didattica:ay2223:tbdm:main [Computer Science @ Unicam]

====== Technologies for Big Data Management ======
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===== News =====
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  * <wrap em>**Exam results 09/01/2024**</wrap>: result are available {{ https://drive.google.com/file/d/1rB-3E-IbbeAGsQTY5GUzRbrYW9TeoYKN/view?usp=sharing | here}}.
  * <wrap em>**Exam results 26/05/2023**</wrap>: result are available {{ https://drive.google.com/file/d/1prqdaZ_z6yM97ltc2ccgplOa-hwBtuII/view?usp=sharing | here}}.
  * <wrap em>**Exam of 26th of May update**</wrap>: the exam will be held in AB3 room from 14:30.
  * <wrap em>**Exam results 27/02/2023**</wrap>: result are available {{ https://drive.google.com/file/d/14PbqOlPmu_36HK3c-EZrdNrB2OL7N38F/view?usp=sharing | here}}.
  * <wrap em>**Exam results 20/02/2023**</wrap>: result are available {{ https://drive.google.com/file/d/19yF5nnv5GbnPhN-0LuBMJI0i9QbQkY1c/view?usp=sharing | here}}.
  * <wrap em>**Exam results 09/02/2023**</wrap>: result are available {{ https://drive.google.com/file/d/15wlJSLFEoABpimSPbuvBv3gLY3O8XJXC/view?usp=sharing | here}}.
  * <wrap em>**Lecture update**</wrap>: next lecture will be the 16th of January.
  * <wrap em>**No lecture 16/12/2022**</wrap>: next lecture will be the 19th of December.
  * <wrap em>**No lecture 02/12/2022**</wrap>: next lecture will be the 5th of December.
  * <wrap em>**Projects update**</wrap>: projects can be found at the following {{https://drive.google.com/drive/folders/1LDC2VnGat9QaFn-JCgpEHDobEUKM3KE6?usp=sharing | link }} **//(in progress)//**
  * <wrap em>**Lectures update**</wrap>: next lecture will be the 11th of October. No lesson the 7th of November.
  * <wrap em>**Lectures update**</wrap>: next lecture will be the 24th of October.
  * <wrap em>**Lectures suspended**</wrap>: lectures are suspended until new communications.
  * <wrap em>**No lecture 07/10/2022**</wrap>: next lecture will be the 10th of October.
  * <wrap em>**TBDM on Telegram**</wrap>: students can join to the Telegram group with the following {{https://t.me/+_If79QPfFR42NzQ0 | link}}.
  * <wrap em>**Slides**</wrap>: course material can be found {{https://drive.google.com/drive/folders/1EO237rwC8XL8uhoewKzMTbUrVjwIa8tz?usp=sharing | here}}.
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===== General Info =====
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**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: dtmassimo.callisto[at]unicam.it

**ESSE3 Link**
  * [[https://didattica.unicam.it/Guide/PaginaADErogata.do?ad_er_id=2022*N0*N0*S1*19060*11166&ANNO_ACCADEMICO=2022&mostra_percorsi=S|Techonologies for Big Data Management - AY 2022/23]]

**Scheduling of Lectures**:
  * Scheduling is available at the following [[:didattica:ay2223:orario_en|link]]
  * 42 h - lectures, exercise sessions
  * Schedule on: Monday 14:00-17:00 and Friday 14:00-17:00, AB1 at Polo Ludovici
  
**Degrees**:
  * [[didattica:mscs|MSc in Computer Science (LM-18)]]

  
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===== Course Objectives =====

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  * 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 =====
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  *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 =====
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**Course Slides**
  * Slides {{ https://drive.google.com/drive/folders/1EO237rwC8XL8uhoewKzMTbUrVjwIa8tz?usp=sharing | link}}

  * Github {{ https://github.com/massimocallisto | link }}
  
  * Webex recordings {{ https://docs.google.com/document/d/1J5oblXV7DelsZ-wrFsMSOhjz4i9dPlldj-TXZS9Ispk/edit?usp=sharing | link}}

  * Projects {{ https://drive.google.com/drive/folders/1LDC2VnGat9QaFn-JCgpEHDobEUKM3KE6?usp=sharing | link }}

  * Projects A.A. 2021/22 - {{ https://docs.google.com/spreadsheets/d/1N4szxna3WLaPHmvqTeium8vU_jhd4WKo9g6bXxkrTu8/edit?usp=sharing | link }}

  * Projects A.A. 2020/21 - {{ https://docs.google.com/spreadsheets/d/1CefKoxu1HuU1MUSL8WcJRNPE5v9pJuzCpUwy0pfYbcc/edit?usp=sharing | link }}

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

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===== Exams =====
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**Exam Dates A.Y. 2023/2024 (tentative)**
  * 09/02/2023
  * 20/02/2023
  * 27/02/2023 at 13:00 (new added)
  * 26/05/2023
  * 16/06/2023
  * 21/07/2023
  * 08/09/2023
  * 25/09/2023
  * 09/02/2024
  * 20/02/2024

**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:
  - Project description / objectives
  - Methodology and technology being used
  - Technical implementation
  - Achieved results
  - future improvements

** Exam Results **
  * See news section

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