====== Enterprise Data Analytics ======

===== News =====
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   * <wrap em>**New exam date**</wrap>: 5th of december, room to be fixed 
   * <wrap em>**Exam results**</wrap>: results for the writing session of 5th of September 2019 are available [[https://drive.google.com/file/d/1jiDzFYzXAffAkKyoVlHe6ynAkzS7tZbv/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.
   * <wrap em>
   **Projects update**</wrap>: projects are available [[https://drive.google.com/drive/folders/1KSrXuj1o9cawKBgtP2epSN6QIbGgol8W?usp=sharing | here]] 
   * <wrap em>**Exam results**</wrap>: results for the writing session of 27th of June 2019 are available [[https://drive.google.com/file/d/1mfAJ_f6NId5BGgcUqIoDrFZuQ6OfX-ae/view?usp=sharing | here]] 
   * <wrap em>**Course update**</wrap>: projects have been published [[https://docs.google.com/spreadsheets/d/1t-iwy3SD7LpzGmim4IgpXWT3JeU0ev-5xQrr4J-b430/edit?usp=sharing|here]]. More details will be given during the next lessons.
   * <wrap em>**Lessons update**</wrap>: for the second part of the lesson planned for 16th of May, students are invited to attend the seminary "Data-driven methods for modeling critically-ill patients affected by atrial fibrillation" 16:30 room AB1.
   * <wrap em>**Lessons update**</wrap>: students should bring their own laptop for lab exercises.
   * <wrap em>**No lesson on 4th of april 2019**</wrap>: lecture suspended for this week.
   * <wrap em>**No lesson on 14th of march 2019**</wrap>: lecture suspended for this week.
   * <wrap em>**Course started**</wrap>: course content updated.
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===== General Info =====
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**Teacher**: 
  * Dr. Massimo Callisto De Donato <dtmassimo.callisto[at]unicam.it>

**ESSE3 Link**
  * [[https://didattica.unicam.it/Guide/PaginaADErogata.do?ad_er_id=2018*N0*N0*S2*14629*9981&ANNO_ACCADEMICO=2018&mostra_percorsi=S|Enterprise Data Analytics - AY 2018/2019]]

**Lessons schedule**:
  * 42 h - lectures and exercise sessions
  * Thursday: 14:00 pm – 16:00 pm
  * Room AB1 - Polo "Carla Lodovici" 

**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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===== Course Contents =====

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  * Acquire knowledge and competence on Big Data methodologies, techniques and technologies.
  * Know most common techniques of Big Data analysis and how they apply to real world examples.
  * Apply Big Data Analysis techniques into practical case studies.

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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/1K8FbQ5I3v35opr_zelh7ybjIQAn6zb-c?usp=sharing | link}}
     * Course introduction and rules
     * Enterprise Data Analytics
     * Big Data
     * Storage & Computation
     * NoSQL
     * Computation
     * Analytics lab with Apache Spark, Cassandra, PySpark
     * Data Analytics models
  
  * Webex {{ https://docs.google.com/document/d/1sUCGkkHJr8NoTfVLeWf7mrDbVWRxCu2bwhkoij3-ySo/edit?usp=sharing | doc with links}}

  * **Reference materials**
  * Slides course.
  * Material provided by the teacher.
  * Examples
  * Other materials
     * Docker + Cassandra {{ https://github.com/nizarhmain/kafka_playing_around#entreprise-business-analytics-part | GitHub }} 
     * Jupyter notebooks {{ https://drive.google.com/drive/folders/1ylU_d7Uxyo12wpfdL4SmUKT4_6iAX2Qb?usp=sharing | link }} 

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===== Exams =====
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**Exam Dates A.Y. 2018/2019**

  * 27/06/2019 - 15:00
  * 11/07/2019 - 15:00
  * 01/08/2019 - 15:00
  * 05/09/2019 - 15:00
  * 26/09/2019 - 15:00
  * 14/11/2019 - 15:00
  * 12/12/2019 - 15:00
  * 16/01/2020 - 15:00
  * 06/02/2020 - 15:00
  * 27/02/2020 - 15:00

**Exam rules**: 
  * Writing Examination on the topics of the course
    * Open or multiple-choice questions + Exercise
    * 2 h
  * Project lab (max 2 member per team)

**Project files**: 
  * Projects description {{ https://docs.google.com/spreadsheets/d/1t-iwy3SD7LpzGmim4IgpXWT3JeU0ev-5xQrr4J-b430/edit?usp=sharing | link}}
  * Projects completed [[https://drive.google.com/drive/folders/1KSrXuj1o9cawKBgtP2epSN6QIbGgol8W?usp=sharing | link]]
  * Rules are discussed in the first lesson slide.

** Exam Results **
  * -

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