PostGraduate in Data Science for Marketing

Education objectives

The Postgraduate program in Data Science for Marketing aims to fill a gap in the postgraduate training of marketing professionals who need to gain new skills to be able to actively participate in the development and application of analytical marketing models. With the proposed study plan, this Post-graduation presents an up-to-date structure that combines several areas of marketing with a transversal approach of data science to leverage them.

This program is designed to provide excellent training, articulating key concepts and challenges for marketing decision-making in its multiple strategic, innovation and methodological strands with practical data-oriented processing (data science & big data), artificial intelligence (machine learning) and analysis of social networks of consumers. The versatility in the offer of optional curricular units also allows to reinforce theoretical and practical knowledge in several related areas such as digital marketing, social media, e-commerce and search engine optimization.

Applications - 3rd 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 from June 21st to July 21st, 2022. The selection process is based on the analysis of the applicant's academic and professional curriculum.

General characterization

DGES code



Postgraduate programmes



Access to other programs

To earn the postgraduate program diploma in Data Science for Marketing, students complete a total of 60 ECTS, of which 41,5 are mandatory and the remaining 18,5 ECTS will be chosen by thestudents from a wide range of available course units.


Paulo Miguel Rasquinho Ferreira Rita

Opening date

September 2022





After Working Hours

Teaching language


Degree pre-requisites

To earn the postgraduate program diploma in Data Science for Marketing, students complete a total of 60 ECTS, of which 41 are mandatory and the remaining 19 ECTS will be chosen by the students from a wide range of available course units.

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


1º year - Autumn semester
Code Name ECTS
200204 Social Network Analysis 3.5
200201 Data Science for Marketing 7.5
200187 Marketing Strategy and Innovation 7.5
200012 Business Intelligence I 7.5
200163 Experimental Design 4.0
200195 Information Systems Development 4.0
400020 Information Systems Governance 3.5
200197 Brand Management 3.5
200014 Business Process Management 7.5
200071 Knowledge Management 3.5
200070 Information Technologies Services Management 4.0
200073 Information Management Systems 3.5
200193 Data Management and Storage 4.0
200189 Descriptive Analytics in Marketing 7.5
400082 Digital Analytics 7.5
200165 Descriptive Methods of Data Mining 7.5
200291 Descriptive Methods of Data Mining 7.5
200192 Data Privacy, Security and Ethics 4.0
1º year - Spring semester
Code Name ECTS
200203 Machine Learning in Marketing 7.5
200202 Big Data for Marketing 7.5
200188 Marketing Engineering and Analytics 0.0
200198 Analysis of Discrete Data 4.0
200205 Analysis of Variance 4.0
200210 Architectures for Information Systems 3.5
200167 Big Data Analytics 7.5
200269 Blockchain 4.0
200013 Business Intelligence II 7.5
200135 Cybersecurity 7.5
200261 Smart and Sustainable Cities 7.5
200170 Consumer Behavior Insights 7.5
400019 Customer Relationship Management Systems 7.5
200162 Data Visualization 7.5
400055 Enterprise Resource Management Systems 7.5
200049 Market Research 7.5
200068 Information Project Management 4.0
200268 Graph Analytics powered by Nokia 7.5
200146 Innovation Management and Design Thinking 7.5
400023 Leadership and People Management 7.5
200196 Digital Marketing & E-Commerce 7.5
400081 Social Media Analytics 7.5
200190 Predictive Analytics in Marketing 7.5
200166 Predictive Methods of Data Mining 7.5
200200 Search Engine Optimization 4.0
200296 Process Mining Powered By Nokia 7.5
200194 Digital Transformation 3.5