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All lectures will be provided also remotely, through the Teams team named “599AA 21/22 - BIG DATA ANALYTICS [WDS-LM]”
Instructors:
Tutor:
Timetable
Dataset assignment: datasets have been assigned to teams, find your dataset here https://bit.ly/2YalEtI
Instructions for MidTerm 1: The first mid-term presentation (Data Understanding and Project Proposal) will be on October 20th (half of the teams) and October 22nd (rest of the teams).
Instructions for MidTerm 2: The second mid-term presentation (model(s) implementation and evaluation) will be on November 17th (half of the teams) and November 19th (rest of the teams).
Instructions for MidTerm3: The third mid term presentation (model interpretation and explanation) will be on December 15th and December 17th.
Paper presentation: they are scheduled on December 1st, 3rd, 10th.
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
29/09 (Mod. 2) Scikit-learn: programming tools for data mining (part 1) https://jovian.ai/jonpappalord/classification
01/10 (Mod. 2) Scikit-learn: programming tools for data mining (part 2) https://jovian.ai/jonpappalord/clustering
6/10 (Mod. 2) Geopandas and scikit-mobility: managing geographic data in Python (part 1)
8/10 (Mod. 2) Geopandas and scikit-mobility: managing geographic data in Python (part 2)
13/10 (Mod. 1) Case study 1: Injury prediction and how to deal with unbalanced datasets and perform feature selection: bda_2122_injury_forecasting.pdf
15/10 (Mod. 2) Feature selection in Python
20/10 (Mod. 3) MidTerm1
22/10 (Mod. 3) MidTerm1
27/10 (Mod. 3) Comments and discussion on first Mid Term 1 tips_mid_1_bda2122.pdf
29/10 (Mod. 1) Case Study 2: How to use Data Science to nowcast well-being bda_wellbeing.pdf
03/11 (Mod. 1) Case Study 3: Performance evaluation in sports
05/11 NO LESSON
10/11 (Mod. 2) Interpretations and Explanations 1: https://jovian.ai/jonpappalord/explanations ONLINE ONLY LESSON
12/11 (Mod. 2) Interpretations and Explanations 2
17/11 (Mod. 3) Mid Term2
19/11 (Mod.3) Mid Term2
01/12 (Mod. 3) Paper presentations
03/12 (Mod. 3) Paper presentations
10/12 (Mod. 3) Paper presentations
15/12 (Mod. 3) Mid Term 3
17/12 (Mod. 3) Mid Term 3
TDA