Understanding cancer behaviour with F#
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Data science is emerging as a hot topic across many areas both in industry and academia. In my research, I'm using machine learning methods to build mathematical models for cancer cell behaviours. But using today's data science tools is hard, we waste a lot of time figuring out what format different CSV files use or what is the structure of JSON or XML files. Often, we need to switch between Python, Matlab, R and other tools to call functions that are missing elsewhere. And why are many programming languages used in data science missing tools standard in modern software engineering?
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#.
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Thanks to our sponsors
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.
Please log in to watch this conference skillscast.
Data science is emerging as a hot topic across many areas both in industry and academia. In my research, I'm using machine learning methods to build mathematical models for cancer cell behaviours. But using today's data science tools is hard, we waste a lot of time figuring out what format different CSV files use or what is the structure of JSON or XML files. Often, we need to switch between Python, Matlab, R and other tools to call functions that are missing elsewhere. And why are many programming languages used in data science missing tools standard in modern software engineering?
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# (in London on 9th - 10th September 2019)
- Modern Application Development with C# and .NET Core (in London on 16th - 19th September 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
- CloudNative London 2019 (in London on 25th - 27th September 2019)
- Keynote by Rob Sewell on Administrating SQL Server with PowerShell dbatools (in London on 19th August 2019)
- Keynote by Valentin Kasas on The Last Frontier and Beyond (in London on 20th August 2019)
- GraphQL: May the Best API Win! (SkillsCast recorded in July 2019)
- A Machine That Predicts the Global Economy in Real-Time (SkillsCast recorded in July 2019)
Thanks to our sponsors
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.