====== Big Data Analytics A.A. 2021/22 ======

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.pappalardo@isti.cnr.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


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

====== Module 1: Big Data Analytics and Social Mining ======
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:   
    - Soccer data landscape and injury prediction
    - Analysis and evolution of sports performance
  * Mobility Analytics
    - Mobility data landscape and mobility data mining methods
    - Understanding Human Mobility with vehicular sensors (GPS)
    - Mobility Analytics: Novel Demography with mobile-phone data 
  * Social Media Mining
    - The social media data landscape: Facebook, Linked-in, Twitter, Last_FM
    - Sentiment analysis. example from human migration studies
    - Discussion on ethical issues of Big Data Analytics
  * Well-being&Now-casting
    - Nowcasting influenza with retail market data
    - 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

====== Calendar ======

15/09 (Mod. 1) Introduction to the course, The Big Data scenario {{ :bigdataanalytics:bda: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


===== Exam (Appelli) =====
TDA

====== Previous Big Data Analytics websites ======

[[bigdataanalytics:bda:bda2019|]]

[[bigdataanalytics:bda:bda2018|]]

[[bigdataanalytics:bda:bda2017|]]

[[bigdataanalytics:bda:bda2016|]]

[[bigdataanalytics:bda:bda2015|]]