Laboratory "cloud and Distributed Environments for Analytics in a Luxury Brand"
A.Y. 2022/2023
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
Responsible
Lesson period
Second trimester
Course syllabus
Pioneer of a dialogue with contemporary society across diverse cultural spheres and an influential leader in luxury fashion, Prada Group founds its identity on essential values such creative independence, transformation, and sustainable development, offering its brands a shared vision to interpret and express their spirit. The Group owns some of the world's most prestigious luxury brands, Prada, Miu Miu, Church's, Car Shoe and the historic Pasticceria Marchesi, and works constantly to enhance their value by increasing their visibility and appeal.
This Lab gives the opportunity to work with the latest and most performing instrument for Data Analytics using Cloud and Distributed computing Environment (Azure Services). Moreover, the Lab provides an overview of some case studies and analysis that are specifically designed for Fashion and Luxury market.
Main topics:
- Azure Environment (Databricks and Datalake)
- Programming in Distributed framework (pyspark)
- Multi Language programming in a single notebook (python, R, SQL)
- ML pipeline experiments and tuning (MLflow)
The Lab schedule will be based on the availability of involved institutions/companies, classrooms, and DSE schedule (Second Year when not differently specified).
This Lab gives the opportunity to work with the latest and most performing instrument for Data Analytics using Cloud and Distributed computing Environment (Azure Services). Moreover, the Lab provides an overview of some case studies and analysis that are specifically designed for Fashion and Luxury market.
Main topics:
- Azure Environment (Databricks and Datalake)
- Programming in Distributed framework (pyspark)
- Multi Language programming in a single notebook (python, R, SQL)
- ML pipeline experiments and tuning (MLflow)
The Lab schedule will be based on the availability of involved institutions/companies, classrooms, and DSE schedule (Second Year when not differently specified).
Prerequisites for admission
Knowledge of Python; basic knowledge of SQL and R.
Teaching methods
The Lab is based on frontal teaching with the support of slides and software tools. Lab exercises are proposed and discussed to analyze on considered case studies in the Fashion and Luxury market.
Teaching Resources
Online resources as well as handouts provided throughout the lectures by the teacher.
Assessment methods and Criteria
The assessment method consists in group and personal assignments submitted to the teacher by a shared, prefixed deadline. The evaluation is expressed through an "Approved" - "Not approved" result.
INF/01 - INFORMATICS
SECS-S/01 - STATISTICS
SECS-S/01 - STATISTICS
Laboratory activity: 20 hours