Training and Research
PhD school courses/classes - 2025/2026
Cose che devi assolutamente sapere
1. I dottorandi devono ottenere un determinato numero di CFU ogni anno frequentando le attività didattiche offerte dal proprio corso di dottorato e dalla Scuola di Dottorato. Per ulteriori informazioni, consulta il Vademecum del Dottorando: https://www.univr.it/phd-vademecum
2. Non è richiesta l'iscrizione ai corsi, a meno che non sia esplicitamente indicato; si prega di consultare le informazioni sul corso per verificare se l'iscrizione è richiesta o meno. Quando l'iscrizione è effettivamente richiesta, non verrà inviata alcuna e-mail di conferma dopo la registrazione. Si prega di non inviare richieste di conferma: se sono state inserite le informazioni richieste, l'iscrizione è andata a buon fine.
3. Quando i link Zoom delle lezioni non sono esplicitamente indicati, si intende che i corsi sono organizzati per essere erogati solo in presenza. Non è quindi possibile in questi casi richiedere un link Zoom.
4. Tutte le informazioni sui corsi in nostro possesso vengono pubblicate qui. Si prega di non richiedere informazioni mancanti o link Zoom: non appena riceveremo nuove informazioni, le pubblicheremo e comunicheremo tempestivamente.
Teaching Activities ex DM 226/2021: Linguistic Activities
Teaching Activities ex DM 226/2021: Research management and Enhancement
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
STATISTICAL ANALYSIS WITH R - MODULE 1
Credits: 1
Language: English
Teacher: Michele Scandola
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Teacher: Marco Minozzo
BASIC LEVEL STATISTICS
Credits: 2.5
Language: English
Teacher: Margherita Brondino
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 1
Language: English
Teacher: Lucia Cazzoletti
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 0.5
Language: inglese
Teacher: Giuseppe Verlato
ANALISI DI SOPRAVVIVENZA: TEST LOG-RANK, CURVE DI SOPRAVVIVENZA DI KAPLAN-MEIER, MODELLO DI REGRESSIONE DI COX
Credits: 1.5
Language: English
Teacher: Simone Accordini
STATISTICAL ANALYSIS WITH R - MODULE 2
Credits: 0.8
Language: Inglese/Italiano
Teacher: Alessandro Mantovani
INTERMEDIATE LEVEL STATISTICS
Credits: 2.5
Language: Inglese
Teacher: Margherita Brondino
STATISTICAL ANALYSIS WITH R - MODULE 1 (2025/2026)
Teacher
Referent
Credits
1
Language
English
Class attendance
Free Choice
Location
VERONA
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Wednesday 18 February 2026 14:00 - 18:00 Duration: 4:00 AM |
Aula virtuale - Lezione online | Michele Scandola | The course aims to introduce the participants to R and to some of its most powerful innovations, which have recently reshaped the way the R code is designed. The approach of the course is mainly applicative, and particular attention is devoted to some topics which are becoming more and more relevant in research, and typically entails highly time-consuming activities, such as data manipulation and code documentation. A unified conceptual framework is provided on model fitting, in order to make participants able to cope with different (and new) regression models autonomously and efficiently. This module introduces the participants to R language and to its basic functions for statistical analysis of data and stochastic simulations. You will need R and RStudio installed on your laptop. Zoom link: https://univr.zoom.us/j/81244060515 |
|
Monday 02 March 2026 14:00 - 18:00 Duration: 4:00 AM |
Aula virtuale - Lezione online | Michele Scandola | The course aims to introduce the participants to R and to some of its most powerful innovations, which have recently reshaped the way the R code is designed. The approach of the course is mainly applicative, and particular attention is devoted to some topics which are becoming more and more relevant in research, and typically entails highly time-consuming activities, such as data manipulation and code documentation. A unified conceptual framework is provided on model fitting, in order to make participants able to cope with different (and new) regression models autonomously and efficiently. This module introduces the participants to R language and to its basic functions for statistical analysis of data and stochastic simulations. You will need R and RStudio installed on your laptop. Zoom link: https://univr.zoom.us/j/81244060515 |
