Program Overview

University Programs

The university offers a range of programs for students, including bachelor's degrees, master's degrees, and integrated bachelor-master degrees.

Bachelor's Degrees

• Bachelor Degree in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Reassessment, Specializations, Competences, Competences for degree subjects
  • Faculty
  • Bachelor's Thesis
  • Timetables
  • Exams
  • Academic Regulations
• Bachelor Degree in Data Science and Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic Regulations and organization
• Bachelor Degree in Artificial Intelligence
  • Enrolment: Places lliures
  • Curriculum: Competences, Syllabus, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic regulations
  • Bachelor's thesis
• Bachelor Degree in Bioinformatics
  • Enrolment: Available places
  • Curriculum: Learning Outcomes, Syllabus
  • Faculty
  • Timetables
  • Exams
  • Academic Regulations
• Integrated Bachelor Master Degree
  • Enrolment
  • Curriculum

Master's Degrees

• Master in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Master's Thesis
  • Timetables
  • Exams
• Master in Informatics Engineering - Industrial Modality
  • Curriculum
• Master in Innovation and Research in Informatics
  • Enrolment: Available places
  • Curriculum: Syllabus, Specializations, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Master's Thesis
  • Seminars
  • Timetables
  • Exams
• Master in Artificial Intelligence
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Master's Thesis
  • Timetables
  • Exams
  • FAQs
• Master in Cybersecurity
• Master in Data Science
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Academic Regulations
  • Timetables
  • Exams
  • Master's Thesis
    • Gender Competency
• Erasmus Mundus Master in Big Data Management and Analytics
  • Timetables
  • Curriculum: Syllabus
  • Exams
• Master in Urban Mobility
  • Curriculum
• EUMaster4HPC
  • Curriculum
• Other Masters
  • Master in Pure and Applied Logic
  • Master in Computational Modelling in Physics, Chemistry and Biochemistry

Academic Management

• Administrative Procedures
• Academic calendars
• Extinct Curriculums

Grants and Financial Aid

• Awards

Mobility

• Incoming
  • Academic stays
  • Research Visit
• Outgoing
  • Mobility Calendar
  • Information Sessions
  • Mobility experiences
  • Study abroad
    • Before you leave
    • When you arrive
    • Before you return
    • When you return
  • Internship abroad
  • Other activities abroad
• Double degrees
• International Partnerships
  • Mobility Programs
    • CERN (Conseil Européen pour le Recherche Nucléaire)
    • Erasmus+
    • Latin America
    • National Institute of Informatics (NII) Tokyo
    • SICUE
    • UNITECH
    • USA grant programs
    • Vulcanus
  • University Networks
  • Partner universities

Research

• Departments
• Research Centers
• Research Groups
• inLab FIB

Companies

• Industrial Practices
  • Offers list
  • Posting offers
• Job Placements
  • Offers list
  • Posting offers
• FIB Visiona
• Sponsorship
  • Jedi Mobile Apps Lab
  • Social Point Lab

The FIB

• The School
  • The school in Figures
  • Location
  • Governance
    • CACFBBI
    • CACFIBBI
    • CACOBBI
  • Staff
  • Awards
  • Graduation Ceremony
  • PROPER Project
• Rooms
  • Computer Labs
  • Teaching laboratories
  • Teaching Classrooms
  • Group work classroom
  • Presentation Rooms
  • Rector Gabriel Ferraté Library
• IT Services
  • How to study remotely
  • IT Guide for new students
  • Service catalog
• University Life
  • Associations
• Quality system
  • Internal Quality Assurance System
  • Qualification assessment
  • Statistical data

Statistical Modelling and Design of Experiments

Course Description

The aim of the course is to provide students with the tools needed to cope with complex systems using statistical modeling techniques. The students also learn different techniques of experimental design.

Credits and Type

• Credits: 6
• Type: Compulsory

Requirements

This subject has no requirements, but it has previous capacities.

Department

EIO

Teachers

• Person in charge: Pau Fonseca Casas
• Others: Esteve Codina Sancho, Lidia Montero Mercadé, Nihan Acar Denizli

Weekly Hours

• Theory: 1
• Problems: 1
• Laboratory: 2
• Guided learning: 0
• Autonomous learning: 6

Competences

Technical Competences of each Specialization

• Computer networks and distributed systems: CEE2.3
• High performance computing: CEE4.1

Generic Technical Competences

• Generic: CG1, CG3

Transversal Competences

• Information literacy: CTR4
• Reasoning: CTR6

Objectives

1. Applying the mathematical formalism to solve problems involving uncertainty.
2. Applying the queuing models for computer systems performance evaluation and/or configurations analysis.
3. Ability to design, conduct experiments and analyze results.

Contents

1. Introduction to probability
2. Introduction to statistical estimation
3. Analysis of data
4. Introduction to experimental design
5. Introduction to queuing theory and simulation

Activities

• Introduction to probability
• Introduction to statistical estimation
• ANalysis Of VAriance
• Linear regression
• Principal component analysis
• Factorial design
• Randomized blocks, Latin squares and related designs
• Incomplete block design
• General structure of queuing models
• Queuing models based on birth and death processes
• Generalized queuing patterns with non-exponential distributions and serial exponential queues.
• Validation Verification and Accreditation
• First report
• Second report
• Third report
• Final exam

Teaching Methodology

The course is practical and aims that students will be able, once the course is completed and from the work done in the sessions, to solve real problems similar to those developed in class.

Evaluation Methodology

The course will have different exercises that the students must solve during the course (80% of the final grade). At the end, there will be an exam that will weigh 20% of the final grade.

Bibliography

Basic:

• Simulation: the practice of model development and use
• Statistics for experimenters : design, innovation, and discovery
• Design and analysis of experiments
• An Introduction to queueing systems
• Estadística per a enginyers informŕtics
• Probability and statistics for computer scientists

Complementary:

• The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling
• Probability and statistics with reliability, queuing and computer science applications
• Introduction to operations research
• Operations research: applications and algorithms
• Practical reliability engineering
• Probability models for computer science

Web Links

• The Comprehensive R Archive Network
• Wiki SIM
• INTRODUCTION TO PROBABILITY

Previous Capacities

Students must have sufficient knowledge of algebra and mathematical analysis to assimilate the concepts related to algebra of sets, numerical series, functions of real variables of one or more dimensions, derivation and integration.