Master Degree in Statistics for Data Science

The Master in Statistics for Data Science aims to provide scientific training in the areas of Statistics and Data Science, empowering students with solid skills in the theoretical foundations of these scientific areas, complemented with the practical knowledge necessary for intervention in an applied context. Statistics and Data Science are crucial areas for the analysis and processing of large amounts of data with the aim of extracting knowledge and information in a sustained manner to support decision making. This cycle of studies enables students to develop skills and knowledge that are essential, both for scientific research activity in these areas and for application in market segments of increasing employability for qualified professionals in these areas.

This cycle of studies will prepare graduates to access the labor market in various areas such as consulting firms, banks and insurance companies, media, health-related institutions, governmental entities and municipalities, universities and large industrial companies, or to pursue a research career, either in academic context or integrating research and development groups.

This course is organized in 4 semesters. The first semester is made up of Curricular Units (CU) aimed at a basic training in Statistics. The second and third semesters provide a set of CUs to complement the training, including optional CUs covering a wide range of specific statistical and data analysis methodologies and techniques. In part of the third semester and in the fourth semester, students devote themselves to the writing of a dissertation, which can be developed in the context of a scientific thesis at the University of Minho, or integrated as a task of a research project, or even as a professional traineeship carried out in a company or in an external institution.

The course teaching staff comprises PhD researchers with high scientific production, members of research centers at the University of Minho and principal investigators in funded research projects, with qualifications providing scientific expertise in different scientific fields studied in the Master in Statistics for Data Science. The teaching and learning methodologies adopted stimulate students’ autonomous and collaborative work via classes with a strong computational component.

Fees (non-refundable)

Number of vacancies

Access requirements

Ranking criteria

The ranking of candidates will be based on the ranking resulting from the application of the following algorithm:

Final classification [0-20] = 0.75 A + 0.10 B + 0.15 C

where components A, B and C are defined below.

It is also considered the following table 1, which defines the P weight given to the candidate's previous type of training:

	P
Bachelor in Applied Statistics, Mathematics, Applied Mathematics, Computer Science or similar	1
Bachelor in Engineering, Physics, Economics, Management or similar	0,8
Bachelor in Medicine, Biology, Pharmaceutical Sciences or similar	0,7
Bachelor in Psychology, Social Sciences or similar	0,5
Bachelor in other related areas	0,5

Component A:

Component A is calculated by the formula:

A=P*FC

where P is defined in table 1, according to the previous training of the candidate, and FC corresponds to the final classification of the degree from 0 to 20.

Component B:

Component B, valued from 0 to 20, is calculated as the sum of the ratings attributed by the Master's Course Steering Committee to other relevant aspects in the scientific area of the cycle of studies such as presentations at scientific events, authorship of published works, participation in research projects, conducting internships, etc.

Component C:

Component C, valued from 0 to 20, is calculated as the sum of the ratings attributed by the Master's Course Steering Committee to the various dimensions of the candidate's technical and professional curriculum, in the area of the cycle of studies, as relevant professional activity, coordination and participation in projects, professional training, etc.

Applications 2024/2025 »»

2023/2024

General

Academic degree:

Master's degree

ECTS:

120

Duration:

4 curricular semesters

Regime:

Daytime

Place:

Azurém Campus, Guimarães (UM)

Main Scientific area:

Statistics

Contacts

Course Director

Arminda Manuela Andrade Pereira Gonçalves

mneves@math.uminho.pt

253510433

Mobility Academic Coordinator

Inês Pereira Silva Cunha Sousa

isousa@math.uminho.pt

+351 253510427

School of Sciences

Campus de Gualtar
4710 - 057 Braga

+351 253604390

+351 253604398

cpedagogico@ecum.uminho.pt; sec@ecum.uminho.pt

http://www.ecum.uminho.pt

Description

The course is structured in 4 semesters. The first semester includes courses that give a first training on statistical concepts and techniques. Courses in the second and third semesters complement the training in the statistical methods as each course focuses in specific statistical models for data with specificities. The dissertation of the Master starts in the first semester of the second year, being the second semester of the second year totally dedicated to the dissertation. Notice that, the dissertation can be developed as a scientific thesis at University of Minho, as a task integrated in an existing research project or as a professional internship at a company or external institution. In the vast majority of this CU, students have contact with real data problems. Students usually have to write reports based on these problems, and present the results in a seminar. This course allows the acquisition of a wide range of skills and competences, namely, problem solving skills, data science skills and ability to integrate interdisciplinary teams.

Key learning outcomes

1. Stimulate the interest and knowledge of several areas of Statistics.
2. Stimulate the theoretical and applied knowledge in Data Science.
3. Develop skills in analyzing large quantities of data.
4. Stimulate the critical spirit.
5. Develop skills for decision making in the context of uncertainty.
6. Develop skills to formulate suitable statistical models for new problems, fit these models to real data and correctly interpret results.
7. Develop competencies to develop, in a relatively autonomous way, basics to research topics.
8. Develop communication skills to different audiences (experts or not).
9. Develop capacity and self-discipline for managing an individual project of relatively long term.
10. Develop ability to work by objectives, essential for individual work or for work integrated in interdisciplinary teams.

Access to higher education

This Master degree provides basic training to carry out a course of 3rd cycle (PhD) in the same scientific area, or in other areas.

Careers

Statistics and Data Science MSc aims to train professionals that are able to integrate the job market at the level of senior technician, positioning themselves in various areas, such as industry, services, banking institutions and insurance companies, public administration, health-related organizations, universities and enterprises that process large quantities of data.

Access

25

Vacancies

12

Minimum

Access forms

The access to this Cycle of Study can be done at the Students Portal of the University of Minho
https://alunos.uminho.pt/EN/candidates/MastersDegrees/Pages/default.aspx

This information does not replace consultation of the Academic Regulations of the University of Minho and other official documents.

Formal requirements

Can be candidates to the Study Program holders of a first degree (or legally equivalent qualification) in Mathematics, Applied Mathematics, Engineering, Computation Sciences, Physics, or with other degrees of related scientific nature,
with at least 120 ECTS.
Are applied other conditions mentioned in Article nº 5 of the bylaws of the Study Program (Resolução SU-101/2006).
During the process of classification of candidates, it is considered the final classification of the previous degree (i.e. bachelor, etc), and the affinity of the degree with the study cycle. Other aspects that are also considered include the previous research activities, participation in conferences, scientific publications, among other.

Application deadlines

Information on Application Deadlines can be found at:
https://www.ecum.uminho.pt/en/Ensino/Pages/Applications-for-Master-Programmes-.aspx

Registration deadlines

Information on Registration Deadlines can be found at:
https://www.ecum.uminho.pt/en/Ensino/Pages/Applications-for-Master-Programmes-.aspx

Aditional Information

Study Plan

When applicable, the elective curricular units available as well as the registration conditions and the number of vacancies will be indicated annually.

Regime	Curricular Unit	Scientific Area	ECTS
Year 1			60
S1	Computational Statistics	EST	7.5
S1	Fundamentals and Applications of Statistics	EST	7.5
S1	Linear Models	EST	7.5
S1	Statistical Inference	EST	7.5
S2	Forecasting Methods and Time Series	EST	5
S2	Generalized Linear Models and Applications	EST	7.5
S2	Multivariate Statistical Analysis	EST	7.5
S2	Theory of Statistical Learning	EST	5
S2	Opção 1		5
	Bayesian Statistics and Applications	EST	5
	Optimization in Machine Learning	I	5
	Sampling and Experimental Design	EST	5
	Spatial Statistics	EST	5
	Survival Analysis	EST	5
Year 2			60
S1	Project in Statistics	EST	20
S1	Opção 2		5
	Metrics in Machine Learning	I	5
	Models for Longitudinal Data	EST	5
	Probability Applications and Stochastic Modeling	EST	5
	Sampling and Experimental Design	EST	5
	Statistical Modeling of Extreme Values	EST	5
S1	Opção 3		5
	Bayesian Statistics and Applications	EST	5
	Risk Theory	EST	5
	Spatial Statistics	EST	5
	Stochastic Simulation	EST	5
	Survival Analysis	EST	5
S2	Dissertation	EST	30

Assessment/Accreditation

A3ES Accreditation status:

Accredited

Resolution publication date:

04-09-2020

A3ES deliberation:

https://www.a3es.pt/pt/resultados-acreditacao/estatistica-para-ciencia-de-dados

Registry number at Ministry:

R/A-Ef 2507/2011/AL01

Registry Date:

09-04-2021

Accreditation validity:

30-07-2026

Master Degree in Statistics for Data Science

Fees (non-refundable)

Number of vacancies

Access requirements

Ranking criteria

General

Contacts

Description

Key learning outcomes

Access to higher education

Careers

Access

25

Vacancies

12

Minimum

Access forms

Formal requirements

Application deadlines

Registration deadlines

Aditional Information

Study Plan

Assessment/Accreditation

Useful links

School of Sciences

Campus de Gualtar

Information

Social Media

Master Degree in Statistics for Data Science

Fees (non-refundable)

Number of vacancies

Access requirements​

Ranking criteria​

General

Contacts

Description

Key learning outcomes

Access to higher education

Careers

Access

25

Vacancies

12

Minimum

Access forms

Formal requirements

Application deadlines

Registration deadlines

Aditional Information

Study Plan

Assessment/Accreditation

Access requirements

Ranking criteria