Bachelor (BSc)

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Accredited
6 Years
20 May 2019
Accreditation DGES
Initial registry R/A-Cr 27/2019 de 14-06-2019
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Tuition fee EU nationals (2024/2025)

1.stYear 697.00 €
2.rdYear 697.00 €
3.thYear 697.00 €
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Lectured in Portuguese
Teaching Type In person

The undergraduate degree in Data Science is based on the convergence of different scientific areas - Mathematics, Statistics and Informatics – and its programme structure is structured around projects which foster both practical and theoretical thinking, with a view towards granting the student an informed, critical, and autonomous understanding of data in the face of the various dimensions of the Knowledge Society and the Digital Revolution.

The Bachelor's is well-situated for helping students to comprehend and explore the areas of this knowledge-base. These actions support the student's progressive acquisition of independence and the capacity to respond to problems of increasing complexity.

With the synthesis, which occurs in the last two semesters, the coherence of the training program is consolidated around responsible practice and the exceptional professional skills required in order to respond to the challenges of modern society.

Programme Structure for 2024/2025

1st Year
Data in Science, Bussiness and Society
6.0 ECTS
Linear Algebra Fundamentals
6.0 ECTS
Programming
6.0 ECTS
Calculus Topics I
6.0 ECTS
Sampling and Information Sources
6.0 ECTS
Exploratory Data Analysis
6.0 ECTS
Data Structures and Algorithms
6.0 ECTS
Optimization for Data Science
6.0 ECTS
Calculus Topics II
6.0 ECTS
Writing Scientific and Technical Texts
2.0 ECTS
Critical Thinking
2.0 ECTS
2nd Year
Big Data Storage
6.0 ECTS
Computational Statistics
6.0 ECTS
Fundamentals of Database Management
6.0 ECTS
Unsupervised Learning Methods
6.0 ECTS
Security, Ethics and Privacy
6.0 ECTS
Introduction to Dynamic Models
6.0 ECTS
Supervised Learning Methods
6.0 ECTS
Heuristic Optimization
6.0 ECTS
Big Data Processing
6.0 ECTS
Applied Project in Data Science I
6.0 ECTS
3rd Year
Network Analysis
6.0 ECTS
Symbolic Artificial Intelligence for Data Science
6.0 ECTS
Web Interfaces for Data Management
6.0 ECTS
Stocastic Modelling
6.0 ECTS
Applied Project in Data Science II
6.0 ECTS
Management Performance Analysis
6.0 ECTS
Applied Final Project in Data Science
12.0 ECTS

Objectives

The BsC degree in Data Science provides:

  • a solid training, at the level of the highest international standards, which allows return of value to society;
  • a solid deontological basis for the professional integration of graduates;
  • a set of skills to design and implement computational solutions to problems in the field of data collection, processing, modelling and analysis;
  • written and oral skills for working and communicating in multidisciplinary teams;
  • ability to undertake and innovate.

 

In short, the general objectives are:

  • Mastery of computational and statistical reasoning;
  • Theoretical, methodological and practical knowledge in specific areas of statistics, operational research, computer science and information sciences, all relevant to large-scale and varied data analysis;
  • Ability to apply knowledge-building solutions in a wide range of problems and domains;
  • To develop a professional practice that is regulated by ethical principles and conduct;
  • Acquire the necessary skills for the development of scientific research and problem solving.

The bachelor should be able to attain the learning outcomes:


Skills:

  • be able to collect, clean, transform, an query data;
  • be able to organize, summarise, visualize data and outcomes;
  • be able to select and apply the appropriate methodologies to perform data analysis, statistical inference, and predictive and prescriptive analysis;
  • be able to implement algorithms in a general purpose language;
  • be able to evaluate and reflect on the level of security, data protection and privacy of a specific technological solutions.


Competencies:

  • be able to develop data-driven analysis;
  • be able to search and evaluate scientific knowledge;
  • be able to work within multidisciplinary teams, while communicating results to stakeholders.

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