NOTICE: ON MONDAY, SEPTEMBER 21st, ALL LESSONS ARE SUSPENDED BECAUSE OF THE ELECTION DAY IN ITALY.
WARNING: All lectures of the First Semester of the academic year 2020/21, until 31/12/2020, will be provided exclusively remotely, through the Teams team named “599AA 20/21 - BIG DATA ANALYTICS [WDS-LM]” (https://bit.ly/35yJ65c).
ATTENZIONE: Tutte le lezioni frontali del Primo Semestre dell’a.a. 2020/21, fino al 31/12/2020, verranno erogate esclusivamente in modalità a distanza, attraverso il canale Teams “599AA 20/21 - BIG DATA ANALYTICS [WDS-LM]” (https://bit.ly/35yJ65c).
Instructors - Docenti:
Fill the doodle with your preference for time/day during the week (forgot about dates, just care about the day of the week and the time of the day): https://doodle.com/poll/bwt8aa5zyczn8p6d
Pre-registration to the course: fill the form with your name and surname, email, skills and languages (the results of the form will help building up teams), by Wed, September 16th: https://forms.gle/tzxKRP4aidKBpk8E9
Team Registration: build up teams of 3 or 4 students and register your team here, by September 23th: https://forms.gle/rbsV4dF6RuAnCBWz9
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:
In this module, analytical methods and processes are presented thought exemplary cases studies in challenging domains, organized according to the following topics:
This module will provide to the students the technologies to collect, manipulate and process big data. In particular the following tools will be presented:
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.
14/09 (Mod. 1) Introduction to the course, The Big Data scenario lesson1_introduction_to_the_course_bda2021.pdf
15/09 (Mod. 2) Python for Data Science and the Jupyter Notebook: developing open-source and reproducible data science
21/09 No Lesson (Election Day in Italy)
28/09 (Mod. 2) Geopandas and scikit-mobility: analyze trajectory data in Python: geopandas.zip
29/09 (Mod. 2) PyMongo and MongoDB: fast querying and aggregation in NoSQL databases: mongodb.zip
05/10 (Mod. 1) Soccer data landscape and injury prediction
06/10 No Lesson (SocInfo2020 conference)
12/10 (Mod. 1) Performance evaluation: from human evaluations to data-driven algorithms
13/10 (Mod. 1) Nowcasting well-being with Big Data
19/10 (Mod. 3) 1st Mid Term - first group of teams
20/10 (Mod. 3) 1st Mid Term - second group of teams
26/10 (Mod. 3) Discussion and group working on projects
27/10 (Mod. 3) Discussion and group working on projects
02/11 (Mod. 1) Forecasting influenza with retail market data
03/11 (Mod. 1) Trustworthy data mining
16/11 (Mod. 3) 2nd Mid Term - first group of teams
17/11 (Mod. 3) 2nd Mid Term - second group of teams
23/11 (Mod. 3) Discussion and group working on projects
24/11 (Mod. 3) Discussion and group working on projects
30/11 (Mod. 3) Paper presentations
01/12 (Mod. 3) Paper presentations
07/12 (Mod. 3) 3rd Mid Term - first and second group of teams