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 NOVA, which 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 |