Educational objectives
Knowledge and understanding
basic mathematical concepts (sets and operations on sets, relations and their properties, general functions, numbers and elementary equations/inequalities)
of functions of a real variable: basic properties, derivatives and their calculation including first-order partial derivatives
of optimisation problems for one variable: concepts and optimality conditions, convexity, algorithmic approach.
of integrals for functions of one variable: indefinite integrals, definite integrals and areas, integral calculus.
of mathematical terminology in English.
of basic concepts of linear algebra: matrices and matrix calculus, vectors and their geometric applications, systems of linear equations.
of functions with several variables: partial derivatives and gradient, convexity.
of optimisation problems for several variables: concepts and optimality conditions, for unconstrained and constrained cases, Lagrange's method.
of descriptive statistics and how to summarise data: variables, frequency distributions, measures of central tendency and variability.
of the concept of uncertainty and the basic elements of probability theory.
of the basic concepts of sample theory.
of the basic concepts of inferential statistics: point estimate; confidence interval; hypothesis testing; linear regression.
of the relationships between variables and basic concepts in hypothesis testing.
of statistical terminology
of the software available for data analysis in the social sciences.
of the basics of linear programming in economics and management.
of the basics of the concepts of uncertainty, ambiguity and robustness in the context of data analysis.
of the basics of order theory specifically partial and total (linear) order relations.
of the implications of non-total order relationships on decision-making models
of Excel's 'best practices' and main functions for collecting, processing and visualising data
of the mechanisms for creating and using big data, and the implications in the business environment.
of the monetary value of personal and corporate data.
of the fundamental methods and algorithms for data analysis, as well as machine learning methods.
of the concept of data security from a legislative and technical point of view.
Ability to apply knowledge and understanding
basic concepts useful for taking courses in economics, business and administration
economic problems with several variables in a formalised manner; ability to identify (optimal) solutions and to interpret the results on the basis of existing theories.
Calculate differentials and integrals of real functions. Ability to solve optimisation problems with one variable.
define economic problems in a formalised way; to find (optimal) solutions and interpret results on the basis of existing theories.
use mathematical tools for the analysis of static and dynamic models.
mathematical problems and models and ideas for solving them.
use mathematical tools for the analysis of static and dynamic models with several variables.
using matrices to represent data and handling them for transformations and calculations.
statistical methods as research tools useful in the social sciences.
descriptive and inferential statistics to synthesise information, to analyse and interpret relationships between variables and for hypothesis testing.
at least one statistical application to develop a simple data analysis.
the use of algorithms/applications to find solutions to linear programmes and their dual problems.
solving zero-sum games via linear programming
solving linear programmes for business management problems: cost and revenue optimisation, logistics design and optimisation, warehouse flow planning, etc.
using mathematical methods to model risks (uncertainties) and to solve expected utility maximisation problems.
distinguishing between decision situations with complete and non-complete preferences and then using the appropriate model.
use of Excel for data collection, processing and visualisation.
use of web services for online data analysis.
understanding the basic principles of modern data analysis concepts, e.g. machine learning.
dealing with data security issues in business realities.
Autonomy of judgement
identify the most relevant variables to be used when making decisions in complex situations;
find the necessary additional information in databases, regulatory sources and scientific bibliography;
adopt logical arguments and relate information and analytical tools to find solutions.
Communication skills
Achievement of this objective will be assessed by means of written examinations, individual and group assignments and the final dissertation.
Learning skills
ability to find the information required to keep abreast of changes in the service sector in general and in the tourism, sports and events sector in particular
ability to find and make use of information from databases, research studies, laws, regulations and standards that are applied in professional life;
ability to analyse, critically evaluate and integrate data, information and experience;
ability to develop possible solutions for economic and management problems in the operational contexts of reference to the graduates' occupational outlets.
Additional educational objectives and learning outcomes
M1 Knowledge and understanding of
- basic mathematical concepts: sets and set operations, relations and their properties, general functions, numbers and elementary equations/inequalities.
- functions one real variable: basic properties, derivatives and their calculus including 1st & 2nd order derivatives.
- single-variable optimization problems: optimality notions and conditions, convexity, algorithmic approach.
- integrals for single-variable functions: indefinite integrals, definite integrals and area, integral calculus.
M2 Knowledge and understanding of
- basic concepts in linear algebra: matrices and matrix calculus, vectors and their geometrical applications, systems of linear equations.
- functions of several variables: partial derivatives and gradients, Hesse matrix, convexity.
- optimization problems for several variables: optimality concepts and conditions for the unconstrained as well as the constrained case, Lagrangian method.
M1/M2 Applying knowledge and understanding to
- follow modern courses in economics, business and administration,
- establish and analyze mathematical problems and models in Economics and Management,
- define economic problems in a formalized mathematical approach; to find (optimal) solutions and to interpret results, being informed by existing theories.
- differentiate and integrate single- and multivariable functions, ability to solve single- and multivariable optimization problems.
- use matrices for data representation and how to manage them for transformations and calculus.
M1/M2 Making judgements
- to make informed decisions about the relevance of sets vs. relations vs. functions in economic models.
- to interpret results obtained for single-variable mathematical models for economic systems.
- to interpret results obtained for linear mathematical models for economic systems involving matrix structures.
- to interpret results obtained for multli-variable mathematical models for economic systems.
M1/M2 Communications skills
- to master the mathematical vocabulary and formalism in English.
- to communicate ideas, problems and solutions for mathematical models involving single-variable real functions.
- to understand matrix formalism and ability to communicate ideas, problems and solutions for linear models.
- to understand multi-variable economic models and the ability to communicate ideas, problems and solutions for such models.
M1/M2 Learning skills for
- the study of basic mathematical structures in an economic environment.
- for the solution of basic mathematical problems related to economical models.
- the study of more complex linear and nonlinear mathematical structures in an economic environment.
- the solution of more advanced mathematical problems related to economical models.