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SkillsCast

Understanding cancer behaviour with F#

17th April 2015 in London at Skills Matter

There are 11 other SkillsCasts available from F# eXchange 2015 - the conference on 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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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.

SkillsCast

Please log in to watch this conference skillscast.

Https s3.amazonaws.com prod.tracker2 resource 41088130 skillsmatter conference skillscast o9nohu

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:

Thanks to our sponsors

About the Speaker

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.

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