Abstract: Network data allows analysts to understand the relationships between things. Analysing networks allows us to build applications like; make friend recommendations in social networks, find the quickest driving route between two points, or identify potentially fraudulent financial transactions. We can also use visualisation techniques that allow us see interesting aspects of the networks and explore different regions of interest.
This tutorial will take some real world data and guide users through the process of loading the data into Python, structuring it into a format suitable for network analysis, using algorithms to highlight key properties and visualising the network. We’ll then take this analysis and turn it into an interactive application with Streamlit.
- Able to read in and manipulate network data
- Covering some useful algorithms to understand network data better
- Visualise network data with different options
Some familiarity with Python
Jupyter and Streamlit experience would be useful
Bio: Ian Hansel is a Director of Verge Labs, a company empowering businesses through Machine Learning and Artificial Intelligence. Verge Labs bridges the gap between business and cutting-edge research applications. Ian has lead data teams in corporates and believes in taking away the complexity of machine learning to show people how to use amazing technology on their own.