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Data Science Lab at Politecnico di Milano

Data Science Lab at Politecnico di Milano

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    • Data Management for Large-scale Analytics (PhD Course)

Teaching

We list here all the teaching activities related to our lab.

  • Data Science for Business Innovation course
    • Mooc on Coursera
    • Master Class in Amsterdam (November 2019)
    • Master Class in Milan (December 2019)
  • Unstructured and Streaming Data Engineering
  • Data Management For Large-scale Analytics (PhD course)
  • Web and Data Science course
  • Crash Course in Data Science – People@DEIB

Mooc on Data Science for Business Innovation on Coursera

We just published our new MOOC “Data Science for Business Innovation” on Coursera!

Our course is available for free on Coursera and is jointly offered by Politecnico di Milano and EIT Digital, as a compendium of the must-have expertise in data science for non-technical people, including executives, middle-managers to foster data-driven innovation.

The course is an introductory, non-technical overview of the concepts of data science. You can enrol in the course online.

The course is completely free and you can enjoy content at any time, with professional English speakers and animated, engaging materials.

Web and Data Science course

The Web and Data Science course focuses on the study of large-scale socio-technical systems associated with the World Wide Web. It considers the relationship between people and technology, the ways that society and technology complement one another and the way they impact on broader society. These analyses are inherently associated with Big Data management issues.

The course is given in Como Campus by Marco Brambilla and Emanuele Della Valle.

Up-to-date calendar of the course on Google Docs: calendar

Official course page on Polimi site: web page

All the materials presented in class is published on beep: web page

Topic Lecturer
Introduction: Big Data and Data Science Della Valle
Semantic Web (part 1/2): intro + RDF + SPARQL Della Valle
Web Api + Scraping Brambilla
Semantic Web (part 2/2): OWL + Ontology Based Data Access Della Valle
NoSQL Data Models – KV, doc, column, .. Brambilla
Big Data techs 1/2: MapReduce + the hadoop ecosystem + Spark Della Valle
Invited seminar: AWS Serverless Brambilla + invited
Big Data techs 2/2: Spark hands-on Della Valle
Basic statistics in practice Brambilla
PCA + clustering Brambilla
Classification + Neural Networks + Deep Learning Brambilla
R examples Marazzi
Data science with Spark 1/2 Della Valle
Data Wrangling Brambilla
Data science with Spark 2/2 Della Valle
Human Computation & Crowdsourcing Brambilla
Web Information Retrieval Brambilla
Mid-term project delivery Brambilla+Della Valle
Final project delivery Brambilla+Della Valle

EVALUATION

The exam consist in a practical part (project work) and a theoretical part (written exam with possible oral discussion).

The practical part consist in solving a realistic problem in web science / data science, based on real or realistic dataset publicly available , accessible via Web API, or provided by the teachers. 40% of the grade of this course is granted based on the evaluation of a project work.

The written exam is composed of a mix of theoretical questions regarding any of the course subjects, and excercises, regarding the technical content and how to apply it in practice.

The oral examination consists of a discussion about the written test and the practical part of the exam. It can include also questions on any subject of the course.

Recent News

  • PERISCOPE: the EU project on socio-economic and behavioral impacts of the COVID-19 pandemic
  • DATA-LIFE PROGRAM 2020
  • Challenges in Data-Driven Genomic Computing
  • FaST – Fashion Sensing Technology
  • A Tool for Extracting Emerging Knowledge from Social Media
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  • Mission
  • Projects
  • Teaching
    • Crash Course in Data Science – Passion in Action
    • Data Management for Large-scale Analytics (PhD Course)
    • Data Science for Business (Amsterdam)
    • Data Science for Business (Milan)
    • Unstructured and Streaming Data Engineering
    • Unstructured and Streaming Data Engineering 2019-20
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