First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
COMPUTER ENGINEERING
Course unit
BIG DATA COMPUTING
INP7079233, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
COMPUTER ENGINEERING
IN0521, Degree course structure A.Y. 2009/10, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination BIG DATA COMPUTING
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0521-000ZZ-2018-INP7079233-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ANDREA ALBERTO PIETRACAPRINA INF/01

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7079233 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA IN2371
SCP7079297 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA SC2377
SCP7079297 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA SC1176
INP7079233 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines INF/01 Computer Science 2.0
Core courses ING-INF/05 Data Processing Systems 4.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
2 A.A. 2018/2019 01/10/2018 15/03/2020 PIETRACAPRINA ANDREA ALBERTO (Presidente)
PUCCI GEPPINO (Membro Effettivo)
BILARDI GIANFRANCO (Supplente)
FANTOZZI CARLO (Supplente)
PESERICO STECCHINI NEGRI DE SALVI ENOCH (Supplente)
SILVESTRI FRANCESCO (Supplente)
VANDIN FABIO (Supplente)
1 A.A. 2017/2018 01/10/2017 15/03/2019 PIETRACAPRINA ANDREA ALBERTO (Presidente)
PUCCI GEPPINO (Membro Effettivo)
BILARDI GIANFRANCO (Supplente)
FANTOZZI CARLO (Supplente)
SILVESTRI FRANCESCO (Supplente)
VANDIN FABIO (Supplente)

Syllabus
Prerequisites: The course has the following prerequisites: competences regarding the desing and analysis of algorithms and data structures, knowledge of fundamental notions of probability and statistics, and programming skills in Java or Python.
Target skills and knowledge: In this course students learn fundamental algorithmic techniques for the effective and efficient processing of large datasets. Moreover, through a number of practical activities, they acquire skills regarding the development of applications in Apache Spark, which is one of the most popular and widely adopted programming frameworks for big data computing.
Examination methods: The exam consists of a number of programming homeworks, assigned approximately every 2-3 weeks and to be carried out in groups of 3-4 students, and of an individual written test comprising both theory questions and exercises.
Assessment criteria: The final evaluation is based on the homeworks and on the written test. The homeworks aim at assessing the students' ability to program big data applications in Apache Spark, while the written test aims at assessing their knowledge of the algorithmic methodologies learned in the course and their problem solving skills in the big data realm.
Course unit contents: The course will cover the following topics:

Introduction to the Big Data phenomenon
Programming frameworks: MapReduce/Hadoop, Spark
Clustering
Association Analysis
Graph Analytics (metriche di centralità, scale-free/Power-law graphs, fenomeno dello small world, uncertain graphs)
Similarity and diversity search
Planned learning activities and teaching methods: Lectures and activities related to the execution of the homeworks.
Additional notes about suggested reading: The lectures' diary, course material, and detailed exam rules are made available on the course web site accessible from MOODLE:

http://www.dei.unipd.it/~capri/BDC/
Textbooks (and optional supplementary readings)
  • J. Leskovec, A. Rajaraman and J. Ullman, Mining Massive Datasets. --: Cambridge University Press, 2014. Available in pdf Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Case study
  • Working in group
  • Questioning
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure