Big Data Analytics and Engineering

Education objectives

The Master''''s Degree in Analysis and Engineering of Big Data aims to train specialists at the level of a 2nd cycle of studies in the emerging field of Data Science and Data Engineering, and is intended for candidates with a background at the level of a 1st cycle of studies including mathematical and programming bases.

The course develops competencies regarding the processing and analysis of large volumes of data by advanced computational and mathematical methods, and methodologies to seek and find necessary answers to management, monitoring and optimization processes, or extract knowledge, trends, correlations, or predictions, in particular through automatic learning.

The objectives of the course are aligned with the "National Digital Competence Initiative e.2030", in the areas of specialisation (item qualification and creation of added value in economics) and research (big data item).

General characterization

DGES code

994

Cicle

Master (2nd Cycle)

Degree

Mestre

Access to other programs

Access to a 3rd cycle

Coordinator

Pedro Manuel Corrêa Calvente Barahona

Opening date

September

Vacancies

25

Fees

1063,47 Euros/year or 7000,00 Euros/year (for foreign students).

Schedule

Daytime

Teaching language

Available soon

Degree pre-requisites

Duration: 2 years

Credits: 120 ECTS

Mandatory scientifc areas

Scientific Area Acronym ECTS
Mandatory Optional
Computer Science and Informatics I 18 6
Mathematics M 12 6

Mathematics or Computer Science and Informatics

M/I  63 6
Transferable Skills CC   3  0
 Any Scientific Area   QAC  -  6 a)
TOTAL 96 24

a) 6 ECTS in courses chosen by the student on a list approved annually by the Scientific Council of FCT / UNLwhich includes the unity of all scientific areas of FCT / UNL

Conditions of admittance

Available soon

Evaluation rules

The following modes of evaluation are used with regard to academic qualifications:

  1. Evaluation based solely on an examination or completion of a final project.
  2. Evaluation based on work done throughout the semester excluding examination or final project. In these courses students can expect to carry out, for example, laboratory activities, mini-tests, tests, individual or group projects, seminar-related activities, any combination of which will be used to determine the final grade.
  3. Evaluation based obligatorily on an examination or a final project. In these courses there extists a form of evaluation similar to one of the aformentioned activities in paragraph 2 as well as a form of evaluation based on a final exam.
  4. Evaluation based on work done throughout the semester with the possibility of foregoing an examination or a final project.

The final Dissertation (or Project) involves a public discussion with a Jury.

Structure

1.º Semester
Code Name ECTS
11157 Machine Learning 6.0
8518 Multivariate Statistics 6.0
10810 Computational Numerical Statistics 6.0
12077 Information Retrieval 6.0
12078 Systems for Big Data Processing 6.0
2.º Semester
Code Name ECTS
10380 Entrepreneurship 3.0
12079 Seminar 3.0
2.º Semester - Unidade Curricular de Bloco Livre
Code Name ECTS
Options
11066 Electives 6.0
2.º Semester - Unidade de Especialização I
Code Name ECTS
Options
12083 Algorithms for Complex Networks 6.0
12082 Large Graph Analytics 6.0
12084 Learning from Unstructured Data 6.0
12081 Decision and Risk 6.0
12080 Bayesian Methods 6.0
12145 Linear Optimization 6.0
10808 Non Linear Optimization 6.0
11562 Stream Processing 6.0
11563 Data Analytics and Mining 6.0
11565 Interactive Data Visualization 6.0
O aluno deverá obter 6.0 créditos nesta opção.
2.º Semester - Unidade de Especialização II
Code Name ECTS
Options
12083 Algorithms for Complex Networks 6.0
12082 Large Graph Analytics 6.0
12084 Learning from Unstructured Data 6.0
12081 Decision and Risk 6.0
12080 Bayesian Methods 6.0
12145 Linear Optimization 6.0
10808 Non Linear Optimization 6.0
11562 Stream Processing 6.0
11563 Data Analytics and Mining 6.0
11565 Interactive Data Visualization 6.0
O aluno deverá obter 6.0 créditos nesta opção.
2.º Semester - Unidade de Especialização III
Code Name ECTS
Options
12083 Algorithms for Complex Networks 6.0
12082 Large Graph Analytics 6.0
12084 Learning from Unstructured Data 6.0
12081 Decision and Risk 6.0
12080 Bayesian Methods 6.0
12145 Linear Optimization 6.0
10808 Non Linear Optimization 6.0
11562 Stream Processing 6.0
11563 Data Analytics and Mining 6.0
11565 Interactive Data Visualization 6.0
O aluno deverá obter 6.0 créditos nesta opção.
2.º Year
Code Name ECTS
12085 Dissertation 60.0