Enrico Gallinucci is Junior Assistant Professor at the Computer Science and Engineering Department of the University of Bologna, where he teaches Big Data and Business Intelligence (BI). At BBS, he teaches BI 2.0, Data Warehousing, and Big Data. His research interests are directed towards the innovation of architectures, techniques, and methodologies to extract value from the data. The main research areas include data platforms, data fabric, Social BI, NoSQL databases, trajectory data, precision agriculture. He published research papers in many prestigious journals, including the VLDB Journal, Decision Support Systems, Future Generation Computer Systems, Information Systems, Data & Knowledge Engineering. He collaborated with international research groups at the Universitat Politècnica de Catalunya and the Université de Tours. He worked as a scientific partner in research projects and consulting activities for companies to study the widespread opinions over politics and vaccines, to carry out materiality analyses, to apply precision agriculture techniques over several food chains. At the international conference EDBT 2021, he received the best demo award for a conversational approach to OLAP analysis. Gallinucci received his Ph.D. in Computer Science and Engineering at the University of Bologna in 2017 and graduated cum laude in 2013 with a thesis on digitally fighting tax evasion.
The goal of the course is to introduce business intelligence (BI) platforms, with specific reference to data warehouses seen as an enabling technology for BI. Teaching will be mainly focused on OLAP, multidimensional modelling (at both the conceptual and logical level), and self-service BI. Some notions about data integration and cleaning will be provided.Data Science and Business Analytics
This course introduces the basic principles and methods of Business Intelligence with emphasis on Data Warehousing, OLAP analysis, and social intelligence. It also focuses on how Business Intelligence solutions can be designed according to user requirements and desires and includes a hands-on session with OLAP tools (mainly Power BI).Data Marketing and Analytics
This course is a natural continuation of the Machine Learning course. It provides guidelines for running a Data Mining process and then discusses, with practical examples, the complete pipeline from data to machine learning concepts.
In particular, the following topics are covered:
Principles are introduced in class with presentation of slides and stimulating discussions with students. Methods are then applied with laboratory exercises.
Data Science and Business Analytics