ID:
SCC0928
Duration (hours):
56
CFU:
6
SSD:
CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI
Year:
2025
Overview
Date/time interval
Secondo Semestre (23/02/2026 - 29/05/2026)
Syllabus
Course Objectives
The course aims to provide students with the knowledge and skills necessary for the exploration and modeling of complex data using the main multivariate analysis techniques and modeling techniques (regression and classification).
At the end of the course the student will be able to:
• recognize and understand complex data structures and the principal techniques to explore and model data of interest for chemistry and environmental sciences.
• apply the gained knowledge in a multidisciplinary context, and in particular to problems arising from the impact of chemicals on the environment.
As a result, the student will develop the following skills:
• ability to explore and manage complex data systems using quantitative methodologies
• ability to identify appropriate models based on the problem under investigation
• ability to develop and validate qualitative and quantitative predictive models
Finally, the student must develop adequate communication skills regarding the exposure of the identified problems, the methods used and the results achieved, using an appropriate language, as well as the ability to formulate a judgment and derive conclusions based on the information available or derived through the application of multivariate analysis and modeling.
At the end of the course the student will be able to:
• recognize and understand complex data structures and the principal techniques to explore and model data of interest for chemistry and environmental sciences.
• apply the gained knowledge in a multidisciplinary context, and in particular to problems arising from the impact of chemicals on the environment.
As a result, the student will develop the following skills:
• ability to explore and manage complex data systems using quantitative methodologies
• ability to identify appropriate models based on the problem under investigation
• ability to develop and validate qualitative and quantitative predictive models
Finally, the student must develop adequate communication skills regarding the exposure of the identified problems, the methods used and the results achieved, using an appropriate language, as well as the ability to formulate a judgment and derive conclusions based on the information available or derived through the application of multivariate analysis and modeling.
Course Prerequisites
Basic knowledge of the basic functions of the software EXCEL. Elements of Statistics.
Teaching Methods
The course is organized in 56 hours of which 40 hours are lectures (Lecturer in Varese, video-connection with Como), and 16 hours consist of a computational laboratory in Varese. Attendance at the computational laboratory is mandatory for 75% of the hours.
The computational laboratory will support the theory with practical examples by providing the student the opportunity to use appropriate software.
Attendance to the Lectures is optional but recommended. The final exam will be the same for attending and non-attending students.
The computational laboratory will support the theory with practical examples by providing the student the opportunity to use appropriate software.
Attendance to the Lectures is optional but recommended. The final exam will be the same for attending and non-attending students.
Assessment Methods
All topics covered in the lectures and exercises, including any in-depth studies in the form of articles or seminars, are examined. The learning assessment consists of a two-hour written test comprising three questions, two of which are open-ended, each on one of the main topics of the course programme, and one exercise. The examination will be marked in thirtieths (the written test is considered sufficient and the examination passed with a mark of 18/30). Alternatively, students may write a report, to be submitted at least 10 days prior to the scheduled date of the examination, which includes the appropriate application of the quantitative methods learned during the course to a dataset selected by the student or proposed by the lecturer (to be agreed prior to the presentation of the report). The report will be graded in thirtieths. Upon reaching the pass mark in the report (18/30), students will be admitted to an oral examination also marked in thirtieths. The examination will be considered passed upon reaching the pass mark (18/30) in both the report and the oral part. The final mark will be an average of the marks awarded in the report and the oral examination. The assessment of the written examinations ( tests and report) and the oral examination, if any, will take particular account of the following criteria -Precision and correctness of answers. -Ability to present, argue and summarise the topics addressed in appropriate language -Ability to use the knowledge acquired to recognise and solve a problem inherent to the topics addressed in the lecture. The examination is the same for attending and non-attending students.
Contents
Introduction to the course and evaluation of basic knowledge (2h).
Introduction to chemometrics and its utility in multiple fields of application. Elements of descriptive statistics. Analysis of the structure of data and pre-treatment methods (8h): missing data, variable transformation and scaling. Association between variables. Basic concepts of matrix algebra.
Methods of explorative analysis (10h): Principal Component Analysis and Cluster analysis.
General introduction to data modeling (4h). Validation techniques (cross-validation, external validation) and variable selection methods (4h).
Linear regression (4h): Ordinary Squares Minimum Method (OLS). Diagnostic methods.
Classification methods (6h): k-NN as an example of methods based on minimum distance. CART as an example of tree classification methods. Discriminant analysis. Evaluation parameters of the efficiency of the classification.
In silico alternatives to animal testing and QSAR modeling with examples of application for the prediction of properties and activities of organic environmental pollutants (2h).
Introduction to chemometrics and its utility in multiple fields of application. Elements of descriptive statistics. Analysis of the structure of data and pre-treatment methods (8h): missing data, variable transformation and scaling. Association between variables. Basic concepts of matrix algebra.
Methods of explorative analysis (10h): Principal Component Analysis and Cluster analysis.
General introduction to data modeling (4h). Validation techniques (cross-validation, external validation) and variable selection methods (4h).
Linear regression (4h): Ordinary Squares Minimum Method (OLS). Diagnostic methods.
Classification methods (6h): k-NN as an example of methods based on minimum distance. CART as an example of tree classification methods. Discriminant analysis. Evaluation parameters of the efficiency of the classification.
In silico alternatives to animal testing and QSAR modeling with examples of application for the prediction of properties and activities of organic environmental pollutants (2h).
Course Language
Italian
More information
Office Hours: The teacher is available by appointment arranged by e-mail or telephone (Varese, Via Dunant, 3, Red Floor).
Degrees
Degrees
ENVIRONMENTAL SCIENCES
Master’s Degree
2 years
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People
People (2)
Teaching staff
Docenti di ruolo di IIa fascia
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