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bigdataanalytics:bda:start [11/11/2021 alle 16:37 (3 anni fa)] – [Calendar] Luca Pappalardobigdataanalytics:bda:start [04/11/2022 alle 12:21 (2 anni fa)] (versione attuale) – Salvatore Ruggieri
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-<html> +====== Big Data Analytics A.A. 2022/23 ======
-<!-- Google Analytics --> +
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-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/'); +
-setTimeout("ga('send','event','adjusted bounce rate','30 seconds')",30000); +
  
-</script> +====== Previous Big Data Analytics websites ======
-<!-- End Google Analytics --> +
-<!-- Global site tag (gtag.js) - Google Analytics --> +
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-    ga('personalTracker.send', 'event',  'BDA', 'Rs', fname); +
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-    ga('personalTracker.send', 'event',  'BDA', 'ZIPs', fname); +
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-====== Big Data Analytics A.A. 2021/22 ====== +
- +
-All lectures will be provided also remotely, through the Teams team named "599AA 21/22 - BIG DATA ANALYTICS [WDS-LM]" +
- +
-Instructors: +
-  * **Luca Pappalardo** +
-  * **Fosca Giannotti** +
-    * KDD Laboratory, ISTI-CNR, Università di Pisa, and Scuola Normale Superiore, Pisa +
-    * [[http://www-kdd.isti.cnr.it]] +
-    * [[[email protected]]] +
-    * [[[email protected]]] +
- +
-Tutor: +
-  * **Giuliano Cornacchia** +
-  * [[[email protected]]] +
- +
-Timetable +
-  * Wednesday 09:00 - 10:45 Aula Fib M1 +
-  * Friday 09:00 - 10:45 Aula Fib C1  +
- +
-**__Dataset assignment__**: datasets have been assigned to teams, find your dataset here https://bit.ly/2YalEtI +
- +
- +
-**Instructions for MidTerm 1**: The first mid-term presentation (Data Understanding and Project Proposal) will be on **October 20th** (half of the teams) and **October 22nd** (rest of the teams). +
-  * **presentation**: prepare a presentation describing the data understanding and a proposal of the problem you aim to solve. __Motivate your decisions and choices__ (e.g., which variables you deleted, how you deal with missing values and noise, the new variables you created, if you integrated your data with external datasets, etc.). __The presentation should last max. 20 minutes__ (+ 10 minutes questions) and __must be done running "live" a Colab notebook__; +
-  * **code**: provide the link to the notebook on Jovian with the code you used for all computations and plots. __Document adequately your notebooks using the markdown language.__ The notebook should be runnable without errors on Google Colab, so put in some blocks instructions to install additional libraries (if any) and instructions on the format the datasets should have in order to run the code correctly. +
-  * upload the material by **__Tuesday, October 19th__**, using the following form: https://forms.gle/BV2Drh9zJKSu1fFC8 +
- +
-**Instructions for MidTerm 2**: The second mid-term presentation (model(s) implementation and evaluation) will be on **November 17th** (half of the teams) and **November 19th** (rest of the teams). +
-  * **presentation**: he models you tried (e.g., Decision Trees, SVMs, etc.), the baselines used as a comparison (e.g., DummyClassifiers), how you perform the hyper-parameter tuning, the evaluation technique used (i.e., holdout, repeated holdout, cross-validation), and the metrics chosen to evaluate the performance of the models. __Motivate your decisions and choices__ (e.g., which evaluation metrics you chose, how you deal with the unbalancing of the dataset).  __The presentation should last max. 20 minutes__ (+ 10 minutes questions) and __must be done running "live" a Colab notebook__; +
-  * **code**: provide the link to the notebook on Jovian with the code you used for all computations and plots. __Document adequately your notebooks using the markdown language.__ The notebook should be runnable without errors on Google Colab, so put in some blocks instructions to install additional libraries (if any) and instructions on the format the datasets should have in order to run the code correctly. Upload also the link to the notebook of the previous mid-term, with the modifications suggested. +
-  * upload the material by **__November 16th__**. +
- +
- +
-** Instructions for MidTerm3**: The third mid term presentation (model interpretation and explanation) will be on December 15th and December 17th. +
- +
-**Paper presentation**: they are scheduled on December 1st, 3rd, 10th.  +
-  * Papers assignment: https://docs.google.com/spreadsheets/d/1o7rkBdHSD-XVK0V0SMxAuWMHzatmim86oEn7jSWqUIc/edit?usp=sharing +
-  * Link to papers: https://docs.google.com/spreadsheets/d/1OXlQx7wvGLUFoqUcxE9yBQqaPs4CS1xeg8oRhq9qhGE/edit?usp=sharing +
-  * Each student will present, during a talk of 7 minutes **at most**, a paper on Big Data Analytics. During the presentation (with slides), you should highlight the following aspects: the data set used, the feature engineering and/or selection (if any), the problem addressed, the models/algorithms used to solve the problem, and finally the explanations of the model constructed (if any). +
-====== 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 +
-__**ONLINE ONLY LESSON**__ +
- +
-12/11 (Mod. 2) Interpretations and Explanations 2: https://jovian.ai/jonpappalord/explanations2 +
-__**ONLINE ONLY LESSON**__ +
- +
-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 +
- +
-01/12 (Mod. 3) Paper presentations +
- +
-03/12 (Mod. 3) Paper presentations +
- +
-10/12 (Mod. 3) Paper presentations +
- +
-15/12 (Mod. 3) Mid Term 3 +
- +
-17/12 (Mod. 3) Mid Term 3 +
-===== Exam (Appelli) ===== +
- TDA +
- +
-====== Previous Big Data Analytics websites ======+
  
 [[bigdataanalytics:bda:bda2020|]] [[bigdataanalytics:bda:bda2020|]]
bigdataanalytics/bda/start.1636648679.txt.gz · Ultima modifica: 11/11/2021 alle 16:37 (3 anni fa) da Luca Pappalardo
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