ECTS - University of Pisa
Scheda programma d'esame
LABORATORY ON ALGORITHMS FOR BIG DATA
ROSSANO VENTURINI
Academic year2016/17
CourseBUSINESS INFORMATICS
Code588AA
Credits6
PeriodSemester 1
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
LABORATORIO DI ALGORITMI PER BIG DATAINF/01LEZIONI48
ROSSANO VENTURINI unimap
Learning outcomes
Knowledge

The course introduces advanced algorithms and data structures of practical interest. The students will encouraged in their projects to implement, test and compare these techniques on real datasets.

The course will provide the opportunity of

  • facing with difficult algorithmic problems of practical interest involving big data;
  • evaluating the impact of efficient algorithmic solutions in the design of software managing big data;
  • implementing advanced software by using powerful and sophisticated libraries;
  • getting in touch with some companies for internships, scholarships, or thesis proposals.
Assessment criteria of knowledge

Methods:

  • Final oral exam
  • Project

 
Skills

Students will learn advanced algorithms and data structures of practical interest and their use to design and to develop efficient implementions.
Assessment criteria of skills

Some lectures ask students to solve algorithmic problems and implement their solutions.
Behaviors

Students will learn how to chose the best solutions to solve algorithmic problems.
Assessment criteria of behaviors

Several problems are presented to improve and to test students' problem solving skills.
Prerequisites

Basic knowledge of Algorithms and Data Structures.
Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • preparation of oral/written report
  • group work
  • Laboratory work
  • Practical

Attendance: Advised

Teaching methods:

  • Lectures
  • laboratory
  • project work
Syllabus

The course consists of a first part of lectures describing advanced algorithms and data structures (3 CFU), and a laboratory in the second part (3 CFU) in which the students will deploy these techniques to develop a software project. The students will select their projects among a set of proposals by major IT companies which are challenging from an algorithmic perspective. These companies will also contribute to identify/construct significant datasets that will help in testing the proposed algorithmic solutions.
Bibliography

The notes and books references provided by the tearcher.
Assessment methods

The exam consists of two parts: Project and its presentation 70% – Oral test (or a serious attempt to compete in any online (algorithmic) contest) 30%.
Pagina web del corso

http://pages.di.unipi.it/rossano/221-2/laboratory-on-algorithms-for-big-data-a-a-201617/
Class web page

http://pages.di.unipi.it/rossano/221-2/laboratory-on-algorithms-for-big-data-a-a-201617/
Updated: 18/05/2017 10:12