Social and environmental commitment
To act with ethics and professional responsibility in the face of social, environmental and economic challenges, taking democratic principles and values and the Sustainable Development Goals as a reference. |
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Innovation and creativity
Propose creative and innovative solutions to complex situations or problems, specific to the field of knowledge, in order to meet diverse professional and social needs. |
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Teamwork and leadership
Collaborate effectively in work teams, assuming responsibilities and leadership roles and contributing to collective improvement and development. |
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Effective communication
Communicate effectively, both orally and in writing, adapting to the characteristics of the situation and the audience. |
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Responsibility and decision-making
Act autonomously in learning, making informed decisions in different contexts, making judgements based on experimentation and analysis and transferring knowledge to new situations. |
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Fundamental
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Ability to apply acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to process improvement and decision making that enable him/her to analyze and solve complex problems in uncertain environments.
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Ability to integrate knowledge and face the complexity of making judgments based on incomplete or limited information, including reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
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Ability to acquire the learning skills that will enable him/her to continue studying in a manner that will be largely self-directed or autonomous.
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Ability to communicate their conclusions (and the ultimate knowledge and rationale behind them) to specialized and non-specialized audiences in a clear and unambiguous manner.
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Ability to work in a team
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Ability to analyze, model and solve problems related to process improvement and optimization.
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Ability to select the most appropriate technique for each possible problem, both in the field of research and in the business environment.
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Ability to use advanced statistical and optimization software, as well as to deal with other related software not covered in the Master's program
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Ability to reason and critically analyze the results of the application of different methodologies in problem solving, as well as their applicability and possible limitations
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Skills in the use of advanced data analysis, process improvement and decision making techniques to support research and business decisions.
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Ability to generate new data analysis methods and optimization algorithms by adapting existing methods or developing original contributions
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Ability to model real phenomena through random vectors and to apply the main techniques of multivariate analysis in the context of industry and business.
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To know and use the different regression techniques for diagnosis, evaluation, inference and subsequent decision making.
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Know how to perform ANOVA and interpret the results obtained on mixed models, both in the nature of their effects (fixed and random) and in the structure of the design (cross-factorial and hierarchical).
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Design and plan a data mining project in real financial or marketing problems.
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To value the vital role that statistical tools play in improving the quality and productivity of products and production processes and services.
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Know, design and use different process control charts in different contexts. To understand the problems associated with the simultaneous control of several variables and their solution by means of multivariate graphs.
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To know various multivariate statistical techniques of projection on latent structures: PCA, PLS, Parafac, Tucker-3, N-PLS, multiblock models, capable of analyzing this type of data matrices, and their relationship with other classical multivariate techniques and from the field of data mining. Select and apply the most appropriate techniques according to the problem to be solved: compression, classification, discrimination or prediction.
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To know and apply different simulation models that allow modeling complex systems. Knowledge of neural network analysis.
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Define the tasks to be performed in a project, their durations and the order of execution. Establish feasible execution schedules, plans for monitoring the actual project progress and appropriate budgetary control measures
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Know and apply methods for solving linear and integer programming models. Interpret the results obtained when solving a model and evaluate the solution when the input data is modified.
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To have a vision of the different techniques of time series analysis and to make forecasts with the best conditions that the statistical methodology allows.
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Ability to improve reliability in the design and manufacturing stage of a product. To know methods to obtain the reliability of a system and specific inference tools for censored data, small samples or tests performed under overload conditions.
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Ability to design simple experiments and analyze their results.
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Know and apply Production Management tools for the development of the Aggregate Plan, Master Plan, CRP, OPT, Lean Manufacturing.
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