bigdataanalytics:bda:start
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bigdataanalytics:bda:start [16/10/2018 alle 14:00 (6 anni fa)] – [Calendar] Luca Pappalardo | bigdataanalytics:bda:start [04/11/2022 alle 12:21 (23 mesi fa)] (versione attuale) – Salvatore Ruggieri | ||
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- | < | + | ====== Big Data Analytics A.A. 2022/23 ====== |
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- | ga(' | + | This year, the course 599AA Big Data Analytics |
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- | ====== Big Data Analytics | + | |
- | Instructors - Docenti: | + | [[bigdataanalytics:bda: |
- | * **Fosca Giannotti, Luca Pappalardo** | + | |
- | * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa | + | |
- | * [[http:// | + | |
- | * [[fosca.giannotti@isti.cnr.it]] | + | |
- | * [[luca.pappalardo@isti.cnr.it]] | + | |
+ | [[bigdataanalytics: | ||
- | **Notice**: you can find a list of the papers to read at this link: http:// | + | [[bigdataanalytics:bda:bda2019|]] |
- | ====== Learning goals ====== | + | |
- | In our digital society, every human activity is mediated by information technologies, | + | [[bigdataanalytics: |
- | 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& | + | |
- | - 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: | + | |
- | * 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 {{ : | + | |
- | + | ||
- | + | ||
- | 21/09 (Mod. 1) Big Data Analytics: new questions to be solved + Presentation of datasets | + | |
- | * list of datasets: http:// | + | |
- | * slides: http:// | + | |
- | + | ||
- | + | ||
- | 24/09 (Mod. 2) Python for Data Science: The Jupyter Notebook: developing open-source and reproducible data science | + | |
- | * How to install Jupyter notebook: https:// | + | |
- | * Python notebooks: http:// | + | |
- | + | ||
- | + | ||
- | 28/09 (Mod. 1) Soccer data landscape and players’ injury prediction | + | |
- | * slides: http:// | + | |
- | * paper: http:// | + | |
- | + | ||
- | 01/10 (Mod. 2) Scikit-learn: | + | |
- | * Python notebooks: http:// | + | |
- | + | ||
- | 05/10 (Mod. 1) Analysis and evolution of sports performance | + | |
- | * Slides: http:// | + | |
- | + | ||
- | 08/10 (Mod. 1) The mobility data landscape | + | |
- | * Slides: | + | |
- | + | ||
- | 12/10 (Mod. 1) // | + | |
- | + | ||
- | 15/10 (Mod. 1) Mobility data mining methods (Patterns& | + | |
- | * Slides: {{ : | + | |
- | + | ||
- | 19/10 (Mod. 1) Understanding Human Mobility with GPS - Case Studies | + | |
- | * Slides: {{ : | + | |
- | * Slides: {{ : | + | |
- | + | ||
- | 22/10 (Mod. 1) Urban Dynamics with mobile phone data | + | |
- | * Slides: | + | |
- | + | ||
- | 26/10 (Mod. 3) **Data Understanding and Project Formulation** | + | |
- | + | ||
- | 05/11 (Mod. 2) GeoPandas: analyse geo-spatial data with Python | + | |
- | + | ||
- | 09/11 (Mod. 1) Predicting well-being from human mobility patterns | + | |
- | + | ||
- | 12/11 (Mod. 1) Nowcasting influenza with retail market data | + | |
- | + | ||
- | 16/11 (Mod. 3) **Papers presentations from students** | + | |
- | + | ||
- | 19/11 (Mod. 2) MongoDB: fast querying and aggregation in NoSQL databases | + | |
- | + | ||
- | 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** | + | |
- | + | ||
- | + | ||
- | 12/01 14,00 @ CNR (Entrance 20 - Room C36b) - Exam | + | |
- | + | ||
- | ===== 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: | + | |
- | {{:bigdataanalytics: | + | |
- | + | ||
- | ====== Previous Big Data Analytics websites ====== | + | |
- | http:// | + | [[bigdataanalytics:bda:bda2017|]] |
- | http:// | + | [[bigdataanalytics:bda:bda2016|]] |
- | http:// | + | [[bigdataanalytics:bda:bda2015|]] |
bigdataanalytics/bda/start.1539698423.txt.gz · Ultima modifica: 16/10/2018 alle 14:00 (6 anni fa) da Luca Pappalardo