• 2017cover Present
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Data Science

INFORMATION, PRE-REGISTRATION AND REGISTRATION
Own Teachings
Telephone: 91 488 70 40
Academic direction: Isaac Martin de Diego

Student attention:    Student Help Box     Suggestions and complaints box

more information        master's website

Basic Information

Presentation

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.

SCHEDULE

The training program is structured in 4 knowledge modules. Core subjects (6 ECTS credits) as well as specialization subjects (3 ECTS credits) are included. In all of them, theoretical knowledge will be combined with exercises and practical work. A differentiating aspect of the Master is the integration of data engineering aspects (Spark, Hadoop, cloud architectures, data collection and storage) and data analytics (statistical models, data mining, simulation, graph analysis or visualization and communication) .

List of Subjects and Subjects.

I. Statistical Methods

  • Data Science Techniques and Methods (6 ECTS).
  • Data Mining (3 ECTS).
  • Simulation and Computing Methods (3 ECTS).

II. Data Capture and Storage

  • Data Collection (3 ECTS).
  • Information Search and Retrieval (3 ECTS).
  • Non-Conventional Databases (3 ECTS).
  • Privacy and Data Protection (3 ECTS).

III. Data Processing

  • Distributed Data Processing Systems (6 ECTS).
  • Cloud Architectures (3 ECTS).
  • Data Processing Oriented Programming (3 ECTS).

IV.Data Analysis

  • Intelligence and Business Analytics (6 ECTS).
  • Analysis of Graphs and Social Networks (3 ECTS).
  • Visualization: Communication and Presentation of Results (3 ECTS).

Recipients

The Master in Data Science is aimed at students with different degrees of training (Diplomas, Technical Engineers, Graduates and Higher Engineers, Graduates) in different areas:

  • Computer Engineering, Technical Engineering in Computer Management, Technical Engineering in Computer Systems.
  • Telecommunication engineering.
  • Industrial Engineering and Industrial Organization.
  • Bachelor's degree in Mathematics or Statistics.
  • Degree in Economic Sciences, Business Administration and Management.
  • Other areas of knowledge related to those mentioned above.

Academic Management and Faculty

The teaching staff of the Master in Data Science will be doctors from the different areas of knowledge: Computer Science, Computer Architecture, Computer Languages ​​and Systems and Statistics and Operational Research and Mathematics.

Academic information

  • Director of the Master:
  • Isaac Martin de Diego
  • Mater Academic Secretary:
  • Belen Vela Sanchez
  • Academic subdirector:
  • Alberto Sanchez Campos (URJC)
  • Infrastructure Deputy Director:
  • Jose Felipe Ortega Soto
  • Deputy Director of Students and Communication:
  • Carlos E. Cuesta Quintero

Duration and development

Degree: Master in Data Science

Modality: Onsite

Duration: 36 weeks

Development: September 16, 2022 to July 14, 2023

Number of credits: 60

Contact hours: 480

Place of delivery: On-site Campus of Móstoles and Madrid Argüelles

Mostoles hours: Wednesday Thursday 17:00-21:00 and Friday 15:00-21:00

Schedule Madrid- Argüelles : Friday 15:00-21:00

                                                Saturday 9:00-15:00

Expected start date: 16 2022 de septiembre

Expected completion date: July 14, 2023

Reservation of place and enrollment

Degree: Master in Data Science

pre-registration period

Pre-registration period: May 12 to September 9, 2022

Registration period: from September 9 to 13, 2022

School starts

Mostoles: September 16, 2022

Madrid: September 17, 2022

End of Classes: 

Mostoles: July 14, 2023

Madrid: July 15, 2023

Title price: €5000

Pre-registration: €500. This amount is included in the total cost of the course and will be returned if your academic request is not accepted. If, once the student's application has been admitted, the enrollment is not formalized, the amount deposited for pre-enrolment will not be returned.

No. of Places: 30

The start of the course is conditioned to the minimum number of students enrolled.

Documentation to attach, forms and place of delivery

the applicants they will present all the scanned documentation, in the formats allowed through the telematic self-registration application at the time of applying for admission to own degrees. They must compulsorily attach to their request the declaration of the person responsible for the veracity of the data provided in digital format.

At any time, both the Program Management and the Own Teaching Service may request the applicants to submit said certified/collated documentation through the General Registry, located on the Móstoles Campus, or in any of the registries assistants located in the different campuses of the Rey Juan Carlos University, or by sending it through Certified Mail to: Rey Juan Carlos University. General Registry. Avda. Tulipán s/n. 28933. Mostoles. Madrid

The student is responsible for the veracity and correctness of the data provided, exonerating the Rey Juan Carlos University from any responsibility and guaranteeing and being responsible for its accuracy, validity and authenticity.

Required documentation:

Students with a degree obtained from a Spanish university or a Higher Education Institution belonging to another Member State of the European Higher Education Area that authorizes access to own postgraduate degrees must present the following documentation:

  • National Identity Document or equivalent
  • University degree of the studies that give access to the requested postgraduate degree.
  • Curriculum vitae
  • Responsible declaration of veracity of the data provided in digital format
  • Any other document that the Director of the Own Title specifically requires for its acceptance

Students with a foreign degree must present the following documentation:

  • Passport or Residence Card
  • Foreign Higher Education Degree (Graduate, Graduate, Architect, Engineer Doctor...) that give access to own postgraduate degree studies.
  • Certificate certifying that the studies carried out give access to an Official Postgraduate Degree in your country of origin, issued by the University of origin
  • Curriculum vitae
  • Declaration of the person responsible for the veracity of the data provided in digital format
  • Any other document that the Director of the Own Title specifically requires for its acceptance

Applicants with studies completed in foreign University Centers may be requested at any time a certificate of verification of these studies and centers, issued by an authorized Institution.

All documentation provided must be legalized in accordance with Spanish law and translated by an official translator.