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Master Degree in Data Driven Marketing

Education objectives

The Master Degree Program in Data Driven Marketing aims to train marketing specialists and managers with analytical tools applied in Marketing, to support decision making in this area. It is designed to include four alternative paths (specializations):

Applications - 4th 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 July 28th to August 17th, 2023. The selection process is based on the analysis of the applicant's academic and professional curriculum.

General characterization

DGES code

9435

Cicle

Master (2nd Cycle)

Degree

Master

Access to other programs

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

Coordinator

Paulo Miguel Rasquinho Ferreira Rita

Opening date

September 2023

Vacancies

Fees

€6.000

Schedule

Working hour and after working hours After working hours (Marketing Research and CRM)

Teaching language

English

Degree pre-requisites

The alternative paths translate to 4 specializations, each corresponding to 60 credits (ECTS), whose classes take place in the first two semesters. Upon the conclusion of the specialization, students will earn a postgraduate program diploma in the chosen specialization. Earning a Master's degree entails the development of a scientific thesis or work project, that must be original and made for this purpose only, carried out in the third and fourth semester of the program, that corresponds to 60 ECTS.

Conditions of admittance

To enter this program, applicants must meet the following requirements: hold a bachelor’s degree in a compatible field (completed by September); be proficient in English (spoken and written).

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

Paths

1º year - Autumn semester
Code Name ECTS
200204 Social Network Analysis 3.5
200201 Data Science for Marketing 7.5
200163 Experimental Design 4.0
200187 Marketing Strategy and Innovation 7.5
200197 Brand Management 3.5
200196 Digital Marketing & E-Commerce 7.5
200189 Descriptive Analytics in Marketing 7.5
400082 Digital Analytics 7.5
200204 Social Network Analysis 3.5
200201 Data Science for Marketing 7.5
200163 Experimental Design 4.0
200187 Marketing Strategy and Innovation 7.5
200197 Brand Management 3.5
200196 Digital Marketing & E-Commerce 7.5
200189 Descriptive Analytics in Marketing 7.5
400082 Digital Analytics 7.5
Options
200204 Social Network Analysis 3.5
200268 Applied Network Analysis 7.5
200012 Business Intelligence I 7.5
200201 Data Science for Marketing 7.5
200163 Experimental Design 4.0
200197 Brand Management 3.5
200014 Business Process Management 7.5
200073 Information Management Systems 3.5
200196 Digital Marketing & E-Commerce 7.5
200189 Descriptive Analytics in Marketing 7.5
400082 Digital Analytics 7.5
200192 Data Privacy, Security and Ethics 4.0
200194 Digital Transformation 3.5
200204 Social Network Analysis 3.5
200268 Applied Network Analysis 7.5
200012 Business Intelligence I 7.5
200201 Data Science for Marketing 7.5
200163 Experimental Design 4.0
200197 Brand Management 3.5
200014 Business Process Management 7.5
200073 Information Management Systems 3.5
200196 Digital Marketing & E-Commerce 7.5
200189 Descriptive Analytics in Marketing 7.5
400082 Digital Analytics 7.5
200192 Data Privacy, Security and Ethics 4.0
200194 Digital Transformation 3.5
1º year - Spring semester
Code Name ECTS
200203 Machine Learning in Marketing 7.5
200202 Big Data for Marketing 7.5
200170 Consumer Behavior Insights 7.5
200049 Market Research 7.5
200188 Marketing Engineering and Analytics 7.5
400081 Social Media Analytics 7.5
200190 Predictive Analytics in Marketing 7.5
200200 Search Engine Optimization 4.0
200203 Machine Learning in Marketing 7.5
200202 Big Data for Marketing 7.5
200170 Consumer Behavior Insights 7.5
200049 Market Research 7.5
200188 Marketing Engineering and Analytics 7.5
400081 Social Media Analytics 7.5
200190 Predictive Analytics in Marketing 7.5
200200 Search Engine Optimization 4.0
Options
200203 Machine Learning in Marketing 7.5
200202 Big Data for Marketing 7.5
200013 Business Intelligence II 7.5
200170 Consumer Behavior Insights 7.5
400019 Customer Relationship Management Systems 0.0
200049 Market Research 7.5
200014 Business Process Management 7.5
200071 Knowledge Management 3.5
400081 Social Media Analytics 7.5
200190 Predictive Analytics in Marketing 7.5
200200 Search Engine Optimization 4.0
200184 Sampling Theory and Methods 7.5
200298 Data-driven decision making 4.0
200203 Machine Learning in Marketing 7.5
200202 Big Data for Marketing 7.5
200013 Business Intelligence II 7.5
200170 Consumer Behavior Insights 7.5
400019 Customer Relationship Management Systems 0.0
200049 Market Research 7.5
200014 Business Process Management 7.5
200071 Knowledge Management 3.5
400081 Social Media Analytics 7.5
200190 Predictive Analytics in Marketing 7.5
200200 Search Engine Optimization 4.0
200184 Sampling Theory and Methods 7.5
200298 Data-driven decision making 4.0
2º year - Autumn semester
Code Name ECTS
200265 Dissertation/Internship Report/Project 54.0
200086 Research Methodologies 6.0
200265 Dissertation/Internship Report/Project 54.0
200086 Research Methodologies 6.0