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bigdataanalytics:bda:start [07/06/2022 alle 08:02 (3 anni fa)] – [Big Data Analytics A.A. 2021/22] Luca Pappalardobigdataanalytics:bda:start [04/11/2022 alle 12:21 (2 anni fa)] (versione attuale) – Salvatore Ruggieri
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 ====== Big Data Analytics A.A. 2022/23 ====== ====== Big Data Analytics A.A. 2022/23 ======
  
-This year, the course 599AA Big Data Analytics (BDA) is replaced by 783AA Geospatial Analytics (GSA). For any questions, please contact Luca Pappalardo (luca [dot] pappalardo [at] isti [dot] cnr [dot] it). +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).
-====== 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 an open-source and reproducible big data analytics project, based on the analysis of real-world datasets.  +
- +
-====== Module 1: Big Data Analytics and Social Mining ====== +
-In this module, analytical methods and processes are presented through exemplary cases studies in challenging domains, organized according to the following topics:  +
- +
-  * The Big Data Scenario and the new questions to be answered +
-  * Sports 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: machine learning in Python +
-  * Keras: deep learning in Python +
- +
- +
-====== 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.  +
- +
-  * 1st Mid Term: Data Understanding and Project Formulation +
-  * 2nd Mid Term: Model(s) construction and evaluation +
-  * 3rd Mid Term: Model interpretation/explanation +
-  * Exam: Final Project results +
- +
-====== Calendar ====== +
- +
-15/09 (Mod. 1) Introduction to the course, The Big Data scenario {{ :bigdataanalytics:bda: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 +
-  * How to install Jupyter notebook: https://jupyter.readthedocs.io/en/latest/install.html +
-  * Python notebooks: https://jovian.ai/jonpappalord/collections/bda-2021-2022 +
-  * datasets: {{ :bigdataanalytics:bda:data_python_for_data_science.zip |}} +
- +
-22/09 (Mod. 2) Data Exploration and Understanding practice in Python +
-  * Python notebooks: https://jovian.ai/jonpappalord/collections/bda-2021-2022 +
-  * datasets: {{ :bigdataanalytics:bda:data_python_for_data_science.zip |}} +
- +
-24/09 (Mod. 3) Presentation of datasets for the project {{ :bigdataanalytics:bda: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) +
-  * datasets: https://bit.ly/301XRwF +
-  * code: https://jovian.ai/jonpappalord/bda-geopandas +
- +
-8/10 (Mod. 2) Geopandas and scikit-mobility: managing geographic data in Python (part 2) +
-  * https://jovian.ai/jonpappalord/collections/scikit-mobility-tutorial +
- +
-13/10 (Mod. 1) Case study 1: Injury prediction and how to deal with unbalanced datasets and perform feature selection: {{ :bigdataanalytics:bda:bda_2122_injury_forecasting.pdf |}} +
-  * Prevedere è meglio che curare: AI al servizio dello sport https://www.youtube.com/watch?v=ZrTSLCB7ZLg +
- +
- +
-15/10 (Mod. 2) Feature selection in Python  +
-  * notebook: https://jovian.ai/jonpappalord/feature-selection +
-  * dataset1: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009/version/2 +
-  * dataset2: https://www.kaggle.com/andrewmvd/heart-failure-clinical-data +
- +
-20/10 (Mod. 3) MidTerm1 +
-  * BigData-Islanders +
-  * WeMine +
-  * cpu_in_flames +
- +
-22/10 (Mod. 3) MidTerm1 +
-  * How I Met Your Big Data +
-  * SLM +
-  * The Missing Values +
- +
-27/10 (Mod. 3) Comments and discussion on first Mid Term 1 {{ :bigdataanalytics:bda:tips_mid_1_bda2122.pdf |}} +
- +
-29/10 (Mod. 1) Case Study 2: How to use Data Science to nowcast well-being {{ :bigdataanalytics:bda:bda_wellbeing.pdf |}} +
- +
-03/11 (Mod. 1) Case Study 3: Performance evaluation in sports  +
-  * {{ :bigdataanalytics:bda:bda_2122_evaluting_soccer_performance.pdf |}} +
-  * {{ :bigdataanalytics:bda:bda_2122_performance_evaluation.pdf |}} +
- +
-05/11 NO LESSON +
- +
-10/11 (Mod. 2) Interpretations and Explanations 1: https://jovian.ai/jonpappalord/explanations +
- +
-12/11 (Mod. 2) Interpretations and Explanations 2: https://jovian.ai/jonpappalord/explanations2 +
- +
-17/11 (Mod. 3) Mid Term2 +
-  * How I Met Your Big Data +
-  * WeMine +
-  * The Missing Values +
- +
-19/11 (Mod.3) Mid Term2 +
-  * BigData-Islanders +
-  * SLM +
-  * cpu_in_flames +
- +
-24/11 NO LESSON +
- +
-26/11 NO LESSON +
- +
-01/12 (Mod. 3) Paper presentations +
-  * BigData-Islanders  +
-  * SLM +
- +
-03/12 (Mod. 3) Paper presentations +
-  * cpu_in_flames +
-  * The Missing Values +
- +
-10/12 (Mod. 3) Paper presentations +
-  * How I met your Big Data +
-  * WeMine +
- +
-15/12 (Mod. 3) Mid Term 3 +
-  * How I Met Your Big Data +
-  * BigData-Islanders +
-  * cpu_in_flames +
- +
-17/12 (Mod. 3) Mid Term 3 +
-  * WeMine +
-  * SLM +
-  * The Missing Values +
-===== Exam (Appelli) ===== +
-  - Jan 26th, 2022 +
-  - Feb 11th, 2022+
  
 ====== Previous Big Data Analytics websites ====== ====== Previous Big Data Analytics websites ======
bigdataanalytics/bda/start.1654588935.txt.gz · Ultima modifica: 07/06/2022 alle 08:02 (3 anni fa) da Luca Pappalardo
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