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).

Career opportunities

The Master''s Degree in Analysis and Engineering of Big Data is aimed at educating analysts, project development leaders and innovation experts in the emerging field of Data Science and Engineering. Experts in Big Data are lacking and intensively looked after by companies and institutions where large volumes of data are generated or consumed, namely in health, public administration, e-commerce and marketing, finance, energy, environment and urban planning, telecommunications, media and social communication, and pharmaceutical or biotechnological industry. 

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

Portuguese students: 2000 €/year

Foreign students: 7000 €/year

Schedule

Daytime

Teaching language

Available soon

Degree pre-requisites

Duration: 2 years

Credits: 120 ECTS

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 0  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 NOVAwhich includes the unity of all scientific areas of FCT NOVA.

Conditions of admittance

Available soon

Evaluation rules

The evaluation of all UCs is continuous for all the components that integrate it, and it must be completed by the last day of the school term of the academic semester.

The continuous evaluation of a UC must include a minimum of three elements in the set of evaluation components, on dates adequately spaced throughout the period of classes.

All UCs with a theoretical-practical evaluation component must provide, in addition, a form of evaluation of this component by exam, to be carried out after the period of classes (Examination of Appeal).

All requirements and conditions related to the evaluation of the UC, namely the minimum weights and classifications, if any, of each component, as well as the Frequency conditions, are defined a priori and, mandatorily, published in the Discipline Form.

For each UC, combinations of three evaluation components are allowed: (i) Theoretical-practical evaluation; (ii) Laboratory or project evaluation; (iii) Summative assessment.

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

Regulamento de Avaliação de Conhecimentos (Licenciaturas, Mestrados Integrados e Mestrados.)

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 Unrestricted Electives 6.0
O aluno deverá obter 6.0 créditos nesta opção.
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
Unidade Curricular de Bloco Livre - O estudante deverá realizar 6 ECTS de entre as UC que integram o Bloco Livre FCT, aprovado anualmente pelo Conselho Científico da FCT NOVA, o qual inclui unidades de todas as áreas científicas da FCT NOVA.