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--- bigdataanalytics:bda:start [11/09/2019 alle 09:14 (5 anni fa)] – [Calendar] Luca Pappalardo
+++ bigdataanalytics:bda:start [04/11/2022 alle 12:21 (23 mesi fa)] (versione attuale) – Salvatore Ruggieri
@@ Linea 1: / Linea 1: @@
-<html>
+====== Big Data Analytics A.A. 2022/23 ======
-<!-- Google Analytics -->
-<script type="text/javascript" charset="utf-8">
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-})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
-ga('create', 'UA-34685760-1', 'auto', 'personalTracker', {'allowLinker': true});
+This year, the course 599AA Big Data Analytics (BDA) is replaced by [[http://didawiki.di.unipi.it/doku.php/geospatialanalytics/gsa/start|Geospatial Analytics]]. For any questions, please contact Luca Pappalardo (luca [dot] pappalardo [at] isti [dot] cnr [dot] it).
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-ga('personalTracker.send', 'pageview', 'ruggieri/teaching/bda/');
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+====== Previous Big Data Analytics websites ======
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-====== Big Data Analytics A.A. 2019/20 ======
-Instructors - Docenti:
+[[bigdataanalytics:bda:bda2021|]]
-  * **Fosca Giannotti, Luca Pappalardo**
-    * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa
-    * [[http://www-kdd.isti.cnr.it]]
-    * [[fosca.giannotti@isti.cnr.it]]
-    * [[luca.pappalardo@isti.cnr.it]]
-====== 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.
+[[bigdataanalytics:bda:bda2020|]]
-This course has three objectives:
-  * introducing to the emergent field of big data analytics and social mining;
+[[bigdataanalytics:bda:bda2019|]]
-  * 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 ======
-/09 (Mod. 1) Introduction to the course, The Big Data scenario {{ :bigdataanalytics:bda:mod1.introduction_bigdatalandscape_newquestions_.pdf |}}
-===== 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:bda:project.png?800|}}
-====== Previous Big Data Analytics websites ======
 [[bigdataanalytics:bda:bda2018|]]