Bachelor Data Science

• What will I learn in this course?

  What will I learn in this course?

  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.

• Entry requirements and conditions

  Entry requirements and conditions

  Those who apply through the institutional admission process may apply for this study program. See the required exams below.
  
  

  • 16 Matemática
  • and

  or

  • 04 Economia
  • and
  • 16 Matemática
  • and

  or

  • 07 Física e Química
  • and
  • 16 Matemática
  • and

  or

  • 16 Matemática
  • and
  • 18 Português
  • and

  or

  • 10 Geometria Descritiva
  • and
  • 16 Matemática
  • and

  or

  • 13 Inglês
  • and
  • 16 Matemática
  • and

  or

  • 09 Geografia
  • and
  • 16 Matemática
  • and

  
  Pass one of the following sets of entrance exams: Maths (16) or Maths (16) and Economics (04) or Maths (16) and Physics and Chemistry (07) 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.

• Profile of the candidates for whom the course is intended

  Profile of the candidates for whom the course is intended

  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.

• Technical and professional skills that I will acquire

  Technical and professional skills that I will acquire

  (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 and curriculum structure

  Study plan and curriculum structure

  • 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
    • Introduction to Artificial Intelligence 6 ects
    • Introduction to the Theory of Graphs and Networks 6 ects

    • [2º Semestre]
    • Artificial Intelligence 6 ects
    • Fundaments of Data Engineering 6 ects
    • Introduction to Stochastic Processes 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

  Ana Cruz

  Andre Fonseca

  Bruno Saraiva

  Carlos Fernandes

  Gabriela Soares

  Giosuè Muratore

  Henrique Navas

  Houda Harkat

  Iolanda Velho

  José Paredes

  João Silva

  João Eleutério

  João Moreira

  Leonardo Carlo

  Luis Gomes

  Luís Campos

  Manuel Pita

  Maria Silva

  Marina Martins

  Martim Mourão

  Pedro Alves

  Rui Ribeiro

  Sofia Fernandes

  Sofia Naique

  Thiago Paiva

  Zuil Filho

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• Career opportunities and career prospects

  Career opportunities and career prospects

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

• Scientific areas of the course

  Scientific areas of the course

  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