Gianpaolo Coro, CNR presented a course on Signal Processing and Mining of Big Data: Biological Data as a Case Study at the University of Pisa. 25 doctoral students from various universitites attended with interested parties from such background as Computer Engineering and Computer Science. 

Some key topics discussed included Data Mining and Big Data analysis. 

The course took place 2-6 May 2016 and took advantage of the BlueBRIDGE VREs. 

For full information on the course visit the course page here

Short Abstract 

Big Data analytics is gaining large interest in both public and scientific agendas, because it has demonstrated that it is possible to extract valid information from a large amount of noisy data and to produce valuable information for decision makers. Applications of Big Data analytics can be found in a large variety of domains, including economics, physics, healthcare and biology. In this last domain, analytics have been used, for example, to predict climate change impact on species' distribution, to monitor the effect of overfishing on economy and marine biodiversity and to prevent ecosystems collapse.

In this course, practical applications of Big Data analytics will be shown, with focus on several signal processing and machine learning-based techniques. The course will clarify the general concepts behind these techniques, with an educational approach making these concepts accessible also to students with intermediate mathematical skills. The examples will regard real cases involving data that would have been unpractical to be human-analyzed and corrected, especially in the biology domain: time series forecasting, periodicities detection, comparison of geographical distribution maps, assessment of environmental similarities between different areas, global scale species distributions.

Course Contents in brief:

  • Cloud and distributed computing
  • Big Data analysis
  • e-Infrastructures
  • Large time series forecasting
  • Automatic periodicities detection
  • Neural Networks
  • Large scale probabilistic GIS maps