data science techniques we used, such as clustering, present our results as well as discuss how we translate them to a non-data science audience, and share learnings of conducting data science work in a secure data lab to allow for analysis of sensitive and confidential data.

This talk is for leaders who want new techniques to improve their success rates. In the last 15 months I've built a private data science peer mentorship group where we discuss rebellious ideas that improve our ability to make meaningful change in organisations of all sizes.

As a leader you've no doubt had trouble defining new projects (perhaps you've been asked - "add ChatGPT!"), getting buy-in, building support, defining defensible metrics and milestones, hiring, developing your team, dealing with conflict, avoiding overload and ultimately delivering valuable projects that are adopted by the business. I'll share advice across all of these areas based on 25 years of personal experience and the topics we've discussed in my leadership community.

You'll walk away with new ideas, perspectives and references that ought to change how to work with your team and organisation.

How can engineers empower marketing teams in the post-cookie era? Discover Bayesian Media Mix Modelling (MMM), a robust data science approach to evaluate multi-channel marketing effectiveness. Learn how to implement MMM and take actionable insights back to your company.

In data science experimentation is vital, the more we can experiment, the more we can learn. However quick iteration isn't sufficient we also need to be able to easily promote these experiments to production to deliver value. This requires all the stability and reliability of any production system. John will discuss building platforms that treat iteration as a first class consideration, the role of open source libraries, and balancing trade-offs.

NLP and data science could be so easy if all of our data came as clean and plain text. But in practice, a lot of it is hidden away in PDFs, Word documents, scans and other formats that have been a nightmare to work with. In this talk, I'll present a new and modular approach for building robust document understanding systems, using state-of-the-art models and the awesome Python ecosystem. I'll show you how you can go from PDFs to structured data and even build fully custom information extraction pipelines for your specific use case.

Lots of data in the real world has missing values, but historically prevalent data science tools have had limited support for such data. This talk will compare traditional numerical approaches, the more modern alternative Arrow, as well as ArcticDB, the client-side Dataframe database developed at Man Group.

Since the end of 2022, the AI space has reached unprecedented velocity, scale and proliferation. When it seems like everyone (and their dog) is talking about AI, how should those of us who've been working in Machine Learning, Data Science (and AI) as domain experts look to navigate the conversation? In this talk, Leanne will aim to shine a light on the impact the AI arms race is having on our field, the reality of what it means to be a practitioner and some principles to stick by to help traverse what may appear to be a time of panic.

Learn how to transform your Python code into a command-line tool. Jeroen Janssens, author of Data Science at the Command Line, guides you through the process of turning your scripts into reusable, executable tools, integrating them into your data workflows and harnessing the power of the Unix command line.