Universitat de Barcelona

Fundamental Principles of Data Science

Introduction

Large amounts of data are generated in many aspects of personal and professional life, from electronic purchases to research or finances. If these data are not monitored or interpreted, they have no value. Data science is a new professional area that aims to give these data meaning by analysing and interpreting them. A data scientist is a new professional profile at the intersection between maths and computer science.
The master's degree in Fundamental Principles of Data Science aims to provide, through theoretical and practical training , the algorithmic and mathematical bases for correct modelling and analysis of data, and the professional competences to face data-based projects. There is a focus on competences to understand the principles of algorithms that lie behind data science. Students will develop the ability to modify algorithms and create new ones, to adapt to the specific needs of a problem. Consequently, the course includes aspects that cover a wide area: computational algebra, optimisation or probabilistic programming, automatic learning techniques and deep learning, complex networks, recommendation systems, applications to natural language processing, time series, extraction of information in images, and support for infrastructures that process big data.

Learning objectives

The Master's Degree in Fundamental Principles of Data Science aims to provide the tools, knowledge and competences required to work effectively as a data scientist. The course focuses on the competences required to understand, modify and create algorithms, analytical and exploratory methods and techniques; as well as leadership abilities and the development of effective data-based projects.

Basic Information

Number of ECTS credits awarded:
 60
Language(s) of instruction:
 English
Number of places available:
 30
Approximate price:
 27,67 euros per credit ( 82 euros for students who are not EU nationals and do not currently reside in Spain). Fees for the academic year 2021-2022
Faculty or school:
  Faculty of Mathematics and Computer Science
Master's degree course homepage:
  Master's degree course homepage

Recommended applicant profile

The ideal applicant for this master's degree holds a bachelor's degree in computer science, mathematics or related studies, has a strong academic CV, and an interest in the field of data science. Applicants will be looking to work in data science in the corporate sector or in government bodies, and in sectors that require high-level specialists in data analysis, interpretation and visualisation (such as finance, biomedicine, information and communication technologies, etc.), or to begin a research career focused on data analysis. Regardless of their previous studies, students of this master's degree should have basic knowledge of programming and of calculus, algebra and statistics.

Admission requirements

In accordance with Article 16 of Royal Decree 1393/29 October 2007, students must hold one of the following qualifications to access university master's degree courses:
  • An official Spanish degree.
  • A degree issued by a higher education institution within the European Higher Education Area framework that authorizes the holder to access university master's degree courses in the country of issue.
  • A qualification from outside the framework of the European Higher Education Area. In this case, the qualification should be recognized as equivalent to an official Spanish degree. If it is not recognized, the University of Barcelona shall verify that it corresponds to a level of education that is equivalent to official Spanish degrees and that it authorizes the holder to access university master's degree courses in the country of issue. Admission shall not, in any case, imply that prior qualifications have been recognized as equivalent to a Spanish master's degree and does not confer recognition for any purposes other than that of admission to the master's degree course.

Specific requirements

Applicants with the following qualifications may be admitted:
Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications (no bridging courses are required).
  • Holders of bachelor's degrees in other engineering subjects or equivalent qualifications, with the authorisation of the Master's Committee (bridging courses will be required).
  • Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications who hold official qualifications from outside of the EHEA (bridging courses will be required).
  • Graduates in other engineering subjects or equivalent qualifications from outside the EHEA, who have official authorisation from the Master's Committee (bridging courses will be required).


  • Given that the master's degree is taught entirely in English, applicants must certify that they have at least level B2 English.

    If applicants must take bridging courses, the Committee will propose a maximum of 30 additional credits from Computer Engineering degrees or from the Bachelor's Degree in Mathematics, depending on the applicant's previous training. The bridging courses will include the following aspects:

    • Introduction to scientific computing and numerical methods
    • Databases
    • Workshop on new uses of computers
    • Probability and statistics
    • Advanced algorithms
    • Artificial intelligence
    • Distributed software

    Skills and competences

    The master's degree covers basic and general competences for managing time, resources and projects, and for working in teams. In addition, it provides tools to face the challenges in the discipline with analytical, critical and creative capacity. The course will focus specifically on the following competences:

    • Capacity to understand the process of analysing data, and the role of data in decision-making.
    • Capacity to gather and extract information from structured and unstructured data sources.
    • Capacity to clean and correct data, in order to create datasets that are easy to manipulate and informative.
    • Capacity to use technologies for the storage, recovery and processing of large volumes of data.
    • Capacity to learn how to propose hypothesis and develop intuition about a dataset using exploratory analysis techniques.
    • Capacity to effectively use analytical and predictive tools for automatic learning.
    • Capacity to understand, develop and modify analytical and exploratory algorithms for a dataset.
    • Capacity to verify and quantify the validity of a hypothesis, using data analysis.
    • Capacity to communicate results using appropriate communication and display techniques.
    • Knowledge of legislation on data protection and privacy, and on the ethical code in professional practice.
    • Capacity to use effective development methods for data science projects.

    Pre-enrolment calendar

    • First period (25 places): 3 February – 28 April 2020.
      Provisional announcement of admission decisions: 10 March 2020.
      Definitive announcement of admission decisions: 17 March 2020.

     

    • Second period (5 places): 11 May – 30 June 2020.
      Provisional announcement of admission decisions: 7 July 2020.

    Definitive announcement of admission decisions: 20 July 2020.*
    * These places will be allocated among those students who were not selected after the first period as well as those students who submitted their applications during the second period.

    [When admitted, the student must do a first payment for the enrollment (500€), that will be discounted from the total, which must be paid later.]
     

     



    Notes:
    • Pre-enrolment fee: A pre-enrolment fee of 30,21 euros is charged. Students who apply to more than one master's degree must pay the fee for each pre-enrolment request. Pre-enrolment requests cannot be processed until this fee has been paid.Fees will only be refunded if the master's degree in question is suspended.
    • Reserved places: A maximum of 5% of the new places of the master's degree are reserved for students who meet the general and specific access requirements and accredit the recognition of a degree of disability equal to or greater than 33%.

    Required documentation

    • Pre-enrolment application
    • Photocopy of degree certificate or equivalent qualification. In case of admission, foreign degrees that require so should be translated and authenticated through diplomatic channels before completing the enrolment.
    • Other specific documentation related to the selection criteria

    Selection criteria

    In the selection process, the following aspects will be evaluated, with the weighting indicated below:
    - Academic transcript from the bachelor's or pre-EHEA degree (70%)
    - Professional experience (30%)

    Enrolment

    Closed Enrolment