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 / UNL, which 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:
- Evaluation based solely on an examination or completion of a final project.
- 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.
- 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.
- 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 |