Program Overview

Master's Degree in Data Science

The master's degree in Data Science aims to create a benchmark for excellence in the field of data science. This eminently interdisciplinary degree is based on two distinct pillars that are equally essential to data science: data management and data analytics.

General Details

• Duration and start date: 2 academic years, 120 ECTS credits, starting in September
• Timetable and delivery: Face-to-face
• Fees and grants: Approximate fees for the master's degree (excluding other costs): 2,324 (5,400 for non-EU residents)
• Language of instruction: English
• Location: Barcelona School of Informatics (FIB)
• Official degree: Recorded in the Ministry of Education's degree register

Admission

• General requirements: Academic requirements for admission to master's degrees
• Specific requirements:
  • Certification of a B2 level (CEFR) of English (or equivalent)
  • Direct admission
  • Recommended entrance qualifications:
    • Bachelor's or pre-EHEA degree in Informatics Engineering
    • Bachelor's or pre-EHEA degree in Mathematics
    • Related qualifications (with priority given to students with a thorough knowledge of mathematics and informatics):
      • Bachelor's degree in Physics
      • Bachelor's degree in Statistics
      • Bachelor's degree in Telecommunications Science and Technology
      • Bachelor's degree in Electronic Engineering and Telecommunications
      • Bachelor's degree in Civil Engineering
      • Bachelor's degree in Industrial Technology Engineering
      • Bachelor's degree in Industrial Electronics and Automatic Control Engineering
      • Bachelor's degree in Bioinformatics
      • Bachelor's degree in Artificial Intelligence
      • Bachelor's degree in Data Science and Engineering
• Places: 40
• Pre-enrolment: Currently closed
• Enrolment: Information available upon request
• Legalisation of foreign documents: Required for documents issued in non-EU countries

Curriculum

First Semester

• Algorithms, Data Structures and Databases (6 ECTS)
• Data Warehousing (6 ECTS)
• Multivariate Analysis (6 ECTS)
• Process-Oriented Data Science (6 ECTS)
• Statistical Inference and Modelling (6 ECTS)

Second Semester

• Big Data Management (6 ECTS)
• Machine Learning (6 ECTS)
• Mining Unstructured Data (6 ECTS)
• Semantic Data Management (6 ECTS)
• Advanced Statistical Modelling (6 ECTS)
• Algorithmics for Data Mining (6 ECTS)
• Bioinformatics and Statistical Genetics (6 ECTS)
• Cloud Computing and Big Data Analytics (6 ECTS)
• Complex and Social Networks (6 ECTS)
• Computer Vision (6 ECTS)
• Data Management for Transportation (4 ECTS)
• Data Visualization (6 ECTS)
• Debates on Ethics of Data Science (3 ECTS)
• Human Language Engineering (4.5 ECTS)
• Interdisciplinary Innovation Project (6 ECTS)
• Introduction to Quantitative Linguistics (6 ECTS)
• Introduction to Research (3 ECTS)
• Introduction to Research (6 ECTS)
• Optimization Techniques for Data Mining (6 ECTS)
• Software Development for Geographic and Spacial Information (3 ECTS)
• Techniques and Methodology of Innovation and Research in Informatics (6 ECTS)
• Techniques and Tools for Bioinformatics (3 ECTS)
• Viability of Business Projects (6 ECTS)

Third Semester

• Advanced Machine Learning (6 ECTS)
• Advanced Multivariate Analysis (6 ECTS)
• Data Analysis and Knowledge Discovery (6 ECTS)
• Information Retrieval and Recommender Systems (6 ECTS)
• Machine Learning Systems in Production (Mlops) (6 ECTS)

Fourth Semester

• Master's Thesis (30 ECTS)

Professional Opportunities

• Graduates work in data management and data analytics
• Main positions:
  • Data scientist
  • Data engineer
  • Data specialist
  • Data administrator
  • Systems architect
  • Systems analyst
  • Digital transformation leader (DTL)
  • Chief information officer (CIO)
  • Chief data officer (CDO)

Competencies

Generic Competencies

• Capacity for innovation and entrepreneurship
• Sustainability and social commitment
• Knowledge of a foreign language (preferably English)
• Teamwork and proper use of information resources

Specific Competencies

• Ability to develop efficient algorithms based on knowledge and understanding of the theory of computational complexity and the main data structures in the field of data science
• Ability to apply the basic principles of data management and processing to problems in the field of data science
• Ability to apply data integration methods to solve data science problems in heterogeneous environments
• Ability to apply scalable methods for storage and parallel processing of data
• Ability to model, design, and implement complex data systems, including data visualization
• Ability to design data science processes and to apply scientific methods to draw conclusions about populations and take decisions accordingly
• Ability to identify the limitations imposed by data quality when tackling data science problems and to apply techniques to reduce their impact
• Ability to extract information from structured and unstructured data
• Ability to apply appropriate methods for analyzing other types of formats, such as processes and graphs, in the field of data science
• Ability to identify machine learning and statistical modeling methods for solving a specific data science problem and to apply them in a rigorous manner
• Ability to analyze and extract knowledge from unstructured information by applying natural language processing techniques and through the use of text and image mining
• Ability to apply data science methods to multidisciplinary projects to solve problems in new or unfamiliar domains
• Ability to identify the main data ethics and privacy issues affecting data science projects and to develop and implement appropriate measures to mitigate related threats
• Ability to carry out and present and defend before an examination committee an original, individual piece of work consisting of a comprehensive data science engineering project that synthesizes the competencies acquired on the degree

Quality Accreditation

• Check the degree's main quality indicators in the University Studies in Catalonia portal of the Catalan University Quality Assurance Agency
• Find information on topics such as degree evaluation results, student satisfaction, and graduate employment data

Organisation: Academic Calendar and Regulations

• UPC school: Barcelona School of Informatics (FIB)
• Academic coordinator: Ňscar Romero Moral
• Academic calendar: General academic calendar for bachelor's, master's, and doctoral degrees courses
• Academic regulations: Academic regulations for master's degree courses at the UPC