Laboratory "cloud and distributed environments for analytics in a luxury brand"
A.A. 2023/2024
Learning objectives
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
Expected learning outcomes
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.
Course syllabus and organization
Edizione unica
Periodo
Secondo trimestre
INF/01 - INFORMATICA
SECS-S/01 - STATISTICA
SECS-S/01 - STATISTICA
Attivita' di laboratorio: 20 ore