Online social media connect us all. How can we use the information that is hidden in our social networks? For example, do you know who is your most influential follower on Twitter? Join Evelina Gabasova as we explore these questions with F#.
In this session we will work through the whole process of social network analysis: from downloading connections using Twitter REST-based API, to implementing our own PageRank algorithm which finds the most central Twitter accounts. In the process you’ll see how we can use F# type providers to access data and harness the power of the statistical language R to run some machine learning algorithms.
At the end, you’ll know how to run your own analysis on data from Twitter and how to use data science tools to gain insights from social networks.
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Data science with F#: Social network analysis - 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.