Big Data Analytics and Engineering

Cycle

Segundo ciclo

Degree

Mestre

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.

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).

Access conditions

Application requirements:

1. Holder of an undergraduate degree in any area pertaining to the Engineering, Exact Sciences, Natural Sciences or Economy, subject to curricular appreciation of the candidate. The program requires mathematical bases and notions of computation and programming at the level of a first general engineering cycle;
2. Holder of a higher education degree (first cycle) from a foreign institution in any one of the aforementioned areas (paragraph 1), organised in accordance with the Bologna process by a participating country;
3. Holder of a higher education degree from a foreign institution in any one of the aforementioned areas, considered by the Scientific Council of the School of Sciences and Technology of the New University of Lisbon to satisfy the prerequisites for an undergraduate degree;
4. Holder of academic, scientific or professional qualifications considered by the Scientific Council of the School of Sciences and Technology of the New University of Lisbon duly to attest to the candidate´s ability to undertake a corresponding cycle of studies.

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

Access to other courses

Access to a 3rd cycle

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
12507	Visualization and Data Analytics	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
12507	Visualization and Data Analytics	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
12507	Visualization and Data Analytics	6.0
O aluno deverá obter 6.0 créditos nesta opção.

2.º Year
Code	Name	ECTS
12085	Dissertation	60.0