A transdisciplinary perspective on Big Data (PhD Course)

MISSION AND GOALS

Big data is everywhere and researchers from all disciplines are addressing this topic from their own perspective, creating vertical excellent experiments, but often loosing the wider picture. This course aims at reconstructing such a picture critically analysing how each discipline contributes to practice and academic debate.

Proposer and Coordinator

  • Emanuele Della Valle

LECTURERS

  • DEIB: Danilo Ardagna, Marco Brambilla, Emanuele Della Valle and Pierluca Lanzi
  • DIG: Michela Arnaboldi and Fabio Pammolli
  • DMAT: Piercesare Secchi and Simone Vantini
  • DESIGN: Paolo Ciuccarelli
  • DASTU: Valeria Fedeli

TEACHING ORGANIZATION

The course is divided in 3 parts. The 1st provides a transversal view on grand challenges to which big data can contribute and allows understanding what big data is. The 2nd one presents the main paradigms and techniques for data analytics. The 3rd one teaches how practically tame volume, variety, velocity, and veracity.

SUBJECT AND PROGRAMME OF THE COURSE

Part 1: Grand challenges of Big Data

  • Opportunities for social, environmental and economic problems.
  • Problem of current research: lack of transversal view.
  • Students define a transdisciplinary research objective, highlighting their contribution to practice and academic debate. This initial work is the starting point for the assignment and a fil-rouge across the course.

Part 2: Making sense of Big Data

  • Introduction to data analytics with the R language
  • Knowledge discovery and Data Mining
  • The role of visualization
  • Discussion of domain applications and students’ transdisciplinary assignments

Part 3: Taming volume and velocity, without forgetting variety and veracity with Big Data technologies

  • Scaling computation and storage horizontally
  • Map Reduce basics from Hadoop to Apache Spark and Flink
  • Information flow processing principle, approaches and tools
  • Hands-on Apache Spark to tame volume and velocity in data analytics
  • Discussion of domain applications and students’ transdisciplinary assignments

Part 4: students’ reporting (to be agreed)

  • Group 1
  • Group 2
  • Gourp N

LEARNING EVALUATION

Students will be required to build a research case, identifying business value, data and methods, using the tools to analyze and visualize data, critically analyzing pitfalls, and highlighting their contributions.