Laboratory "cloud and distributed environments for analytics in a luxury brand"
A.A. 2024/2025
Obiettivi formativi
Partner company: Prada Group
This Lab is provided within the Data Science for Economics (DSE) degree program.
A small number of students can be admitted due to logistics constraints.
The students (either DSE or non-DSE) must apply for admission. Candidates will be selected by the involved institutions/companies according to CV and motivations.
For application, students must respond to a call that is posted on this website: https://dse.cdl.unimi.it/en/courses/laboratories
The call is typically published a few weeks before the Lab starts.
This course aims at giving students the possibility to know better which are the competences, tasks and analysis that a Data Science Team is usually required to do in a Luxury Company. This course will focus on 2 business-cases which will be solved by analysis and ML models by coding in a distributed manner on Azure Environment
This Lab is provided within the Data Science for Economics (DSE) degree program.
A small number of students can be admitted due to logistics constraints.
The students (either DSE or non-DSE) must apply for admission. Candidates will be selected by the involved institutions/companies according to CV and motivations.
For application, students must respond to a call that is posted on this website: https://dse.cdl.unimi.it/en/courses/laboratories
The call is typically published a few weeks before the Lab starts.
This course aims at giving students the possibility to know better which are the competences, tasks and analysis that a Data Science Team is usually required to do in a Luxury Company. This course will focus on 2 business-cases which will be solved by analysis and ML models by coding in a distributed manner on Azure Environment
Risultati apprendimento attesi
Basic knowledge of Azure Environment (Databricks and Datalake) for programming in Distributed framework (pyspark), using multi-language programming in a single notebook (python, R, SQL) and optimizing ML pipelines by running experiments on MLFlow
Periodo: Secondo trimestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
INF/01 - INFORMATICA
SECS-S/01 - STATISTICA
SECS-S/01 - STATISTICA
Attivita' di laboratorio: 20 ore