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didattica:magistrale:bda:main [2018/02/07 10:29] massimo [News] |
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| - | ====== Big Data Analytics ====== | ||
| - | |||
| - | ===== News ===== | ||
| - | <WRAP center round important 95%> | ||
| - | * <wrap em>**Exam update**</wrap>: exam scheduled for 8/2/2018 will be held in AB3, 15:00-16:00. Two types of exam will be held: A.A. 2016-17 (full exam for the past course), A.A. 2017-18 (partial for the new course). | ||
| - | * <wrap em>**Exam update**</wrap>: exam scheduled for 1/2/2018 has been re-scheduled for 8/2/2018 . | ||
| - | * <wrap em>**Course update**</wrap>: Homeworks are now available in **Study Material** section. | ||
| - | * <wrap em>**Lecture update**</wrap>: Le lezioni della seconda parte del corso inizieranno l'8 gennaio e seguiranno per 4 settimane come corso intensivo di 21 ore. Siete invitati ad iscrivervi al gruppo facebook BIGDATA-2017 | ||
| - | * <wrap em>**Lecture update**</wrap>: Next lecture is scheduled for 27th november 2017 at 14:00pm - room LA2. | ||
| - | * <wrap em>**Lecture update**</wrap>: Next lecture is scheduled for 20th november 2017 at 14:00pm - room LA2. | ||
| - | * <wrap em>**No lesson 9th november 2017**</wrap>: Lecture suspended. Next lecture is 13th november 2017 at 14:00pm. | ||
| - | * <wrap em>**Papers 2016-17**</wrap>: available {{ https://drive.google.com/drive/folders/0B3Jl2zBf8Y-pV2hsTXRPZGJaZEE?usp=sharing | here }}. | ||
| - | * <wrap em>**No lesson 26th october 2017**</wrap>: Lecture suspended. Next lecture is 9th november 2017. | ||
| - | * <wrap em>**Course start**</wrap>: lessons will start on 5th October 2017. | ||
| - | * <wrap em>**Course 2017-16**</wrap>: {{https://goo.gl/PDpZPw | link }} | ||
| - | </WRAP> | ||
| - | ---- | ||
| - | ===== General Info ===== | ||
| - | <WRAP box round 95% center> | ||
| - | **Teacher**: | ||
| - | * **Prof.ssa Emanuela Merelli** <emanuela.merelli[at]unicam.it> | ||
| - | * **Dr. Massimo Callisto De Donato** <massimo.callistodedonato[at]gruppofilippetti.it> | ||
| - | |||
| - | **Lessons schedule**: | ||
| - | * 42 h - lectures (2 modules), exercise sessions | ||
| - | * Thursday: 16:00 pm – 19:00 pm | ||
| - | * Room LA2 - Polo "Carla Lodovici" | ||
| - | |||
| - | **Students Office hours**: | ||
| - | * Send an e-mail to the teacher to fix an appointment. | ||
| - | </WRAP> | ||
| - | ---- | ||
| - | ===== Course Objectives ===== | ||
| - | |||
| - | <WRAP box round 95% center> | ||
| - | * The course gives an introduction to the Big Data models and related techniques required to perform data analysis in real world examples. | ||
| - | * The course focuses on data with "Big Data characteristics" such as data that can generated by any kind of systems with an high volume, data that grows very fast, data highly semi-structured or un-structured. | ||
| - | * The course highlights the correlations between Big Data and related fields of IoT and Smart Cities. | ||
| - | * The course introduces all relevant state-of-the-art concepts, methods and technologies enabling Big Data Analysis in real world business cases. | ||
| - | </WRAP> | ||
| - | |||
| - | ---- | ||
| - | ===== Course Contents ===== | ||
| - | |||
| - | <WRAP round 95% center box> | ||
| - | * 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. | ||
| - | |||
| - | </WRAP> | ||
| - | ---- | ||
| - | |||
| - | |||
| - | ===== Syllabus ===== | ||
| - | <WRAP round 95% center box> | ||
| - | * **Introduction to Big Data** | ||
| - | * Why Big Data | ||
| - | * Big Data use cases in the real world | ||
