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Big Data Analytics A.A. 2021/22

All lectures will be provided also remotely, through the Teams team named “599AA 21/22 - BIG DATA ANALYTICS [WDS-LM]”




  • Wednesday 09:00 - 10:45 Aula Fib M1
  • Friday 09:00 - 10:45 Aula Fib C1

List of registered students:

Team Registration: build up teams of 3 or 4 students and register your team here, by September 29th: Teams should be composed of students already registered to the course.

Only for the registered teams, express your preference for the datasets by September 30th:

Learning goals

In our digital society, every human activity is mediated by information technologies, hence leaving digital traces behind. These massive traces are stored in some, public or private, repository: phone call records, movement trajectories, soccer-logs, and social media records are all examples of “Big Data”, a novel and powerful “social microscope” to understand the complexity of our societies. The analysis of big data sources is a complex task, involving the knowledge of several technological and methodological tools. This course has three objectives:

  • introducing to the emergent field of big data analytics and social mining;
  • introducing to the technological scenario of big data, like programming tools to analyze big data, query NoSQL databases, and perform predictive modeling;
  • guide students to the development of an open-source and reproducible big data analytics project, based on the analysis of real-world datasets.

Module 1: Big Data Analytics and Social Mining

In this module, analytical methods and processes are presented through exemplary cases studies in challenging domains, organized according to the following topics:

  • The Big Data Scenario and the new questions to be answered
  • Sports Analytics:
    1. Soccer data landscape and injury prediction
    2. Analysis and evolution of sports performance
  • Mobility Analytics
    1. Mobility data landscape and mobility data mining methods
    2. Understanding Human Mobility with vehicular sensors (GPS)
    3. Mobility Analytics: Novel Demography with mobile-phone data
  • Social Media Mining
    1. The social media data landscape: Facebook, Linked-in, Twitter, Last_FM
    2. Sentiment analysis. example from human migration studies
    3. Discussion on ethical issues of Big Data Analytics
  • Well-being&Now-casting
    1. Nowcasting influenza with retail market data
    2. Predicting well-being from human mobility patterns
  • Paper presentations by students

Module 2: Big Data Analytics Technologies

This module will provide to the students the technologies to collect, manipulate and process big data. In particular, the following tools will be presented:

  • Python for Data Science
  • The Jupyter Notebook: developing open-source and reproducible data science
  • MongoDB: fast querying and aggregation in NoSQL databases
  • GeoPandas: analyze geo-spatial data with Python
  • Scikit-learn: machine learning in Python
  • Keras: deep learning in Python

Module 3: Laboratory for Interactive Project Development

During the course, teams of students will be guided in the development of a big data analytics project. The projects will be based on real-world datasets covering several thematic areas. Discussions and presentation in class, at different stages of the project execution, will be performed.

  • 1st Mid Term: Data Understanding and Project Formulation
  • 2nd Mid Term: Model(s) construction and evaluation
  • 3rd Mid Term: Model interpretation/explanation
  • Exam: Final Project results


15/09 (Mod. 1) Introduction to the course, The Big Data scenario lesson1_introduction_to_the_course_2021.pdf

17/09 (Mod. 2) Python for Data Science and the Jupyter Notebook: developing open-source and reproducible data science

22/09 (Mod. 2) Data Exploration and Understanding practice in Python

24/09 (Mod. 3) Presentation of datasets for the project bda21_22_datasets_1_.pdf

Exam (Appelli)


Previous Big Data Analytics websites

bigdataanalytics/bda/start.txt · Ultima modifica: 24/09/2021 alle 12:25 (3 giorni fa) da Luca Pappalardo