====== Data Analytics ======



===== News =====
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  * <wrap em>**exam 29/07/2022 results**</wrap>: results are available {{ https://drive.google.com/file/d/1TOJBmMROh7OMn6Wx9ZVGcyHY9X3NP8iY/view?usp=sharing | here }}.
  * <wrap em>**Exam rules update**</wrap>: 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.
  * <wrap em>**exam 01/07/2022 results**</wrap>: results are available {{ https://drive.google.com/file/d/1iLbyR7ONaGcWcl4C_Z16h5G7Iwh1Q7Gd/view?usp=sharing | here }}.
  * <wrap em>**Exam session update**</wrap>: the exam planned for the 16th of June 2022 has been changed to the 1st of July 2022, 14:30 aula AB2.
  * <wrap em>**Exam rules update**</wrap>: 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.
  * <wrap em>**exam 25/02/2022 results**</wrap>: results are available {{ https://drive.google.com/file/d/1zAXkoWmX8HJ8sXxXeyjbRrW8_nlRxC15/view?usp=sharing | here }}.
  * <wrap em>**exam 14/02/2022 results**</wrap>: results are available {{ https://drive.google.com/file/d/1CdMd91drpG7ECO6M52j52Cy6pnjnhJLy/view?usp=sharing | here }}.
  * <wrap em>**exam update**</wrap>: the exam planned for the 11th of february has been rescheduled on the **14th of february, 2:00pm at Polo Ludovici AB1**.
  * <wrap em>**lecture of 10th of january 2022 will be online**</wrap>: last lecture will be online via webex at 14:15.
  * <wrap em>**Rilevazione delle opinioni e della soddisfazione degli studenti, aa 2021/22**</wrap>: students are invaited to submit the survey.
  * **No lesson today 17th of December.**
  * <wrap em>**Project update**</wrap>: {{ https://docs.google.com/spreadsheets/d/1N4szxna3WLaPHmvqTeium8vU_jhd4WKo9g6bXxkrTu8/edit?usp=sharing | 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:
    -   Project description / objectives
    -   Methodology and technology being used
    -   Technical implementation
    -   Achieved results
    -   Possible future improvements
  * <wrap em>**Course update**</wrap>: **lecture of 29/11/2021** will be done online only via webex.
  * <wrap em>**No lecture 12/11/2020**</wrap>: lecture suspended.
  * <wrap em>**Course start**</wrap>: the first lesson is planned for 4th of October 2020, at Polo Ludovici (LB1).

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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]]


**ESSE3 Link**
  * [[https://didattica.unicam.it/Guide/PaginaADErogata.do?ad_er_id=2021*N0*N0*S1*17829*10593&ANNO_ACCADEMICO=2021&mostra_percorsi=S|Data Analytics - AY 2021/22]]

**Scheduling of Lectures**:
  * Scheduling is available at the following [[:didattica:ay2122:orario_en|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**:
  * [[didattica:mscs|MSc in Computer Science (LM-18)]]

**Students Office hours**:
  * Send an e-mail to the teacher to fix an appointment.
  
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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/1YZ4V3lgDwvxLFgnn80N9oktd_eHqXEB9?usp=sharing | link}}
  
  * Webex {{ https://docs.google.com/document/d/1rNYpEIXkYowP0Y1mf2_HBcYgDfy4jRu4T2gyeYwVDv4/edit?usp=sharing | link}}

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

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

** Exam Results **
  * See news section

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