All lectures will be provided also remotely, through the Teams team named “599AA 21/22 - BIG DATA ANALYTICS [WDS-LM]”
List of registered students: https://docs.google.com/spreadsheets/d/1oG2EpbgtIQhKx1UgPMcePOgKZFSgT-W5tMqpwAnCHt0/edit?usp=sharing
Team Registration: build up teams of 3 or 4 students and register your team here, by September 29th: https://forms.gle/UWLUp11QNVCPBgSZ6. 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: https://forms.gle/CLNoUfTAa79aBKnL6
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 through 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.
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