| - | * Needs, challenges and opportunities of Big Data | ||
| - | * **Big Data models: Methodologies and Techniques** | ||
| - | * The 3V model | ||
| - | * From datawarehouse to Big Data | ||
| - | * Enable Big Data Analysis | ||
| - | * **Big Data Analysis: storage** | ||
| - | * Storage models for Big Data | ||
| - | * Hadoop framework | ||
| - | * hdfs filesystem | ||
| - | * **Big Data Analysis: computation** | ||
| - | * Computational framework for Big Data Analysis | ||
| - | * Data at rest and Data in motion | ||
| - | * MapReduce and YARN framework | ||
| - | * Other related processing frameworks | ||
| - | * **Big Data Analysis: NoSQL** | ||
| - | * Databases in Big Data systems | ||
| - | * The CAP theorem and BASE model | ||
| - | * NoSQL data models | ||
| - | * **Big Data Analysis: streaming** | ||
| - | * How enabling Big Data Streaming Processing | ||
| - | * Batch processing Vs RealTime processing | ||
| - | * An overview of Apache Spark | ||
| - | * **Advance topics in Big Data Analysis** | ||
| - | </WRAP> | ||
| - | ---- | ||
| - | |||
| - | ===== Study material ===== | ||
| - | <WRAP box round center 95%> | ||
| - | **Course Slides** | ||
| - | * slide 1st_0 lesson {{ https://drive.google.com/file/d/0B3Jl2zBf8Y-pSWJqaDQ5WWVoeWs/view?usp=sharing | slides_1}} | ||
| - | * slide 1st_1 lesson {{ https://drive.google.com/file/d/0B3Jl2zBf8Y-pbG0zcm5aS0pmSUU/view?usp=sharing | slides_1_1}} | ||
| - | * slide 2st lesson {{ https://drive.google.com/file/d/0B3Jl2zBf8Y-pNHZlUXFCbXV2NGs/view?usp=sharing | slides_2}} | ||
| - | * slide 3st_1 lesson {{ https://drive.google.com/file/d/0B3Jl2zBf8Y-pV0p4ekJ0RnhpdXM/view?usp=sharing | slides_3st_1}} | ||
| - | * slide 3st_2 lesson {{ https://drive.google.com/file/d/0B3Jl2zBf8Y-pYU5HRVBYV3lNYU0/view?usp=sharing | slides_3st_2}} | ||
| - | * slide 4st lesson {{ https://drive.google.com/file/d/1ENRrfxuhoH8h4Kuir8Omx7B5jZUaGYht/view?usp=sharing | slides_4st}} | ||
| - | * slide 5st lesson {{ https://drive.google.com/file/d/1ncOQ2skwVcp-UVQtjR7Zfcw3n6JXSLxB/view?usp=sharing | slides_5st}} | ||
| - | * slide 6st lesson {{ https://drive.google.com/file/d/10WsuwCjFC4SiyQnKZnfFICX2DXK6X75D/view?usp=sharing | slides_6st}} | ||
| - | * slide 6_1st lesson {{ https://drive.google.com/file/d/1VZIkzkO9F2_Ok7Lwhy8mA_6rPy6opklC/view?usp=sharing | slides_6_1st}} | ||
| - | * slide 7st lesson {{ https://drive.google.com/file/d/1TSSCo4iaazxD5Ua02T2V_c-fV3wbTOP3/view?usp=sharing | slides_7st}} | ||
| - | |||
| - | * **Reference materials** | ||
| - | * Slides course. | ||
| - | * Material provided by the teacher. | ||
| - | * Examples | ||
| - | |||
| - | * **Homework** | ||
| - | * {{ https://drive.google.com/file/d/15r_hh1dpREW9GgwaMqJEShRwDcN9D-aB/view?usp=sharing | Info and available homeworks}} | ||
| - | * {{ https://drive.google.com/file/d/1bHKfxpLIyQCBjfuNOnbFRHH9qUhO4qPW/view?usp=sharing | Template (.doc) }} | ||
| - | |||
| - | </WRAP> | ||
| - | ---- | ||
| - | |||
| - | ===== Exams ===== | ||
| - | <WRAP box round center 95%> | ||
| - | **Exam Dates A.Y. 2017/2018** | ||
| - | |||
| - | * 08/02/2018 - 15:00 | ||
| - | * 01/03/2018 - 15:00 | ||
| - | * 14/06/2018 - 15:00 | ||
| - | * 12/07/2018 - 15:00 | ||
| - | * 20/09/2018 - 15:00 | ||
| - | * 22/11/2019 - 15:00 | ||
| - | * 07/02/2019 - 15:00 | ||
| - | |||
| - | **Exam rules**: | ||
| - | * Writing Examination on the topics of the course | ||
| - | * Open or multiple-choice questions + Exercise | ||
| - | * 2 h | ||
| - | * Homework assignment evaluation | ||
| - | |||
| - | ** Exam Results ** | ||
| - | * - | ||
| - | |||
| - | </WRAP> | ||
| - | |||
