Course: 2020/2021

Tools for research in telematic engineering

(16142)

Students are expected to have completed

Students taking this course are expected to have prior knowledge on probability theory at the basic level (concept of probability, conditional probability, probability distribution function, probability density function, usual probability distributions, etc.) In addition, they are expected to be fluent programmers.

The competencies that are reinforced in this course are:
- Students acquire the learning abilities that enable them to continue studying in a self-regulated and autonomous learning way
- Ability to apply knowledge of mathematics, statistics and science to the problems of Telematics Engineering
- Ability to design and conduct experiments, and analyze and interpret data
- Students know how to use different techniques for the modelling and analysis of the temporal behavior of a telematics system
- Students master the analytical foundations of the performance analysis of networks and telematics applications
After finishing the course, students will be able to:
- Properly design experiments and apply the appropriate statistical methods in order to evaluate different research objectives they will face in their research career
- Apply concepts of graph theory in order to solve problems that require it, and program algorithms on graphs

Description of contents: programme

The contents of the course are divided into two separated parts. Each one of them is detailed next with their included topics.
1.- Applied statistics
- Design of experiments: Research objectives, data gathering, methodology.
- Descriptive statistics
- Confidence intervals
- Normality test, Levene test
- Statistical hypothesis testing
- Parametric tests such as t-test or ANOVA
- Non-parametric tests such as Kruskal-Wallis or Mann-Whitney.
- Correlation
- Prediction: regression, bayesian networks
2.- Introduction to graphs in telematic systems
- Definition of graph, properties, paths, loops, connectivity, types of graphs
- Shortest path algorithms

Learning activities and methodology

The course comprises theoretical and practical sessions. The theoretical part will include, in addition to the explanations of the different concepts, examples and case studies. The practical part about the applied statistical part will include the resolution of practical cases and the use of a statistical software. The practical part about graph theory will include programming at least one shortest path algorithm.

Assessment System

- % end-of-term-examination 50
- % of continuous assessment (assigments, laboratory, practicals...) 50

Basic Bibliography

- Andy Field. Discovering Statistics using R. SAGE. 2012
- John A. Dossey, Albert D. Otto, Lawrence E. Spence, Charles Vanden Eynden. Discrete Mathematic, 4th ed.. Addison Wesley. 2001

- · R manuals : http://cran.r-project.org/manuals.html
- G.P. Quinn, M.J. Keough · Experimental Design and data analysis for biologists : http://bio.classes.ucsc.edu/bio286/MIcksBookPDFs/
- Ruth Rosenholtz · Statistical Methods in Brain and cognitive sciences : http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-07-statistical-methods-in-brain-and-cognitive-science-spring-2004/

Additional Bibliography

- Deborah Rumsey. Intermediate Statistics For Dummies. John Wiley & Sons. 2007
- Peter Dalgaard . Introductory statistics with R. Springer. 2002

(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN