Objectives

The main objective of the Master's in Data Science is to prepare highly qualified professionals, particularly in the analysis of large amounts of data. The course is designed to provide students with sound knowledge in the areas of statistical analysis and computer science. Data science lies at the intersection of these two areas of knowledge that the data scientist must master. It is this virtuous combination of skills that differentiates this course from others in the same field. In addition to sound knowledge, this master's programme also provides practical knowledge in Data Science, with laboratory classes, hands-on assignments, and projects in collaboration with companies that face real problems which require Data Science methodologies.

Structure

The cycle of studies consists of:

• A first year of the master's course of 60 ECTS, whose approval awards a certificate in the master's course (not leading to a degree) in Data Science.
• An original thesis of scientific nature, especially carried out for this purpose, which awards 48 ECTS, to which 12 ECTS are added for obtaining approval in a curricular unit in the area of Management and another optional curricular unit. Obtaining approval in the public defense of the thesis, awards a Master's Degree in Data Science.

Schedule and teaching language

The master's course schedule, although taught in the daytime, will be concentrated as much as possible in blocks that allow the students to manage their time more efficiently. All materials will be provided in English, including exams and other papers. Oral communication will be in English, unless it is not justified.

Employment Prospects

Numerous market studies have alerted us to the growing need for professionals who are skilled in analysing the amount of data that our society has been producing exponentially. Several technological advances have contributed to the increase in the amount of data available. The decreasing cost of countless sensors, the advance of the "computerization" of the vast majority of human activities, the phenomenon known as Internet of Things (IoT), among other factors, have caused this growth. The vast majority of human activities are increasingly being recorded in some electronic form. This huge amount of data "hides" useful information about organizations and their activities. The ability to discover this information from this large data collection is therefore a competitive advantage that most organizations have already identified as key to being successful. For this to be possible, given the amount of data available, computational tools are needed as well as professionals capable of developing and using them efficiently. The Master's degree in Data Science aims to train this type of professional and thus help fill the recognized gaps in terms of the workforce currently available in the job market, as pointed out by several studies and business organizations.

Admissions Requirements

• Holders of a bachelor's degree or holders of an equivalent foreign higher academic degree, under the conditions described in the law, in the areas of Computer Science, Mathematics, Economics, Engineering, Physics, Biology, or similar areas.
• Undergraduate students who are able to complete a degree in the terms in the previous paragraph before the end of the enrolment period for the cycle of studies; applications that do not prove that 85% of the credits of the cycle of studies in question have been obtained at the time of the application deadline will be excluded
• Good knowledge of English language: written and spoken.

Note: in the registration phase, applications that do not prove having completed the degree (or equivalent) by the end of the enrolment deadline will be excluded.

Criteria for Selection and Ranking

Ranking of candidates will be done according to the following criteria:

• Criterion 1: academic curriculum (area of training and final average obtained) (85%)
  • Sub-criterion 1.1: bachelor’s degree adequacy (15%)

    The degree adequacy will be scored on a scale of 0 to 20 according to the following:

    • degrees in the area of computer science/ computer engineering, mathematics, statistics, data analysis, artificial intelligence, physics, physical engineering, electrical engineering, and industrial management engineering or similar will be rated 20.
    • degrees with a solid background in statistics/mathematics or computing such as mechanical engineering, economics, management, management informatics will be rated 18.
    • degrees with some background in statistics or computing such as biology, (engineering) chemistry, economy or another engineering will be rated 16.
    • Other degrees will be analysed on a case-by-case basis and their rating will be determined according to the Curricular Units completed in the areas of computing and statistics / mathematics.
  • Sub-criterion 1.2: adjusted bachelor’s degree grade (70%)

  The adjusted grade is obtained by normalising the bachelor’s degree grade to the 0-20 scale (rounded to the nearest integer), adding the value of ln(R/r), and rounding to the first decimal digit, with ln expressing the natural logarithm and R and r being, respectively the world ranking of the University of Porto and the university issuing the degree, as published in http://www.webometrics.info

  For admitted applications where the degree has not yet been concluded, the previous formula still applies although replacing the bachelor’s degree final grade with the weighted average of the curricular units completed on the application date rounded to the nearest integer.

• Criterion 2: scientific curriculum and professional experience (15%)

  The scientific curriculum and professional experience are rated from 0 to 20 according to the following two sub-criteria, considering the relevance of the indicators for the area of the cycle of studies.

  • Sub-criterion 2.1: technical and/or scientific publications and communications (3%).
  • Sub-criterion 2.2: relevant professional experience in companies, participation in research projects or internships in the area (6%).
  • Sub-criterion 2.3: complementary training in the area, including degrees and courses not awarding a degree, such as other masters, short or long postgraduate courses, and certified short courses (6%).

Tiebreaker criteria:

In the event of a tie, the ranking of the higher education institution considered in sub-criterion 1.2 will be used as the first tie-break criterion, and the grade obtained in an interview as the second tie-break criterion.

Observations:

The grades of the curricular units completed must be certified by an official document to be presented by the candidate, which indicates, whenever possible, their weighted average. In case the candidate does not yet have a bachelor's degree, and the indication of the average is not possible via an official document, that information must be indicated, explicitly, in the comments section of the application form.

Teaching Language

• In Portuguese and partially in English

Information

• Application Dates 2024-2025
• Readmission Dates 2024-2025

Contacts

General information

Study Plan

Certificates

Predominant Scientific Areas