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<html> <!– 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}); ga('personalTracker.require', 'linker'); ga('personalTracker.linker:autoLink', ['pages.di.unipi.it', 'enforce.di.unipi.it', 'didawiki.di.unipi.it'] ); ga('personalTracker.require', 'displayfeatures'); ga('personalTracker.send', 'pageview', 'ruggieri/teaching/bda/'); setTimeout(“ga('send','event','adjusted bounce rate','30 seconds')”,30000); </script> <!– End Google Analytics –> <!– Global site tag (gtag.js) - Google Analytics –> <script async src=“https://www.googletagmanager.com/gtag/js?id=G-LPWY0VLB5W”></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-LPWY0VLB5W'); </script> <!– 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. 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 * luca [dot] pappalardo [at] isti [dot] cnr [dot] it * fosca [dot] giannotti [at] isti [dot] cnr [dot] it Tutor: * Giuliano Cornacchia * giuliano [dot] cornacchia [at] phd [dot] unipi [dot] it 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 and November 19th. Instructions for MidTerm3: The third mid term presentation (model interpretation and explanation) will be on December 15th and December 17th. Paper presentation: December 1st, 3rd, 10th. ====== 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 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: 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: data_python_for_data_science.zip 24/09 (Mod. 3) Presentation of datasets for the project 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: 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 tips_mid_1_bda2122.pdf 29/10 (Mod. 1) Case Study 2: How to use Data Science to nowcast well-being bda_wellbeing.pdf ===== Exam (Appelli) ===== TDA ====== Previous Big Data Analytics websites ====== Big Data Analytics A.A. 2020/21 Big Data Analytics A.A. 2019/20 Big Data Analytics A.A. 2018/19 Big Data Analytics A.A. 2017/18 Big Data Analytics A.A. 2016/17 Big Data Analytics A.A. 2015/16