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

===== News =====
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 <wrap em>**Exam 2020-07-30 results**</wrap>: partial results are available [[https://drive.google.com/file/d/1DJvK3TXEe3ckvaSkH5yheyzDOzXvwSp5/view?usp=sharing | here]]. Complete results will be available after project evaluation.

<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 2020-07-09 results**</wrap>: partial results are available [[https://drive.google.com/file/d/1VsmSYfgxvbM4LzpnA8gv2NHWzjtf-kfL/view?usp=sharing | here]]. Complete results will be available after project evaluation.

 <wrap em>**Exam 2020-06-19 results**</wrap>: partial results are available [[https://drive.google.com/file/d/1YIoCLpzknaFLJ54NuGM0o7xPIM5sCuBi/view?usp=sharing | here]]. Complete results will be available after project evaluation.

 <wrap em>**Exam update**</wrap>: exam rules are available [[https://docs.google.com/document/d/1WiqSQSDoQeeFMI1wVZ8hZE4zP17Te3u4zXyryqO0y64/edit?usp=sharing | here]]. Note that exam will be done via Webex and students must have Acrobat Reader installed.

 <wrap em>**Course update**</wrap>: students are asked to complete the assessment questionnaire for this course.

 <wrap em>**Material update**</wrap>: Other materials section updated with EDA projects A.Y 2018-19.

 <wrap em>**Project update**</wrap>: projects and IoT dataset have been updated.

 <wrap em>**Project update**</wrap>: a draft of available projects is available [[https://docs.google.com/spreadsheets/d/1hGmHKzxT1qkP1gH9NielVhTGMRZPOtZi7B2liKJ3pKM/edit#gid=0|here]].

 <wrap em>**Google meet**</wrap>:  due to some possible problems with Webex, lesson will be done via Google Meet. Instruction [[https://drive.google.com/file/d/1H7QKgkeSFgWw_ydgSP8X0a_5Ado6JfJ3/view?usp=sharing|here]]. Room name ''edaunicam2020''.

 <wrap em>**Course start**</wrap>: the first lesson is planned for 12th of March 2019, only via webex room.
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===== General Info =====
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**Teacher**: 
  * Dr. Massimo Callisto De Donato <massimo.callistodedonato[at]gruppofilippetti.it>

**ESSE3 Link**
  * ---

**Webex Link**
  * https://unicam.webex.com/meet/massimo.callisto

**Lessons schedule**:
  * 42 h - lectures, exercise sessions
  * Schedule on Thursday from 14:00am to 18:00pm
  * LB1 room 
  * Schedule (tentative)
    - <del>12-mar-20 - 14:00-18:00</del>
    - <del>19-mar-20 - 14:00-18:00</del>
    - <del>26-mar-20 - 14:30-17:30</del>
    - <del>02-apr-20 - 14:00-18:00</del>
    - <del> 09-apr-20 - 14:00-18:00</del>
    - <del>16-apr-20 - 14:00-17:00</del>
    - <del>23-apr-20 - 14:00-18:00</del>
    - <del>30-apr-20 - 14:30-17:30</del>
    - <del>07-mag-20 - 14:00-18:00</del>
    - <del>14-mag-20 - 14:00-18:00</del>
    - <del>21-mag-20 - 14:00-18:00</del>
    - <del>28-mag-20 - 14:30-16:30</del>


**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/1dlVI_UJTVWd5WmJnPxRLQFFmlyd2uevL?usp=sharing | link}}
  
  * Webex {{ https://docs.google.com/document/d/1yImTJQ7SM7-0mk-H3fpmreAdMtstrcKLnu1K3BX4QMw/edit?usp=sharing | doc with links}}

  * Projects {{ https://docs.google.com/spreadsheets/d/1hGmHKzxT1qkP1gH9NielVhTGMRZPOtZi7B2liKJ3pKM/edit?usp=sharing | link}} | {{ https://drive.google.com/drive/folders/1Sop3tGrQng0dcjKDSCocArHXsMsIKhed?usp=sharing | submitted projects}}


  * **Reference materials**
     * Slides course.
     * Material provided by the teacher.
     * Examples
         * [[https://drive.google.com/drive/folders/1swSIC7deh1X95j_cZKqJl3wBDXEyQlbk?usp=sharing | IoT Dataset]]
         * [[https://drive.google.com/drive/folders/18GUqT-eMNx6zYN9f5dtTPXC3maBZdtrB?usp=sharing | Examples]]
         * [[https://drive.google.com/file/d/12yW6sbKlSw4gpc0nPC6-ze1YNPG1KcE1/view?usp=sharing | PySpark Dataframe reference]]
         * [[https://drive.google.com/drive/folders/1E4XHrTfmIsEGHo9G7IHMMdUf5iOO15_3?usp=sharing | Spark Standalone cluster installation guide]]
     * Other materials
         * [[https://shorturl.at/msI09 | Docker Deepe Dive]]
         * [[https://shorturl.at/wIJK6 | Kubernetes: Up and Running]]
         * [[http://shorturl.at/clAR3 | A Gentle Introduction to Spark]]
         * Projects completed on A.Y 2018-19 [[https://docs.google.com/spreadsheets/d/1t-iwy3SD7LpzGmim4IgpXWT3JeU0ev-5xQrr4J-b430/edit?usp=sharing|Description]] [[https://drive.google.com/drive/folders/1KSrXuj1o9cawKBgtP2epSN6QIbGgol8W?usp=sharing|Projects]]
         * [[https://www.researchgate.net/publication/299379163_A_formal_definition_of_Big_Data_based_on_its_essential_features | A formal definition of Big Data based on its essential features]]

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===== Exams =====
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**Exam Dates A.Y. 2019/2020 (tentative)**
  * 06/18/2020
  * 07/09/2020
  * 07/30/2020
  * 09/03/2020
  * 10/01/2020
  * 10/22/2020
  * 12/10/2020
  * 01/14/2021
  * 02/11/2021

**Exam rules**: 
  * Writing Examination on the topics of the course
  * Project lab (max 2 member per team)
  * [[https://docs.google.com/document/d/1WiqSQSDoQeeFMI1wVZ8hZE4zP17Te3u4zXyryqO0y64/edit?usp=sharing | Rules]]

** Exam Results **
  * Exam 2020-06-19 [[https://drive.google.com/file/d/1YIoCLpzknaFLJ54NuGM0o7xPIM5sCuBi/view?usp=sharing | results]]
  * Exam 2020-07-09 [[https://drive.google.com/file/d/1VsmSYfgxvbM4LzpnA8gv2NHWzjtf-kfL/view?usp=sharing | results]]
  * Exam 2020-07-30 [[https://drive.google.com/file/d/1DJvK3TXEe3ckvaSkH5yheyzDOzXvwSp5/view?usp=sharing | results]]

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