Bachelor Computation and Applied Mathematics

• Presentation

  Presentation

  1. With the exponential advance in the volume of `big data¿ and its digital transformation over the last decade and a half, we have witnessed and continue to observe a phase of constant reinvention not only of the infrastructures and models of information systems, but also the reinvention of new profiles of professionals in the transformation of data into knowledge. 2. It can be seen that professionals trained in areas related to traditional `Maths¿ have shown a very strong basis for facing this challenge. However, these professionals have made a great effort to adapt to IT infrastructures. 3. The aim is to maintain the analytical thinking of these professionals but also to develop skills in computer engineering.

• Admission Conditions

  Admission Conditions

  This study cycle accepts candidates who submit their application via the institutional entry admission and have obtained a positive score on one of the following sets:
  
  

  • 04 Economia
  • 16 Matemática

  or

  • 07 Física e Química
  • 16 Matemática

  or

  • 16 Matemática
  • 18 Português

  or

  • 02 Biologia e Geologia
  • 16 Matemática

  or

  • 09 Geografia
  • 16 Matemática

  or

  • 13 Inglês
  • 16 Matemática

  
  
  Candidates can also enter through the change of institution/course pair system, or through a Special Competition, in accordance with the legal rules in force. according to the legal rules in force (holders of higher education courses, holders of CETs, holders of CTSPs, over 23s, holders of dual certification courses). courses). Candidates who fulfil the conditions laid down in the International Student Statute may also enter.

• Knowledge, abilities and skills to be acquired

  Knowledge, abilities and skills to be acquired

  1. Theoretical, methodological and practical foundations in the areas of computer science, applied mathematics and computer engineering. In particular: Maths, Statistics, Machine Learning, Artificial Intelligence and Computer Engineering. 2. Know how to handle large volumes of data within data protection laws, create and implement new and current algorithms and mathematical models in A.I. 3. Obtain scientific quality results with fluidity; know how to integrate and work in multidisciplinary teams; good synthesis skills.

• Study plan

  Study plan

  • 1º Ano / Common Core
    • [1º Semestre]
    • Data Science Foundations 6 ects
    • Discrete Mathematics 6 ects
    • Introduction to Mathematical Probability 6 ects
    • Mathematics I 6 ects

    • [2º Semestre]
    • Exploratory Data Analysis 6 ects
    • Linear Algebra 6 ects
    • Mathematics II 6 ects
    • Numerical Analysis 6 ects
    • Programming Fundamentals 6 ects
    • Programming Languages 6 ects
  • 2º Ano / Common Core
    • [1º Semestre]
    • Algorithms and Data Structures 6 ects
    • Databases 6 ects
    • Fundaments of Data Engineering for Data Science 6 ects
    • Introduction to Computer Science 6 ects
    • Introduction to Ordinary Differencial Equations 6 ects

    • [2º Semestre]
    • Introduction to Stochastic Processes 6 ects
    • Introduction to the Theory of Graphs and Networks 6 ects
    • Machine Learning I 6 ects
    • Statistical Methods 6 ects
    • Visualization for Data Science 6 ects
  • 3º Ano / Common Core
    • [Anual]
    • Final Course Work 20 ects

    • [1º Semestre]
    • Machine Learning II 5 ects
    • Operational Research 5 ects
    • Option I
    • Option II

    • [2º Semestre]
    • Cloud Information Systems 5 ects
    • Computer Networks 5 ects
    • Option III
    • Option IV

• Options

  Options

  • Option I
    • Abstract Algebra (Option I) 5 ects
    • Calculus III (Option I) 5 ects
  • Option II
    • Introduction to Privacy, Security and Ethics (Option II) 5 ects
    • Scientific Research Methodologies (Option II) 5 ects
  • Option III
    • Artificial Intelligence (Option III) 5 ects
    • Reinforcement Learning and Optimal Control (Option III) 5 ects
  • Option IV
    • Introduction to Criptography (Option IV) 5 ects
    • Introduction to Fuzzy Logic (Option IV) 5 ects

• Course Team

  Course Team

  Aleksandar Mikovic

  Ana Cruz

  Andre Fonseca

  Berend Kuipers

  Bruno Saraiva

  Duarte Neves

  Gabriela Soares

  Guilherme Antunes

  Henrique Navas

  Iolanda Velho

  Leonardo Carlo

  Luis Gomes

  Maria Silva

  Martim Mourão

  Pedro Alves

  Rui Ribeiro

  Sofia Fernandes

  Sofia Naique

  Wellington Junior

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• Career Opportunities

  Career Opportunities

  Data Scientist Computer Scientist Business Intelligence Analyst Machine Learning & A.I. Scientist Solid background to pursue a Ph.D programme.

• Scientific Areas

  Scientific Areas

  Ciências informáticas

• Observations

  Observations

  Assessment methodology The programme offers a strong technical introduction to the student in the areas mentioned, through methodologies: (1) Expository, (2) Experimental, (3) Active, promoting teamwork and (4) Self-Study. Teaching Model B-learning (Mixed)

Department of Computer Engineering and Information Systems

• DEISI Website