The F#orce Awakens
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Let's dive together into the the world of Star Wars! This talk will use the force of F# to process publicly available datasets relating to the Star Wars movies to find out who's the most important character in the stories and why were the prequels so unsuccessful. On the way, you'll see why F# is a great language for data science - from preprocessing the data to visualizing them - and you'll also learn how you can use similar data processing pipelines to get interesting insights from your own data.
The Call for Papers is now open for F# eXchange 2017! Submit your talk for the chance to join a stellar line-up of experts on stage. Find out more.
Get your tickets for F# eXchange 2017!
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)
- Haskell eXchange 2019 (in London on 10th - 11th October 2019)
- London #PowerBI user group with Will Thompson (in London on 19th August 2019)
- Telling Human Stories With Data (in London on 19th August 2019)
- Ethics in Artificial Intelligence (SkillsCast recorded in July 2019)
- GraphQL: May the Best API Win! (SkillsCast recorded in July 2019)
Thanks to our sponsors
The F#orce Awakens
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.
Let's dive together into the the world of Star Wars! This talk will use the force of F# to process publicly available datasets relating to the Star Wars movies to find out who's the most important character in the stories and why were the prequels so unsuccessful. On the way, you'll see why F# is a great language for data science - from preprocessing the data to visualizing them - and you'll also learn how you can use similar data processing pipelines to get interesting insights from your own data.
The Call for Papers is now open for F# eXchange 2017! Submit your talk for the chance to join a stellar line-up of experts on stage. Find out more.
Get your tickets for F# eXchange 2017!
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)
- Haskell eXchange 2019 (in London on 10th - 11th October 2019)
- London #PowerBI user group with Will Thompson (in London on 19th August 2019)
- Telling Human Stories With Data (in London on 19th August 2019)
- Ethics in Artificial Intelligence (SkillsCast recorded in July 2019)
- GraphQL: May the Best API Win! (SkillsCast recorded in July 2019)
Thanks to our sponsors
The F#orce Awakens
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.