Big Data Analytics

  • Exam results 2018-02-08 A.A. 2017-18: results have been published link . Students can confirm their vote by mail and register at the next exam session.
  • Exam results 2018-02-08 A.A. 2016-17: results have been published link . Students can confirm their vote by mail and register at the next exam session.
  • Exam update: 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).
  • Exam update: exam scheduled for 1/2/2018 has been re-scheduled for 8/2/2018 .
  • Course update: Homeworks are now available in Study Material section.
  • Lecture update: 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
  • Lecture update: Next lecture is scheduled for 27th november 2017 at 14:00pm - room LA2.
  • Lecture update: Next lecture is scheduled for 20th november 2017 at 14:00pm - room LA2.
  • No lesson 9th november 2017: Lecture suspended. Next lecture is 13th november 2017 at 14:00pm.
  • Papers 2016-17: available here .
  • No lesson 26th october 2017: Lecture suspended. Next lecture is 9th november 2017.
  • Course start: lessons will start on 5th October 2017.
  • Course 2017-16: link


  • Prof.ssa Emanuela Merelli <emanuela.merelli[at]>
  • Dr. Massimo Callisto De Donato <massimo.callistodedonato[at]>

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.

  • 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.

  • 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.

  • 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

Course Slides

  • Reference materials
  • Slides course.
  • Material provided by the teacher.
  • Examples

Exam Dates A.Y. 2017/2018

  • 08/02/2018 - 15:00
  • 22/02/2018 - 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

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