Skip to main content
If you continue browsing this website, you agree to our policies:
  • Condizioni di utilizzo e trattamento dei dati
Continue
x
e-Learning - UNIMIB
  • Home
  • Calendar
  • My Media
  • More
Listen to this page using ReadSpeaker
English ‎(en)‎
English ‎(en)‎ Italiano ‎(it)‎
You are currently using guest access
 Log in
e-Learning - UNIMIB
Home Calendar My Media
Percorso della pagina
  1. Postgraduate
  2. PhD School
  3. Doctoral programs' teaching activities
  4. Chemical, Geological and Environmental Sciences / Scienze Chimiche, Geologiche e Ambientali
  5. 2023-2024
  6. Intercurricular
  1. Time Series Analysis of Environmental Data
  2. Summary
Course full name
Time Series Analysis of Environmental Data
Course ID number
2324-1-124R016
Course summary SYLLABUS

Course Syllabus

  • Italiano ‎(it)‎
  • English ‎(en)‎
Export

Titolo

Time Series Analysis of Environmental Data

Docente(i)

Tullia Bonomi and Chiara Zanotti

Lingua

Italiano/Inglese

Breve descrizione

L'obiettivo del corso è quello di apprendere i fondamenti dell'analisi delle serie temporali utili per le applicazioni a dati ambientali, con particolare attenzione alla qualità e disponibilità delle risorse idriche.
Verranno svolte esercitazioni pratiche in ambiente R su dati di acque sotterranee, superficiali e dati meteorologici.

Le lezioni frontali e la formazione copriranno i principali aspetti della gestione, acquisizione, visualizzazione, analisi e modellazione di dati di serie temporali ambientali:

  • Tipologie di dati ambientali
  • Basi di R
  • Esplorazione e visualizzazione di serie temporali, statistiche descrittive
  • Analisi dei trend
  • Auto e cross-correlazione
  • Decomposizione di serie storiche
  • Time series cluster analysis

Valutazione: Si - presentazione orale finale

CFU / Ore

2 CFU - 20 Ore
Chiara Zanotti 16 ore lezione
Tullia Bonomi 4 ore esercitazione

Periodo di erogazione

II semestre: June 2024

Sustainable Development Goals

ISTRUZIONE DI QUALITÁ | ACQUA PULITA E SERVIZI IGIENICO-SANITARI | IMPRESE, INNOVAZIONE E INFRASTRUTTURE
Export

Title

Time Series Analysis of Environmental Data

Teacher(s)

Tullia Bonomi and Chiara Zanotti

Language

Italian/English

Short description

The aim of the course is to learn the fundamentals of time series analysis for environmental data applications, with a specific focus on water resources quality and availability.
Practical exercises will be carried out in the R environment on groundwater, surface water, and meteorological data.

Lectures and training will cover the main aspects of environmental time series data management, acquisition, visualization, analysis, and modeling:

  • Types of environmental data
  • R basics
  • Explore and visualize time series data, descriptive statistics
  • Trend analysis
  • Auto and cross-correlation
  • Time series decomposition
  • Time series cluster analysis

Evaluation: YES - with a final oral presentation

CFU / Hours

2 CFU - 20 Hours
Chiara Zanotti 16 hours lectures
Tullia Bonomi 4 hours training

Teaching period

II semester: June 2024

Sustainable Development Goals

QUALITY EDUCATION | CLEAN WATER AND SANITATION | INDUSTRY, INNOVATION AND INFRASTRUCTURE
Enter

Key information

Field of research
GEO/05
ECTS
2
Course Length (Hours)
20

Staff

    Teacher

  • TB
    Tullia Bonomi
  • CZ
    Chiara Zanotti

Students' opinion

View previous A.Y. opinion

Bibliography

Find the books for this course in the Library

Enrolment methods

Manual enrolments
Self enrolment (Student)

Sustainable Development Goals

QUALITY EDUCATION - Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
QUALITY EDUCATION
CLEAN WATER AND SANITATION - Ensure availability and sustainable management of water and sanitation for all
CLEAN WATER AND SANITATION
INDUSTRY, INNOVATION AND INFRASTRUCTURE - Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
INDUSTRY, INNOVATION AND INFRASTRUCTURE

You are currently using guest access (Log in)
Policies
Get the mobile app
Powered by Moodle
© 2025 Università degli Studi di Milano-Bicocca
  • Privacy policy
  • Accessibility
  • Statistics