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Master's Degree in Data Science

The Master's Degree in Data Science is an academic proposal of excellence in the field of data science. It is defined as highly interdisciplinary, based on two well-differentiated but equally necessary pillars for data science: data management and data analysis. The objective is to educate highly qualified professionals with a high capacity for innovation in the fields of data management and analysis.

General Information

• Duration and Start: 2 academic years, 120 ECTS credits, starting in September.
• Schedule and Modality: Face-to-face.
• Prices and Scholarships: Approximate price of the master's degree without additional expenses (does not include non-academic fees or degree issuance): 2,324 (5,400 for non-EU residents). More information on prices and payment of enrollment.
• Languages: English.
• Place of Implementation: Faculty of Computer Science of Barcelona (FIB).
• Official Title: Registered in the register of the Ministry of Education, Culture and Sport.

Access

• General Requirements: Academic requirements for access to a master's degree.
• Specific Requirements:
  • The master's degree is taught entirely in English, so it is required to accredit a B2 level of English or equivalent.
  • Direct Access: The recommended entry profile for admission to the master's degree is that of students with the following degrees:
    • Degree in Computer Science (or Computer Engineering of the previous order of studies).
    • Degree in Mathematics (or Bachelor's degree in Mathematics of the previous order of studies).
  • Also considered are related degrees that guarantee solid knowledge in computer science and mathematics, such as:
    • Degree in Physics or equivalents.
    • Degree in Statistics or equivalents.
    • Degree in Science and Technologies of Telecommunication, Engineering of Technologies and Services of Telecommunication, Electronic Engineering of Telecommunication or equivalents.
    • Degree in Civil Engineering or equivalents.
    • Degree in Industrial Technologies Engineering, Electronic Industrial and Automatic or equivalents.
  • New degree programs close to the field of data, such as:
    • Degree in Bioinformatics or equivalents.
    • Degree in Artificial Intelligence or equivalents.
    • Degree in Data Science and Engineering or equivalents.
• Places: 40.
• Pre-enrollment: The pre-enrollment for this master's degree is currently closed.

Study Plan

First Quarter

• Algorithms, Data Structures, and Databases: 6 credits.
• Data Warehouses: 6 credits.
• Multivariate Analysis: 6 credits.
• Process-Oriented Data Science: 6 credits.
• Statistical Inference and Modeling: 6 credits.

Second Quarter

• Massive Data Management: 6 credits.
• Machine Learning: 6 credits.
• Semantic Data Management: 6 credits.
• Unstructured Data Mining: 6 credits.
• Algorithmics for Data Mining: 6 credits.
• Bioinformatics and Statistical Genetics: 6 credits.
• Cloud Computing and Massive Data Analysis: 6 credits.
• Debates on Data Science Ethics: 3 credits.
• Software Development for Geographical and Spatial Information: 3 credits.
• Data Management for Transport: 4 credits.
• Human Language Engineering: 4.5 credits.
• Introduction to Research: 3 credits.
• Introduction to Research: 6 credits.
• Introduction to Quantitative Linguistics: 6 credits.
• Advanced Statistical Modeling: 6 credits.
• Interdisciplinary Innovation Project: 6 credits.
• Social and Complex Networks: 6 credits.
• Optimization Techniques for Data Mining: 6 credits.
• Bioinformatic Techniques and Methodology: 3 credits.
• Techniques and Methodology of Innovation and Research in Computer Science: 6 credits.
• Business Project Viability: 6 credits.
• Computer Vision: 6 credits.
• Data Visualization: 6 credits.

Third Quarter

• Data Analysis and Knowledge Discovery: 6 credits.
• Advanced Multivariate Analysis: 6 credits.
• Advanced Machine Learning: 6 credits.
• Information Retrieval and Recommender Systems: 6 credits.
• Machine Learning Systems in Production (MLOps): 6 credits.

Fourth Quarter

• Master's Thesis: 30 credits.

Professional Outlets

The future graduates will carry out tasks of data management and data analysis. The main positions related to each of these tasks are:

• Data Scientist.
• Data Engineer.
• Data Specialist.
• Data Administrator.
• Systems Architect.
• Systems Analyst.
• Digital Transformation Leader (DTL).
• Chief Information Officer (CIO).
• Chief Data Officer (CDO).

Competences

Transversal Competences

The transversal competences describe what a graduate is capable of knowing or doing upon completing their learning process, regardless of the degree. The transversal competences established in the UPC are:

• Entrepreneurial spirit and innovation.
• Sustainability and social commitment.
• Knowledge of a third language (preferably English).
• Teamwork and solvent use of information resources.

Specific Competences

• Develop efficient algorithms based on the knowledge and understanding of computational complexity theory and main structures within the field of data science.
• Apply data management and processing fundamentals to a data science problem.
• Apply data integration methods to solve data science problems in heterogeneous environments.
• Apply scalable data storage and parallel processing methods, including continuous data flows, once identified as the most suitable for a data science problem.
• Model, design, and implement complex data systems, including data visualization.
• Design the data science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from structured or unstructured data and potentially stored in heterogeneous forms.
• Identify the limitations imposed by data quality in a data science problem and apply techniques to reduce the impact.
• Extract information from structured and unstructured data, taking into account the multivariate nature.
• Apply appropriate methods for the analysis of other types of formats, such as processes and graphs, within the field of data science.
• Identify machine learning and statistical modeling methods to be used to solve a specific data science problem and apply them rigorously.
• Analyze unstructured information using natural language processing techniques, text mining, and image analysis, and extract knowledge.
• Apply data science in multidisciplinary projects to solve problems in new or little-known domains that are economically viable, socially acceptable, and in accordance with current legislation.
• Identify the main threats in the field of data ethics and privacy in a data science project (both in the aspect of data management and data analysis) and develop and implement measures to reduce these threats.
• Carry out, present, and defend an original individual exercise before a university tribunal, consisting of a data science engineering project, in which the competences acquired in the teaching are synthesized.

Quality Seals

Consult the quality indicators of the degree in the portal University Studies of Catalonia of the Agency for the Quality of the University System of Catalonia. You can know, among others, the results of the evaluation of the studies, the degree of satisfaction of the students or the labor insertion data of the graduates.

Academic Organization: Normatives, Calendars

• Teaching Center UPC: Faculty of Computer Science of Barcelona (FIB).
• Academic Responsible for the Program: Ňscar Romero Moral.
• Academic Calendar: Academic calendar of university studies of the UPC.
• Academic Normatives: Academic normative of master's studies of the UPC.