Master's of permanent training in Data Science

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The Master's degree in Data Science of the Rey Juan Carlos University arises from the need to train qualified professionals who combine knowledge in both engineering and analysis of complex data sets for application in multiple industrial, research and innovation sectors. Currently, there is a great demand for this type of professionals that is difficult to satisfy, given the scarcity of candidates who integrate an adequate knowledge profile and also have practical experience in the design, development and deployment of this type of project.

Objectives

• Integrate the theoretical and practical knowledge necessary for the practice of data science, integrating both the engineering and data analysis dimensions.
• Acquire skills in the use of the main architectures and technological tools, as well as mathematical and statistical methods used in data science.
• Put into practice the knowledge acquired to apply it in a real work environment (internships in a company) and develop a complete data science project (master's thesis).

Competences

GENERAL COMPETENCIES

• Possess and understand knowledge that provides a foundation or opportunity to be original in the development and/or application of ideas, often in a research context
• That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ​​study
• That students are able to integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
• That students know how to communicate their conclusions and the knowledge and ultimate reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way
• That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
• Ability to develop and apply original ideas in the area of ​​data engineering and analytics, using available technological methodologies and tools.
• Ability to develop and apply original ideas in the area of ​​business intelligence and decision support, using data mining and optimization tools and models.
• Ability to lead multidisciplinary teams in order to face and solve problems related to engineering and data analytics in multiple application areas.
• Ability to formulate judgments based on data through methodologies, models and analysis tools that consider the level of associated uncertainty, all based on socially responsible utility functions.
• Ability to fully justify the design and construction alternatives/decisions of information systems for engineering and data analytics, which can be understood by different types of audiences.
• Ability to continue training in the field of engineering and data analytics through sources of information and reliable and proven references.
• Capacity for analysis and synthesis, organization and planning
• Oral and written communication skills
• Critical and self-critical capacity
• Ability to integrate and communicate with experts from other areas, in different contexts and at different levels of detail.
• Ability to write and present technical and scientific documents.

SPECIFIC COMPETENCES

• Ability to understand the different methodologies, processes and data mining tools, especially applied to the detection of latent information in heterogeneous data sources.
• Ability to understand the application of Big Data methods and technologies applied to the achievement and improvement of business requirements and objectives.
• Ability to use Big Data extraction, preparation, storage and analysis tools.
• Ability to understand the connections and interrelationships between the different engineering and data analytics methodologies and techniques.
• Ability to understand the main aspects related to the security of information systems, especially in the context of engineering and data analysis.
• Ability to understand the most relevant concepts and paradigms of distributed computing systems for the design and implementation of data engineering systems.
• Ability to understand, design and implement advanced data visualization techniques that allow the effective transmission of analysis results.
• Ability to design and implement cloud computing architectures that meet the requirements of data analytics processes in a scalable and elastic way.
• Ability to learn about the different dissemination forums related to data engineering, distributed and cloud computing, data analytics and business intelligence and select the most appropriate for a specific objective.
• Ability to design, implement and manage massive heterogeneous data storage systems.
• Ability to design advanced data models adapted to complex data engineering systems.
• Ability to handle tools that automate the engineering process and data analysis.
• Ability to structure analysis of decision-making processes and participate in a structured way in a negotiation.
• CE14 . Ability to handle the modeling tools of business intelligence and decision analysis.
• CE15 . Ability to understand and take advantage of the characteristics of new trends in the development of engineering and data analytics systems.

Internal quality assurance system

Manual of the Internal Quality Assurance System

Composition of the commission

Composition of the commission

Results report

Once the follow-up of the Master of Lifelong Training has been carried out, the most relevant quantitative information on the results obtained in the follow-up of said Degree is shown.

General information collection plan

• Indicators 2022/23