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

Instructors - Docenti:

• Luca Pappalardo
  • KDD Laboratory, Università di Pisa and ISTI-CNR, Pisa
  • http://www-kdd.isti.cnr.it
  • luca [dot] pappalardo [at] isti [dot] cnr [dot] it

Tutor:

• Giuliano Cornacchia
• KDD Laboratory, Università di Pisa and ISTI-CNR, Pisa
• http://www-kdd.isti.cnr.it
• giuliano.cornacchia@phd.unipi.it

Timetable

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

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 a open-source and reproducible big data analytics project, based on the analyis of real-world datasets.

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

• The Big Data Scenario and the new questions to be answered
• Sport 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

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

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_bda2021.pdf

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

• How to install Jupyter notebook: https://jupyter.readthedocs.io/en/latest/install.html
• Python notebooks: http://bit.ly/bda2021_notebooks_1

TDA

Big Data Analytics A.A. 2019/20

Big Data Analytics A.A. 2018/19

Big Data Analytics A.A. 2017/18

Big Data Analytics A.A. 2016/17

Big Data Analytics A.A. 2015/16