Table of Contents

Data Analytics

News

  • exam 29/07/2022 results: results are available here .
  • Exam rules update: students must confirm their presence to the next exam sessions at least 5 days before the exam date. Confirmation must be send to by mail to the teacher, esse3 registration is not sufficient.
  • exam 01/07/2022 results: results are available here .
  • Exam session update: the exam planned for the 16th of June 2022 has been changed to the 1st of July 2022, 14:30 aula AB2.
  • Exam rules update: students must confirm their presence to the next exam sessions at least 5 days before the exam date. Confirmation must be send to by mail to the teacher, esse3 registration is not sufficient.
  • exam 25/02/2022 results: results are available here .
  • exam 14/02/2022 results: results are available here .
  • exam update: the exam planned for the 11th of february has been rescheduled on the 14th of february, 2:00pm at Polo Ludovici AB1.
  • lecture of 10th of january 2022 will be online: last lecture will be online via webex at 14:15.
  • Rilevazione delle opinioni e della soddisfazione degli studenti, aa 2021/22: students are invaited to submit the survey.
  • No lesson today 17th of December.
  • Project update: project link has been published. Project discussion rules: for the project discussion students have to publish the final code to a public github repository (or similar) along with the necessary documnatation (pre-requisite, installation, configuration, usage, etc). Moreover a set of slides have to be presented in that day along with a practical demonstration of the running code. Slides should cover the following points:
    1. Project description / objectives
    2. Methodology and technology being used
    3. Technical implementation
    4. Achieved results
    5. Possible future improvements
  • Course update: lecture of 29/11/2021 will be done online only via webex.
  • No lecture 12/11/2020: lecture suspended.
  • Course start: the first lesson is planned for 4th of October 2020, at Polo Ludovici (LB1).

General Info

Teacher:

ESSE3 Link

Scheduling of Lectures:

  • Scheduling is available at the following link
  • 42 h - lectures, exercise sessions
  • Schedule on: Monday at LB1 room-new Polo Ludovici - 14:00 to 16:00 / Friday at AB1 Polo Ludovici - 14:00 to 16:00

Degrees:

Students Office hours:

  • Send an e-mail to the teacher to fix an appointment.

Course Objectives

  • knowledge about business data analysis and modern scenarios such as the Internet of Things.
  • Understanding of main differences between classical methods in data analysis and new modern scenarios.
  • Knowledge and expertise on Big Data methodologies and technologies, basic principles and concepts, techniques that enable data analysis and management.
  • Knowledge of the main Big Data technological frameworks and application in real case studies.
  • Highlight some Intelligent Data Analysis techniques.

Syllabus

  • Introduction to enterprise and data management.
  • Analysis of scenarios and contexts of data generation: from the inter-organizational model to the Internet of things.
  • Introduction to the classic data analysis techniques: ETL (Extract, Transform, Load), Business Intelligence, Reporting Tools.
  • Methodologies and technologies for the management and analysis of large amounts of data: introduction to the Big Data model, concepts, principles and technological frameworks.
  • Data Analytics methodologies and techniques: batch analysis models, streaming computation.
  • Data Analytics evolution towards intelligent data understanding models.

Study material

Course Slides

  • Reference materials
    • Slides course.
    • Material provided by the teacher.
    • Examples
      • …
    • Other materials
      • …

Exams

Exam Dates A.Y. 2021/2022 (tentative)

  • 14/02/2022
  • 25/02/2022
  • 23/05/2022
  • 17/06/2022
  • 25/07/2022
  • 05/09/2022
  • 30/09/2022
  • 06/02/2023
  • 27/02/2023

Exam rules:

  • Writing Examination on the topics of the course
  • Project lab (max 3 member per team)

For the discussion students have to publish the final code to a public github repository (or similar) along with the necessary documnatation (pre-requisite, installation, configuration, usage, etc). Moreover a set of slides have to be presented in that day along with a practical demonstration of the running code. Slides should cover the following points:

  1. Project description / objectives
  2. Methodology and technology being used
  3. Technical implementation
  4. Achieved results
  5. Possible future improvements

Exam Results

  • See news section