A range of six subjects in Data Analytics, Data Mining and Machine Learning
A range of 6 subjects in Data Analytics, Data Mining and Machine Learning will give students the chance to develop skills and expertise, broaden their international experience and capitalize on knowledge opportunities.
The subjects have been selected so that they provide an engaging and high impact educational experience that will enable them to make progress towards their degree or expand their academic and/or professional life and experience.
- Data and Algorithmic Bias in the Web, by Ricardo Baeza-Yates
- Online social networks and media, by Evi Pitoura
- Challenges for Visual Analytics in Data Science, by J. J. van Wijk
- Data integration, CNNs, Fairness, Privacy in the Context of Urban Data, by Bill Howe
- Mining Structured Knowledge from Massive Text Data: A Data-Driven Approach, by Jiawei Han
- Ethics of Data Driven Innovation, by Natasa Milic-Frayling
Participants should bring their own laptop, which they should use during the exercises and other hands-on work they will do during the School.