Online social media connect us all. How can we access the information that is hidden in our social networks? For example, do you know who is your most influential follower on Twitter?
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.
Evelina uses machine learning and data science for academic research in personalized medicine. She studied computational statistics and machine learning at University College London and currently she is finishing her PhD at Cambridge University in bioinformatics and statistical genomics.
Evelina has used many different languages to implement machine learning algorithms, such as Matlab, R or Python. In the end, F# is her favourite and she uses it frequently for data manipulation and exploratory analysis. She writes a blog on F# in data science at evelinag.com and you can find her on Twitter as @evelgab.