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bigdataanalytics:bda:start

Big Data Analytics A.A. 2018/19

Instructors - Docenti:

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 within Thursday, October 26th with your choice for three/four papers. We then assign you one of the papers considering your preferences.

Instructions for project proposal (October 26th):

  • presentation: 10 minutes (+ 5 minutes questions), send the pdf of the presentation to Luca Pappalardo by Thursday 25th.
  • report: 5 pages at most, summarize the data understanding and show your project proposal. Send the pdf of the report to Luca Pappalardo by Thursday 25th. In the report put the name of the dataset you are working on and the names of the members of the team.

Instructions for paper presentation (November 16th and 23th):

  • presentation: 7 minutes (+ 3 minutes questions), send the pdf of the presentation to Luca Pappalardo by the day before the presentation of your paper.
  • scheduling: date of presentation for each student: http://bit.ly/papers_scheduling

Instructions for project advancements report and presentation (November 26th):

  • presentation: 10 minutes (+ 3 minutes questions), send the pdf of the presentation to Luca Pappalardo by November 25th.
  • report: 10 pages at most, extend the previous report by adding details about the implementation of the solution to your analytical problem and its validation. Send me the extended report and the Python notebooks used to develop the solution by November 25th.

Instructions for final report and presentation (December 10th and 14th):

  • presentation: 20 minutes (+ 10 minutes questions), send the pdf of the presentation to Luca Pappalardo by December 10th, 2pm.
  • report: 20 pages at most, extend the previous report. Send me the extended report and the Python notebooks used to develop the solution by December 10th, 2pm.
  • check the date of your presentation here: http://bit.ly/2rm3P6Y

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 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:
    1. Soccer data landscape and injury prediction
    2. Analysis and evolution of sports performance
  • Mobility Analytics
    1. Mobility data landscape and mobility data mining methods
    2. Understanding Human Mobility with vehicular sensors (GPS)
    3. Mobility Analytics: Novel Demography with mobile-phone data
  • Social Media Mining
    1. The social media data landscape: Facebook, Linked-in, Twitter, Last_FM
    2. Sentiment analysis. example from human migration studies
    3. Discussion on ethical issues of Big Data Analytics
  • Well-being&Now-casting
    1. Nowcasting influenza with retail market data
    2. 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 mod1.introduction_bigdatalandscape_newquestions_.pdf

21/09 (Mod. 1) Big Data Analytics: new questions to be solved + Presentation of datasets

24/09 (Mod. 2) Python for Data Science: The Jupyter Notebook: developing open-source and reproducible data science

28/09 (Mod. 1) Soccer data landscape and players’ injury prediction

01/10 (Mod. 2) Scikit-learn: programming tools for data mining and analysis.

05/10 (Mod. 1) Analysis and evolution of sports performance

08/10 (Mod. 1) The mobility data landscape

12/10 (Mod. 1) Suspended

15/10 (Mod. 1) Mobility data mining methods (Patterns&Models)

19/10 (Mod. 1) Understanding Human Mobility with GPS - Case Studies

22/10 (Mod. 1) Urban Dynamics with mobile phone data

26/10 (Mod. 3) Data Understanding and Project Formulation

05/11 (Mod. 2) GeoPandas: analyse geo-spatial data with Python

09/11 (Mod. 1) Predicting well-being from human mobility patterns

12/11 (Mod. 2) MongoDB: fast querying and aggregation in NoSQL databases

16/11 (Mod. 3) Papers presentations from students

19/11 (Mod. 1) Nowcasting influenza with retail market data

23/11 (Mod. 3) Papers presentations from students

26/11 (Mod. 3) Mid Term Project Results

30/11 No lessons

03/12 (Mod. 1) The social media data landscape and social media mining methods

07/12 (Mod. 1) Sentiment analysis and Opinion Mining (Andrea Esuli)

10/12 (Mod. 3) Discussion on Ethical issues in Big Data Analytics and Final Project results

14/12 (Mod. 3) Final Project results

18/01 EXAM: 09:00 @ aula L1

08/02 EXAM: 09:00 @ aula L1

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:

Previous Big Data Analytics websites

bigdataanalytics/bda/start.txt · Ultima modifica: 05/12/2018 alle 13:57 (8 giorni fa) da Luca Pappalardo