Data mining, data visualisation and network analysis for EUR research
On December 13th 2016 we explored the possibilities of high performance data analytics and how it will help you in your text based research. Please find the slides and some pictures of the session here at SURFdrive.
We will look at a non-technical way how to drastically scale up tasks like data mining, data visualisation or network analysis. If you feel that your desktop consumes your precious time when doing calculations or that you want to do more beyond the power of your desktop, perhaps high performance computing can help.
The amount of data required to analyse complex behavior is impressive, however, with the High Processing Computing this task can be done by the computer. Massive amounts of data can be analyzed at once. On December 13th Frank Heere and Damian Podareanu from SURF will join us at the EUR to provide a training on the use of High Processing Computers in the Social Sciences. They will emphasize on the need for a heterogeneous computing solution, user cases in the social sciences, and the power of the visualization of data. Further they will show the possibilities of the Jupyter Notebook, a web application that allows researchers to create and share documents that contain live code, equations, visualizations and explanatory text.
The EUR has access to several hpc services for high performance data analytics. See: https://www.eur.nl/en/research/research-matters/research-data-management/high-performance-computing
Times Higher Education recently raised the interesting question: How the social sciences can embrace the big data revolution. See: https://www.timeshighereducation.com/news/how-social-sciences-can-embrace-big-data-revolution?utm_source=SAGE_social&hootPostID=90083153b6f509d4ff0aafb69c9d72df
See for experiences by EUR researchers with HPC: Easy access to processing power for computational recommendation models by dr. Flavius Frasincar [ERIM]: https://www.surf.nl/en/knowledge-base/2016/case-easy-access-to-processing-power-for-computational-recommendation-models.html