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====== Big Data Analytics A.A. 2018/19 ======
Instructors - Docenti:
* Fosca Giannotti, Luca Pappalardo
* KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa
* http://www-kdd.isti.cnr.it
* fosca [dot] giannotti [at] isti [dot] cnr [dot] it
* luca [dot] pappalardo [at] isti [dot] cnr [dot] it
Notice: you can find a list of the papers to read at this link: http://bit.ly/bda_papers. Send an email to Luca Pappalardo within Thursday, October 26th with your choice for three/four papers. We then assign you one of the papers considering your preferences.
Instructions for project proposal (October 26th):
* presentation: 10 minutes (+ 5 minutes questions), send the pdf of the presentation to Luca Pappalardo by Thursday 25th.
* report: 5 pages at most, summarize the data understanding and show your project proposal. Send the pdf of the report to Luca Pappalardo by Thursday 25th. In the report put the name of the dataset you are working on and the names of the members of the team.
Instructions for paper presentation (November 16th and 23th):
* presentation: 7 minutes (+ 3 minutes questions), send the pdf of the presentation to Luca Pappalardo by the day before the presentation of your paper.
* scheduling: date of presentation for each student: http://bit.ly/papers_scheduling
====== 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: programming tools for data mining and analysis
* M-Atlas: a toolkit for mobility data mining
====== 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.
* Data Understanding and Project Formulation
* Mid Term Project Results
* Final Project results
====== Calendar ======
17/09 (Mod. 1) Introduction to the course, The Big Data scenario mod1.introduction_bigdatalandscape_newquestions_.pdf
21/09 (Mod. 1) Big Data Analytics: new questions to be solved + Presentation of datasets
* list of datasets: http://bit.ly/bda_list_datasets
* slides: http://bit.ly/bda18_datasets_slides)
24/09 (Mod. 2) Python for Data Science: 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/bda_notebooks_1
28/09 (Mod. 1) Soccer data landscape and players’ injury prediction
* slides: http://bit.ly/bda_sports_data_injury
* paper: http://bit.ly/plos_injury
01/10 (Mod. 2) Scikit-learn: programming tools for data mining and analysis.
* Python notebooks: http://bit.ly/bda_notebooks_2
05/10 (Mod. 1) Analysis and evolution of sports performance
* Slides: http://bit.ly/bda_soccer_evaluation
08/10 (Mod. 1) The mobility data landscape
* Slides: part1.mobilitydataanaysis1-foundations_.pdf
12/10 (Mod. 1) Suspended
15/10 (Mod. 1) Mobility data mining methods (Patterns&Models)
* Slides: part1.mobilitydataanaysis2-patterns_models.pdf
19/10 (Mod. 1) Understanding Human Mobility with GPS - Case Studies
* Slides: part1.mobilitydataanaysis3-humanmobility-gps.pdf
* Slides: part1.mobilitydataanaysis5-casesudies.pdf
22/10 (Mod. 1) Urban Dynamics with mobile phone data
* Slides: part1.mobilitydataanaysis4-citydinamics-gsm.pdf
26/10 (Mod. 3) Data Understanding and Project Formulation
05/11 (Mod. 2) GeoPandas: analyse geo-spatial data with Python
* Python notebook: bda_geopandas.zip
09/11 (Mod. 1) Predicting well-being from human mobility patterns
12/11 (Mod. 2) MongoDB: fast querying and aggregation in NoSQL databases
16/11 (Mod. 3) Papers presentations from students
19/11 (Mod. 1) Nowcasting influenza with retail market data
23/11 (Mod. 3) Papers presentations from students
26/11 (Mod. 3) Mid Term Project Results
30/11 No lessons
03/12 (Mod. 1) The social media data landscape and social media mining methods
07/12 (Mod. 1) Sentiment analysis: examples from Human Migration studies
10/12 (Mod. 3) Discussion on Ethical issues in Big Data Analytics and Final Project results
14/12 (Mod. 3) Final Project results
18/01 EXAM: 09:00 @ aula L1
08/02 EXAM: 09:00 @ aula L1
===== Exam =====
The two mid-terms will be 40% of the final grade, the remaining 60% is the evaluation of the Project and the Discussion (prepare some Slides to present your project).
There is the possibility to do the a final test about technologies if the Mid-Terms are not sufficient.
The following table describe the expected content of a project:
====== Previous Big Data Analytics websites ======
http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2017
http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2016
http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2015