Understanding cancer behaviour with F#
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)
- Fast Track to Machine Learning with Louis Dorard (in London on 15th - 17th July 2019)
- Functional Concurrency in .NET with C# and F# (in London on 9th - 10th September 2019)
- Infiniteconf 2019 - A one-day community celebration of Big Data, Machine Learning and AI (in London on 4th July 2019)
- FullStack London 2019 - The Conference on JavaScript, Node & Internet of Things (in London on 10th - 12th July 2019)
- London TensorFlow.js (in London on 20th June 2019)
- Hands-on: Fractal art with Fable and WebGL (in London on 20th June 2019)
- Accessing Snap ML (SkillsCast recorded in June 2019)
- Snap Machine Learning (SkillsCast recorded in June 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.
-
SkillsCast
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)
- Fast Track to Machine Learning with Louis Dorard (in London on 15th - 17th July 2019)
- Functional Concurrency in .NET with C# and F# (in London on 9th - 10th September 2019)
- Infiniteconf 2019 - A one-day community celebration of Big Data, Machine Learning and AI (in London on 4th July 2019)
- FullStack London 2019 - The Conference on JavaScript, Node & Internet of Things (in London on 10th - 12th July 2019)
- London TensorFlow.js (in London on 20th June 2019)
- Hands-on: Fractal art with Fable and WebGL (in London on 20th June 2019)
- Accessing Snap ML (SkillsCast recorded in June 2019)
- Snap Machine Learning (SkillsCast recorded in June 2019)
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.