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.
Person in charge
Pau Fonseca Casas (
Esteve Codina Sancho (
Lidia Montero Mercadé (
Technical Competences of each Specialization
Computer networks and distributed systems
CEE2.3 - Capability to understand models, problems and mathematical tools to analyze, design and evaluate computer networks and distributed systems.
High performance computing
CEE4.1 - Capability to analyze, evaluate and design computers and to propose new techniques for improvement in its architecture.
Generic Technical Competences
CG1 - Capability to apply the scientific method to study and analyse of phenomena and systems in any area of Computer Science, and in the conception, design and implementation of innovative and original solutions.
CG3 - Capacity for mathematical modeling, calculation and experimental designing in technology and companies engineering centers, particularly in research and innovation in all areas of Computer Science.
CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.
CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.
Applying the mathematical formalism to solve problems involving uncertainty.
Applying the queuing models for computer systems performance evaluation and/or configurations analysis.
Ability to design, conduct experiments and analyze results.
Introduction to probability
Students should feel comfortable with the use of set notation and basic statistical terminology. Likewise, the student should be able to write the sample space of simple experiments, including sampling with replacement (like throwing coins or throwing dice), sampling without replacement, from Bernoulli trials and with rules of detention. Likewise, the student should be able to calculate the probabilities in simple cases of the above type of experiment.
Introduction to statistical estimation
Estimation, in the framework of statistical inference, is the set of techniques with the aim of give an approximate value for a parameter of a population from data provided by a sample. From the different methods that exist (point estimate, estimate intervals, or Bayesian estimation) we focus on the point estimate.
Analysis of data
The main objective of the section is to know the procedures associated with the analysis of variance (ANOVA terminology in English) and when is useful to be applied.This activity also introduces MANOVA, as a technique useful when there are two or more dependent variables. We also work with the techniques of linear regression and PCA, completing the repertoire of tools for data analysis.
Introduction to experimental design
Statistical experimental design, a.k.a. design of experiments (DoE) is the methodology of how to conduct and plan experiments in order to extract the maximum amount of information in the fewest number of runs (saving resources). In this section we describe different techniques to achieve that.
Introduction to queuing theory
This section will introduce the student to use the techniques of operations research for systems analysis for making quantitative decision in the presence of uncertainty through their representation in terms of queuing models.
Introduction to probability
At the end of this activity the Student must be comfortable with using basic set notation and terminology. Also the Student must be capable of write down the sample space for simple experiments, including sampling with replacement (such as tossing coins or rolling dice), sampling without replacement, and Bernoulli trials with stopping rules. Also the Student must be capable of calculate probabilities in straightforward instances of the above types of experiment. Objectives:1 Contents:
Estimation, in the framework of statistical inference, is the set of techniques with the aim of give an approximate value for a parameter of a population from data provided by a sample. From the different methods that exist (point estimate, estimate intervals, or Bayesian estimation) we focus on the point estimate. Objectives:1 Contents:
The main objective of the activity is to know the procedures associated with the analysis of variance (ANOVA terminology in English) and when is useful to be applied.This activity also introduces MANOVA, as a technique useful when there are two or more dependent variables. Objectives:1 Contents:
Linear regression is a mathematical method that models the relationship between a dependent variable Y, independent variables Xi and a random term. This section will examine this method and explain its applicability from different examples. Objectives:1 Contents:
The principal component analysis (PCA, PCA in English), in statistics, is a technique that reduces the dimensionality of a dataset. This allows us to represent them graphically in two or three dimensional graphs of various variables grouped the data into factors, or components, consisting of the grouping variables. In this section we will work this technique from a practical point of view. Objectives:1 Contents:
Randomized blocks, Latin squares and related designs
In many research problems is necessary to design experiments that can systematically control the variability caused by different sources. This section will consider some experimental designs for solve these situations. Objectives:3 Contents:
15 (Outside class hours) Type:
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.
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.
Statistics for experimenters : an introduction to design, data analysis, and model building -
Box, George E. P; Hunter, William Gordon; Hunter, J. Stuart,
John Wiley and Sons, cop. 1978. ISBN: 0-471-09315-7 http://cataleg.upc.edu/record=b1006823~S1*cat
Design and Analysis of Experiments -
MONTGOMERY, Douglas C.,
Wiley, ISBN: 1118146921
Probability and statistics for computer scientists -
Chapman & Hall, 2014.
The Art of computer systems performance analysis : techniques for experimental design, measurement, simulation, and modeling -
Jain, Raj, John Wiley & Sons ,
ISBN: 0471503363 http://cataleg.upc.edu/record=b1080952~S1*cat
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.
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00