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 It will be activated in the A.Y. 2026/2027
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1 module among the following2 modules among the following1 module among the following
A.A. 2025/26 e 2026/27 - Network science and econophysics not activated
A.A. 2026/27 Complex systems and social physics not activated1 module among the following2 modules among the followingLegend | 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 (2025/2026)
Teaching code
4S009074
Teacher
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICS
Period
1st semester dal Oct 1, 2025 al Jan 30, 2026.
Courses Single
Authorized
Learning objectives
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.
Program
Course Program (10 Modules covering 24 Lectures):
1.Probability and Statistical Foundations
2. Descriptive Statistics: Summarizing and Visualizing Data
3. Likelihood and Parameter Estimation
4. Simple and Multiple Linear Regression
5. Time Series Analysis and ARIMA Modeling
6. Classification Methods: KNN, Naive Bayes, and Decision Trees
7. Neural Networks and Gradient Descent
8. Logistic Regression
9. Clustering
10. Data Mining
Note: Python is used throughout the course for all practical exercises and implementations.
Didactic methods
All lectures include slides and Python code, which will be provided to students for practical exercises and hands-on implementation.
Learning assessment procedures
The final exam will be a written test. Students will answer questions by providing explanations, calculations as required.
Evaluation criteria
The final written exam will cover all course topics and will determine the student’s final grade.
Exam language
Inglese / English
