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bigdataanalytics:bda:start [08/10/2018 alle 09:02 (6 anni fa)]
Luca Pappalardo [Big Data Analytics A.A. 2018/19] 		bigdataanalytics:bda:start [04/11/2022 alle 12:21 (18 mesi fa)] (versione attuale)
Salvatore Ruggieri
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-<html> +====== Big Data Analytics A.A. 2022/23 ======
-<!-- Google Analytics --> +
-<script type="text/javascript" charset="utf-8"> +
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ +
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), +
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) +
-})(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).
-ga('personalTracker.require', 'linker'); +
-ga('personalTracker.linker:autoLink', ['pages.di.unipi.it', 'enforce.di.unipi.it', 'didawiki.di.unipi.it'] ); +
-   +
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-ga('personalTracker.send', 'pageview', 'ruggieri/teaching/bda/'); +
-setTimeout("ga('send','event','adjusted bounce rate','30 seconds')",30000); +
  
-</script> +====== Previous Big Data Analytics websites ======
-<!-- End Google Analytics --> +
-<!-- Capture clicks --> +
-<script> +
-jQuery(document).ready(function(){ +
-  jQuery('a[href$=".pdf"]').click(function() { +
-    var fname = this.href.split('/').pop(); +
-    ga('personalTracker.send', 'event',  'BDA', 'PDFs', fname); +
-  }); +
-  jQuery('a[href$=".r"]').click(function() { +
-    var fname = this.href.split('/').pop(); +
-    ga('personalTracker.send', 'event',  'BDA', 'Rs', fname); +
-  }); +
-  jQuery('a[href$=".zip"]').click(function() { +
-    var fname = this.href.split('/').pop(); +
-    ga('personalTracker.send', 'event',  'BDA', 'ZIPs', fname); +
-  }); +
-  jQuery('a[href$=".mp4"]').click(function() { +
-    var fname = this.href.split('/').pop(); +
-    ga('personalTracker.send', 'event',  'BDA', 'Videos', fname); +
-  }); +
-  jQuery('a[href$=".flv"]').click(function() { +
-    var fname = this.href.split('/').pop(); +
-    ga('personalTracker.send', 'event',  'BDA', 'Videos', fname); +
-  }); +
-}); +
-</script> +
-</html> +
-====== Big Data Analytics A.A. 2018/19 ======+
  
-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]]   +
  
 +[[bigdataanalytics:bda:bda2020|]]
  
-**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 with your choice for three papers. We then assign you one of the papers. +[[bigdataanalytics:bda:bda2019|]]
-====== 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:bda2018|]]
-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 {{ :bigdataanalytics:bda: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 +
- +
-08/10 (Mod. 1) The mobility data landscape and mobility data mining methods +
- +
-12/10 (Mod. 1) Soccer Data Challenge +
- +
-15/10 (Mod. 1) Understanding Human Mobility with GPS +
- +
- +
-19/10 (Mod. 3) Data Understanding and Project Formulation +
- +
-22/10 (Mod. 2) MongoDB: fast querying and aggregation in NoSQL databases +
- +
- +
-05/11 (Mod. 2) GeoPandas: analyze 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. 1) papers presentation    +
- +
- +
-19/11 (Mod. 1) papers presentation +
- +
- +
-23/11 (Mod. 3) Mid Term Project Results +
- +
- +
-26/11 (Mod. 1) The social media data landscape and social media mining methods +
- +
-30/11 No lessons +
- +
-03/12 (Mod. 1) Sentiment analysis: examples from Human Migration studies +
- +
- +
-07/12 (Mod. 1) Discussion on Ethical issues in Big Data Analytics +
- +
-10/12 (Mod. 3) 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:bda:project.png?800|}} +
- +
-====== Previous Big Data Analytics websites ======+
  
-http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2017+[[bigdataanalytics:bda:bda2017|]]
  
-http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2016+[[bigdataanalytics:bda:bda2016|]]
  
-http://didawiki.di.unipi.it/doku.php/bigdataanalytics/bda/bda2015+[[bigdataanalytics:bda:bda2015|]]
bigdataanalytics/bda/start.1538989320.txt.gz · Ultima modifica: 08/10/2018 alle 09:02 (6 anni fa) da Luca Pappalardo

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