Learning style

Skills and competencies expected to be developed:


  • Autonomy

    A successful data scientist is able to lead a work team taking the responsibility of key technical choices; she/he is expected to appraise results, as well as visualize and communicate them in an effective manner. The learning process will contribute to the development of this kind of skills by exposing each student to team-working, contributing to project work, checking their learning with professors, being involved into stage and writing a final dissertation. This dissertation will stem out of a work done either in the academy or at private companies or public bodies (internship).

  • Communication

    Even though the student will specialise in the domain defined by the choosen curriculum, a data science career is interdisciplinary by nature, so every student will also learn how to interact with practitioners and scientists from different fields and contexts. She/he will be able to effectively capture the problem statement and present results. As well, very good communication and a strong attitude toward collaboration, along with the capability of exerting leadership, will be developed in a multi-disciplinary setting by means of lectures for peers attended by professors, reading seminars, and simulation of interviews with experts.

  • Learning

    The main target of the learning process is to develop the ability to cope with new problems: this means that students will be trained in a "problem-oriented" mind-setting. She/he will be keen to update and enhance knowledge by keeping the pace of scientific and technological innovations, moving from a sound grounding in basic as well domain expertise. The development of the final dissertation will test all the acquired skills and will make each new data scientist able to operatively apply them on the job.


Collaborative learning


Students will cooperate on team projects and activities:

  • Presence on social media platforms to spread the words about and get feedback (supervised by tutors)
  • Seminars and other conferences
  • Contests and international awards based on problem solving
  • Hands-on labs, hack-a-tons, ‚Ķ

Module
SSD
CFU
Teoria delle reti e applicazioni
SECS-S/06
6
Metodi Statistici per laValutazione
SECS-S/01
6
Ottimizzazione dinamica
SECS-S/06
6
Probabilità e processi stocastici
SECS-S/06
6
Modelli e metodi statistici per le previsioni
SECS-S/01
6
Statistica computazionale
SECS-S/01
6
Epistemologia critica delle scienze sociali: teorie metodi e dati
SPS/07
12
Finanza matematica
SECS-S/06
6
Metodi quantitativi per gli economisti
SECS-S/01
12
Analisi microeconomica
SECS-P01
12
Analisi microeconomica - corso integrato
SECS-P01/SECS-P02
12
Auditing e governance d'azienda
SECS-P07
12
Governance e strategia aziendale
SECS-P07
12
Teoria della finanza
SECS-P/11
12
Software e applicazioni per l'analisi statistica di dati economici
SECS-S/01
6
Econometria
SECS-S/06
6
The digital society
SPS/08
6
Concept analysis in the web environment
SPS/04
6
Analisi organizzativa dei big data
SECS-P/10
6
Module
SSD
CFU
Numerical methods
MAT/08
6
Mathematical Physics Models
MAT/07
6
Ricerca operativa
MAT/09
6
Computational complexity
INF/01
6
Algorithms and parallel computing
INF/01
6
Information theory
ING-INF/03
6
Data security
ING-INF/05
6
Incertezza dei dati
ING-INF/07
6
Astroinformatica
FIS/05
6
Geoinformaticaa
FIS/06
6
Analisi dati in Fisica
FIS/01
6
Mathematics for Cryptography
INF/01
6
Fisica computazionale
FIS-01/06
6
Image processing
ING-INF/03
6
Text mining
SECS-S/01
6
Data visualization
ING-INF/05
6
Introduzione alla bioinformatica
BIO/10
6
Bioinformatica avanzata
INF/01
6
Teoria dei segnali
ING-INF/03
6
Statistical methods for industrial process monitoring
SECS-S/02
6
Advanced non linear methods for mechanical signal processing
ING-IND/13
6
Chimica computationale
CHIM/02
6
Metodi computazionali per lo studio delle reazioni di interesse industriale
CHIM/04
6
Module
SSD
CFU
Statistica medica per la RWE
MED/01
12
Introduzione alla bioinformatica
BIO/10
6
Bioinformatica avanzata
INF/01
6
Farmacoepidemiologia
BIO/14
6
Farmacoviglianza
BIO/14
6
Metodologia epidemiologia clinica
MED/01
6
Fondamenti di Oncologia
MED/06
6
Fondamenti di Cardiologia
MED/11
6
Introduzione alle biotecnologie e biologia
BIO/13
6
Metagenomica
BIO/19
6
Genomica
BIO/18
6
Proteomica
BIO/10
6
Modellistica degli ecosistemi
BIO/03
6