Understanding cancer behaviour with F#
Please log in to watch this conference skillscast.
In this talk I'll look at data science tools in F# and how they simplify the life of a modern scientist, who heavily relies on data analytics. F# provides a unique way of integrating external data sources and tools into a single environment. This means that you can seamlessly access not only data, but also R statistical and visualization packages, all from a single environment. Compile-time static checking and rich interactive tooling gives you many of the standard tools known from software engineering, while keeping the explorative nature of simple scripting languages.
Using examples from my own research in bioinformatics, I'll show how to use F# for data analysis using various type providers and other tools available in F#.
YOU MAY ALSO LIKE:
- Keynote: The Bottom of the Iceberg: Enabling Data Science Research with F# (SkillsCast recorded in April 2019)
- Functional Concurrency in .NET with C# and F# with Riccardo Terrell (Online Course on 7th - 10th December 2021)
- ScalaCon 2021: November Edition (Online Conference on 2nd - 5th November 2021)
- Haskell eXchange 2021: Novice Track (Online Conference on 15th November 2021)
- Accessibility Testing: Why and How to involve People with Disabilities (Online Meetup on 28th October 2021)
- Using F# to Structure an Island of Simplicity (SkillsCast recorded in October 2021)
- Adopting F# on a Consultancy Project: From Zero to MVP to V0 Launch (SkillsCast recorded in October 2021)
Thank you to our sponsors and partners
Platinum
Silver
Understanding cancer behaviour with F#
Evelina Gabasova
Evelina is a Senior Research Data Scientist at The Alan Turing Institute, the UK's national centre for data science and artificial intelligence. She is passionate about making data science understandable and accessible to everyone.
She originally started as a programmer but got interested in machine learning early on and did a mathematics PhD at the University of Cambridge. During her PhD, she worked on Bayesian models for unsupervised learning that integrate heterogeneous biomedical datasets. After that, she worked in cancer research at the MRC Cancer unit in Cambridge, where she focused on helping biologists analyse genomic data.