Bachelor Data Science

• Presentation

  Presentation

  Data Science, as defined by experts in the field, is a multidisciplinary experimental science that responds to the large volume of big data that has grown exponentially in the face of recent technological developments. The opportunity to transform this data into information and knowledge for the good of society is essential in business, science and other areas with a social impact. In this cycle of study we present a programme with strong components in: Statistics, Maths, Automated Learning, Programming, Data Extraction and Handling, various Data Engineering components and a strong focus on Privacy, Security and Ethics.

• 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

  • 10 Geometria Descritiva
  • 16 Matemática

  or

  • 13 Inglês
  • 16 Matemática

  or

  • 09 Geografia
  • 16 Matemática

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

• Who is this course for?

  Who is this course for?

  This cycle of studies is aimed at candidates with a background in Computer Science, Statistics, Physics, Mathematics, Management, Economics and Financial Sciences or similar, who wish to acquire skills in the area of Data Science.

• Knowledge, abilities and skills to be acquired

  Knowledge, abilities and skills to be acquired

  (1) complete experimental analysis cycles; (2) obtain scientific quality results with fluidity; (3) know how to integrate and work in multidisciplinary teams, (4) good ability to summarise and present results.

• Study plan

  Study plan

  • 1º Ano / General path
    • [1º Semestre]
    • Data Science Foundations 6 ects
    • Discrete Mathematics 6 ects
    • Mathematics I 6 ects
    • Probabilities and Statistics 6 ects
    • Programming Fundamentals 6 ects

    • [2º Semestre]
    • Exploratory Data Analysis 6 ects
    • Linear Algebra 6 ects
    • Mathematics II 6 ects
    • Numerical Analysis 6 ects
    • Programming Languages 6 ects
  • 2º Ano / General path
    • [1º Semestre]
    • Algorithms and Data Structures 6 ects
    • Data Science 6 ects
    • Databases 6 ects
    • Fundaments of Data Engineering 6 ects
    • Introduction to Artificial Intelligence 6 ects

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

    • [1º Semestre]
    • Computer vision 5 ects
    • Introduction to Privacy, Security and Ethics 5 ects
    • Machine Learning II 5 ects
    • Scientific Research Methodologies 5 ects

    • [2º Semestre]
    • Business Analytics 5 ects
    • Machine Learning III 5 ects
    • Operational Research 5 ects
    • Time Series 5 ects

• Course Team

  Course Team

  Afonso Oliveira

  Ana Cruz

  Andre Fonseca

  Bruno Saraiva

  Carlos Fernandes

  Duarte Neves

  Gabriela Soares

  Henrique Navas

  Houda Harkat

  Iolanda Velho

  João Silva

  João Carvalho

  Leonardo Carlo

  Luis Gomes

  Luís Campos

  Manuel Pita

  Maria Silva

  Martim Mourão

  Pedro Alves

  Rui Ribeiro

  Sofia Fernandes

  Sofia Naique

  Thiago Paiva

  Wellington Junior

  See all teachers

• Career Opportunities

  Career Opportunities

  - Data Scientist - Applied Data Scientist - Applied Data Researcher - Applied Machine Learning Researcher - Business Intelligence Analyst

• Scientific Areas

  Scientific Areas

  Ciências informáticas

• Observations

  Observations

  Assessment methodology (1) Expository, which explains the theoretical presentation according to the syllabus (2) Experimental, stimulates fundamental experimental development in data science (3) Active, promotes teamwork and (4) Self-study Teaching Model Face-to-face

Department of Computer Engineering and Information Systems

• DEISI Website