Mathematics Statistics

1. Knowing how to model and implement optimization methods and simulation techniques in decision-making problems in business. 2. Knowing the conditions to be satisfied by solutions of optimization problems. 3. Learn to use tools of modern optimization and simulation techniques in an efficient way.

K7: Understand the distinct nature of the various variables of an economic problem and solve it using concepts of linear algebra, matrix, and differential calculus S1: Apply statistical methods to perform linear regression analysis and test hypotheses, and understand the nature of the different variables of an economic problem S4: Use the information by interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to evaluate the reliability and quality of such information. S9: Show the ability to organize and plan work, to make decisions based on the information obtained and to make judgments from critical thinking within their area of knowledge C1: To be able to analyse, develop and defend an interdisciplinary problem related to the content taught in the Bachelor's Degree, applying the knowledge, skills, tools and strategies acquired or developed during the course. C2: Know and be able to manage interpersonal skills on initiative, responsibility, conflict resolution, negotiation, etc., that are required in the professional field. C4: Manage and prepare budgets, as well as apply accounting tools to record and analyze the financial transactions of a company. C5: Be able to apply the scientific method to relevant business and economic questions, to pose a problem in this area, identifying a method that allows for careful interpretation of the available data C6: Evaluate the adequacy of the strategies of business organizations, as well as propose improvements in such strategies based on information available both in the company itself and in its environment C7: Apply statistical and econometric tools to solve organizational problems.

1. Introduction Process modeling in decision-making problems 2. Optimization of linear models 2.1 Optimality 2.2 Examples: financial planning, etc.. 3. Optimization of discrete models 3.1 Optimality 3.2 Examples: project management, electricity markets, etc. 4. Otimization of non-linear models 4.1 Optimality conditions 4.2 Least-squares methods 4.3 Newton's Method 4.4 Examples: estimation of "betas" of stocks, portfolio management, etc. 5. Simulation 5.1 Examples: stock prices, overbooking, etc.

Theory (3 ECTS), Practice (3 ECTS). 14 lectures with supporting materials available on the Web. Another 14 practical sessions (exercises and computer labs).