CES Resolution: RPC-SO-03-No. 094-2021
Resolución de los ajustes curriculares sustantivos: RPC-SO-12-No.215-2026
Acuerdo de los ajustes curriculares no sustantivos: ACU-CPP-SO-10-No.102-2026

Modality

Online

---

Entry Profile:

Applicants entering the Master's Degree in Data Science at the Postgraduate Unit of the Technical State University of Quevedo must meet the following characteristics:

• Hold an undergraduate degree in the broad field of Information and Communication Technologies (ICT).
• Experience and interest in deepening technical-professional knowledge in Data Science systems.
• Proactive attitude to advance interactive programmes in technical-professional and research processes in Computer Science.
• Comprehension, reading, and translation of the English language.
• Basic management of scientific information.

---

Graduate Profile:

Graduates of the Master's programme in Data Science are well-rounded professionals capable of developing analytical and machine learning solutions in both local and cloud environments, from data acquisition and preparation to the communication of results, with an ethical and data protection focus.

The professional will demonstrate advanced technical expertise for performance in multidisciplinary environments, with competencies to:

• C1. Model applied problems using statistical inference and advanced regression, applying estimation, hypothesis testing, and validation procedures under criteria of rigour and reproducibility, evidenced through documented models, reproducible analyses, and a technical report interpreting results.
• C2. Implement machine learning solutions through justified selection of approaches, model training, and comparative evaluation using appropriate metrics, evidenced through a functional prototype, reproducible experiments, and a performance report.
• C3. Execute data mining processes following recognised methodologies (e.g., KDD or CRISP-DM), integrating data preparation, pattern discovery, model construction, and validation.
• C4. Design scalable data processing architectures and workflows for analytics, both batch and streaming, in Big Data ecosystems and cloud services, incorporating considerations of availability, security, and data governance.
• C5. Communicate analytical results using exploratory and explanatory visualisation techniques, as well as dashboards and indicators, adapting communication to different audiences with well-supported presentations of findings.
• C6. Collect and prepare data from web sources using information access and extraction mechanisms, respecting terms of use, ethical principles, and applicable personal data protection regulations.
• C7. Investigate applied data science problems through the formulation of research questions, literature review, methodological design, and computational experimentation, communicating results with academic integrity.

General Objective

To train human talent in areas related to Data Science through an educational process that promotes the theoretical-practical learning of a set of techniques and theories of “data retrieval, security, analysis, processing and management, predictive models, programming, statistical learning, pattern recognition, and data mining”, aimed at generating knowledge that contributes to the timely making of strategic business decisions in both public and private organisations.

Specific Objectives

Linked to knowledge and expertise

• Develop capabilities in mastering the theoretical foundations related to Data Science [OEM1], based on intelligent development that includes the systematisation and computerisation [OEM2] of organisations, in order to achieve greater production with fewer resources.

Linked to relevance

• Understand the most effective ways to strategically use the large volumes of data stored by organisations.

Linked to learning

• Solve the main problems arising in the fields of action of Data Science through the implementation of research, development, and entrepreneurship projects within the community [OEM3].
• Acquire specific knowledge and the most advanced tools for problem-solving, not only in business areas but also in scientific domains.
• Master the basic principles of data modelling and the methodologies and techniques of Data Mining.

Linked to interculturality

• Value the challenges involved in proper data management based on ethical and professional principles, including respect for environmental care in accordance with the National Development Plan 2017–2021 “Toda una Vida”.

Class Schedule:

Saturdays and Sundays: 08:00 - 12:00 | 13:00 - 17:00

---

Modules:

First Level

• Statistical Inference and Modelling
• Fundamentals of Machine Learning
• Data Mining
• Advanced Data Mining
• Scientific Research Methodology

Second Level

• Big Data
• Information Visualisation
• Web Mining
• Cloud Processing
• Degree Project Workshop

Research Line