Laboratory "cloud and Distributed Environments for Analytics in a Luxury Brand"
A.Y. 2024/2025
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
Lesson period: Second trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
INF/01 - INFORMATICS
SECS-S/01 - STATISTICS
SECS-S/01 - STATISTICS
Laboratory activity: 20 hours