Bachelor Computation and Applied Mathematics
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 Read more
Course formatIn-Person
DegreeBachelor
Semesters6
Credits180
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What will I learn in this course?
What will I learn in this course?
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.
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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
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or- 04 Economia
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- 16 Matemática
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or- 07 Física e Química
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- 16 Matemática
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or- 16 Matemática
- and
- 18 Português
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or- 02 Biologia e Geologia
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- 16 Matemática
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or- 09 Geografia
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- 16 Matemática
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or- 13 Inglês
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- 16 Matemática
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Pass in 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 the programme by changing institution/course pair, or by means of a Special Competition, in accordance with the legal rules in force (see below). in accordance with 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.
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Technical and professional skills that I will acquire
Technical and professional skills that I will acquire
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.
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Study plan and curriculum structure
Study plan and curriculum structure
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1º Ano / Common Core
- [1º Semestre]
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Data Science Foundations
6 ects -
Discrete Mathematics
6 ects -
Mathematics I
6 ects -
Numerical Analysis
6 ects -
Programming Fundamentals
6 ects
- [2º Semestre]
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Exploratory Data Analysis
6 ects -
Introduction to Mathematical Probability
6 ects -
Linear Algebra
6 ects -
Mathematics II
6 ects -
Programming Languages
6 ects
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2º Ano / Common Core
- [1º Semestre]
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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]
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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
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3º Ano / Common Core
- [Anual]
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Final Course Work
20 ects
- [1º Semestre]
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Machine Learning II
5 ects -
Operational Research
5 ects - Option I
- Option II
- [2º Semestre]
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Cloud Information Systems
5 ects -
Computer Networks
5 ects - Option III
- Option IV
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1º Ano / Common Core
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Options
Options
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Career opportunities and career prospects
Career opportunities and career prospects
Data Scientist Computer Scientist Business Intelligence Analyst Machine Learning & A.I. Scientist Solid background to pursue a Ph.D programme.
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Scientific areas of the course
Scientific areas of the course
Ciências informáticas
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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)
Porto | Fac.Ciênc.Naturais,Engª e Tecnol.
Current Application Dates
Fees
Course Direction
Secretariat
Agência de Avaliação e Acreditação do Ensino Superior
Graduation Requirements
Have completed a total of 180 ECTS.
Mobility for Students and Academic Staff
Taking part in a mobility programme is the perfect opportunity to broaden your horizons through a rich cultural, academic and personal experience.
Learn more
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