DS Inno - Data Science driven INNOvation in business and society
I EDITION | ON SITE | APPLICATION
Deadline for Registration
December 8th, 2024
Period
February 10th-14th, 2025
Learning objectives
Due to technological advancements, increasing digitization, and the development of social media, as well as the widespread use of mobile devices with sensors and apps, the phenomenon of big data is becoming crucial not only for businesses but also for many sectors and society as a whole. Consequently, acquiring specific skills to effectively leverage the abundance of data in business innovation environments is particularly important.
Although the importance of this topic in the global economy is becoming clear, the university educational offerings in the fields of innovation management and data science are still limited. To address these aspects comprehensively, it is necessary to develop skills with an interdisciplinary approach, involving scholars in management, economics, as well as computer scientists and statisticians.
This Seasonal School aims to examine the relationship between innovation management and data science through a multi-level approach. First, this relationship will be explored at the company level through not only theoretical knowledge but especially through firsthand testimonials and site visits to large and small enterprises. Second, this relationship will be addressed at the sector level (e.g., healthcare and pharmaceuticals) and finally at the social level by presenting some direct experiences of computer scientists. To conclude, participants will also be provided with stimuli and tools of data science to understand and measure the complexity of this relationship.
Teaching methodologies
Students will be interactively and proactively engaged in the training process thanks to the integration of the theoretical concepts with the practical experience under the guidance of the trainers, encompassing both academics and practitioners.
Target participants
Undergraduate, postgraduate and PhD students from different backgrounds (e.g. management, economics, law, political science, engineering, life sciences).
Coordinattor and Key teaching staff
Coordinator: Giulio Ferrigno
Key teaching staff: Andrea Piccaluga, Alberto Di Minin, Saverio Barabuffi, Enrico Marcazzan, Francesca Chiaromonte, Chiara Seghieri, Andrea Vandin, Laura Magazzini, Valentina Lorenzoni, Riccardo Guidotti, Anna Monreale, Roberto Trasarti, Michela Natilli, Valerio Grossi