====== Technologies for Big Data Management ======
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===== News =====
* **Projects:** the list of the **projects** is available at following {{https://drive.google.com/drive/folders/1gFg_sGXI-29ClcwHuqW9BfHOXKHeEYhG | link}}.
* **No lesson on 14 November:** no lesson on this day.
* **TBDM on Telegram**: students can join to the Telegram group with the following {{https://t.me/+_If79QPfFR42NzQ0 | link}}.
* **No lesson on 11 November:** no lesson on this day.
* **No lesson on 31 October:** no lesson on this day.
* **Webex recordings**: recordings are available at the following [[https://docs.google.com/document/d/1JYz2CP5fzmcMjYVzYw8S4NNfHTirVYdF4AwWyJ9C14g/view | link]].
* **Course start**: the course is planned to start the **30th of September** - AB2 - Polo Lodovici A.
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===== General Info =====
**Teacher**:
* [[https://didattica.unicam.it/Guide/PaginaDocente.do?docente_id=3138|Prof. Massimo Callisto De Donato]]
* Webex room: https://unicam.webex.com/meet/massimo.callisto
* Email: massimo.callisto[at]unicam.it
**ESSE3 Link**
* [[|Techonologies for Big Data Management - AY 2024/25]]
**Scheduling of Lectures**:
* Scheduling is available at the following [[:didattica:ay2425:orario_en|link]]
**Degrees**:
* [[didattica:mscs|MSc in Computer Science (LM-18)]]
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===== 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.
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===== 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.
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===== Study material =====
**Course Slides**
* Slides {{ https://drive.google.com/drive/folders/1tcpUqLRvOnPeAJXHSAqVpLvfv6vZ1DvI?usp=sharing | link}}
* Github {{ https://github.com/massimocallisto | link }}
* Webex recordings {{ https://docs.google.com/document/d/1JYz2CP5fzmcMjYVzYw8S4NNfHTirVYdF4AwWyJ9C14g/edit?usp=sharing | link}}
* Projects {{ https://drive.google.com/drive/folders/1gFg_sGXI-29ClcwHuqW9BfHOXKHeEYhG | link}}
* TBDM [[http://didattica.cs.unicam.it/doku.php?id=didattica:ay2324:tbdm:main | A.A.2023/24]]
* **Reference materials**
* Slides course.
* Material provided by the teacher.
* Examples
* ...
* Other materials
* ...
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===== Exams =====
**Exam Dates A.Y. 2024/2025 (tentative)**
* 03/02/2025
* 24/02/2025
* 23/06/2025
* 07/07/2025
* 28/07/2025
* 08/09/2025
* 29/09/2025
* 02/02/2026
* 16/02/2026
**Exam rules**:
* Writing Examination
* 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:
- Project description / objectives
- Methodology and technology being used
- Technical implementation
- Achieved results
- future improvements
** Exam Results **
* See news section