Data Analytics

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).

Teacher:

Prof. Massimo Callisto De Donato

ESSE3 Link

Data Analytics - AY 2021/22

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:

MSc in Computer Science (LM-18)

Students Office hours:

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

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.

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.

Course Slides

Slides link

Webex link

Projects - link

Course YY 2020-21 (http://didattica.cs.unicam.it/old/doku.php?id=didattica:magistrale:eda:ay_2021:main)

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

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: