Specialization in Data Science

Education objectives

The Master Degree Program in Data Science and Advanced Analytics, with a specialization in Data Science, is aimed at technically oriented people with solid scientific background, who want to strengthen and deepen their skills on the most used paradigms and environments for software development, and apply them to solve complex real-world problems involving vast amounts of data.

The best students of the 1st year of the Master in Data Science and Advanced Analytics will be invited for a 6 month paid internship, to be held during the 2nd year, in one of the following institutions: Accenture, BI4ALL, Feedzai, Future Healthcare, Grupo Ageas Portugal, iFood, Izertis, Millennium BCP, NOVA IMS, SAS and Tranquilidade.

This Program will provide a set of interdisciplinary skills and tools such as:

  • Understanding of the main paradigms associated with large databases and data warehouses;
  • Understanding the processes of decision making;
  • Mastering data mining tools, in particular for "Big Data" related problems;
  • Mastering the processes of creation and maintenance of descriptive and predictive models;
  • Recognizing and applying the most effective analytical models to different business cases;
  • Interpreting models and their implications to the business.

Applications - 1st call
To complete the application, the applicant must register in NOVA IMS' Applications Portal, fill the form, upload their Curriculum Vitae, pay the application fee (€ 51), and submit the application in the end. The selection process is based on the analysis of the applicant's academic and professional curriculum. The members of the Admissions’ Jury Panel may decide to hold an interview with all or some applicants - face-to-face or videoconference.

The applications for this program, for the 2025/2026 academic year, are open from January 1st to February 3rd, 2025. To apply, click here.

General characterization

DGES code

MB68

Cicle

Specialization Area

Degree

Master

Access to other programs

This Master Degree gives acess to a Doctoral Program, confirm ours here.

Coordinator

Roberto Henriques

Opening date

September 2025

Vacancies

105

Fees

€6.200 for applicants with a nationality from a European Union member country; €8.000 for applicants of other nationalities.

Schedule

Working Hours

Teaching language

English

Degree pre-requisites

This program lasts 4 semesters: 3 correspond to the curricular component and 1 to the development of a thesis or work project, in a total of 120 ECTS.

Conditions of admittance

The requirements for the applications are: a degree in a compatible field (complete until September); analysis of the applicants' academic and professional curriculum.

Evaluation rules

The assessment method will be continuous assessment, i.e. through individual or group work, projects, quizzes, tests/exams, etc.

At the beginning of each academic year, it is up to each teacher of each Curricular Unit (CU) to define how assessment will be carried out in their CUs, and this information is then made available via the student platform.

Structure

1º year - Autumn semester
Code Name ECTS
200179 Machine Learning 7.5
200174 Storing and Retrieving Data 4.0
200175 Data Mining 7.5
200178 Statistics for Data Science 7.5
200211 Programming for Data Science 3.5
200179 Machine Learning 7.5
200174 Storing and Retrieving Data 4.0
200175 Data Mining 7.5
200178 Statistics for Data Science 7.5
200211 Programming for Data Science 3.5
200179 Machine Learning 7.5
200174 Storing and Retrieving Data 4.0
200175 Data Mining 7.5
200178 Statistics for Data Science 7.5
200211 Programming for Data Science 3.5
1º year - Spring semester
Code Name ECTS
200180 Deep Learning 3.5
200282 Big Data Analytics 7.5
200011 Business Intelligence 7.5
200208 Business Cases with Data Science 7.5
200278 Business Process Management 3.5
200142 Computational Intelligence for Optimization 7.5
200181 Text Mining 4.0
200283 Digital Transformation 4.0
200180 Deep Learning 3.5
200282 Big Data Analytics 7.5
200011 Business Intelligence 7.5
200208 Business Cases with Data Science 7.5
200278 Business Process Management 3.5
200142 Computational Intelligence for Optimization 7.5
200181 Text Mining 4.0
200283 Digital Transformation 4.0
200180 Deep Learning 3.5
200282 Big Data Analytics 7.5
200011 Business Intelligence 7.5
200208 Business Cases with Data Science 7.5
200278 Business Process Management 3.5
200142 Computational Intelligence for Optimization 7.5
200181 Text Mining 4.0
200283 Digital Transformation 4.0
Options
200167 Big Data Analytics 7.5
200208 Business Cases with Data Science 7.5
200292 Ethics in Data Science 3.5
200142 Computational Intelligence for Optimization 7.5
200207 Big Data Modelling and Management 3.5
200294 Neural and Evolutionary Learning 4.0
200293 Machine Learning Operations 3.5
200295 Reinforcement Learning 4.0
200176 Data Visualization 4.0
200167 Big Data Analytics 7.5
200208 Business Cases with Data Science 7.5
200292 Ethics in Data Science 3.5
200142 Computational Intelligence for Optimization 7.5
200207 Big Data Modelling and Management 3.5
200294 Neural and Evolutionary Learning 4.0
200293 Machine Learning Operations 3.5
200295 Reinforcement Learning 4.0
200176 Data Visualization 4.0
200167 Big Data Analytics 7.5
200208 Business Cases with Data Science 7.5
200292 Ethics in Data Science 3.5
200142 Computational Intelligence for Optimization 7.5
200207 Big Data Modelling and Management 3.5
200294 Neural and Evolutionary Learning 4.0
200293 Machine Learning Operations 3.5
200295 Reinforcement Learning 4.0
200176 Data Visualization 4.0
2º year - Autumn semester
Code Name ECTS
200290 Dissertation/ Work Project 54.0
200289 Research Methods 6.0
200290 Dissertation/ Work Project 54.0
200289 Research Methods 6.0
200290 Dissertation/ Work Project 54.0
200289 Research Methods 6.0