Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2022/2023
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Statistical methods for business intelligence (It will be activated in the A.Y. 2021/2022)
Teaching code
4S009074
Credits
6
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICA
Learning outcomes
The course will introduce the modeling / quantitative foundations of modern Business Analytics (BA) theory, taking advantage of a rigorous mathematical approach in order to effectively deal with real case studies. Through probabilistic / statistical tools of descriptive and predictive analysis, the course will provide elements of predictive analysis, risk analysis, simulation and data mining and decision analysis. Students will acquire the fundamental theoretical tools both to develop models to manage typical BA challenges, and to communicate their results concretely, so as to provide brilliant solutions to specific problems in a synergistic and proactive way. A great emphasis will be given to real world applications, also making use of specific packages for data analysis, manipulation and prediction (e.g. Rapidminer, Orange, Knime, R-AnalyticFlow, etc.). At the end of the course the student has to show to have acquired the following skills: ● master the typical pipeline: query, reporting and online analytical processing ' ● ability to control the analytical / quantitative flow, i.e. I / O data management, forecasting and optimization ● ability to develop models in predictive analytics ● ability to develop data mining and cluster analysis models ● ability to classify within heterogeneous databases ● ability to develop pro customer retention rate, targeting marketing models, also in relation to social media, financial (portfolio, insurance, etc.) analytics.
