Specialization in Data Science for Marketing
Education objectives
The Master Program in Data Driven Marketing, with a specialization 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 - 2nd 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 April 14th to May 18th, 2023. The selection process is based on the analysis of the applicant's academic and professional curriculum.
General characterization
DGES code
94353
Cicle
Specialization Area
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
After working hours
Teaching language
English
Degree pre-requisites
This path is an option of
Master Degree in Data Driven Marketing
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
| 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 |
| 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 |
| 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 |
| 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 |
| 2º year - Autumn semester | ||
|---|---|---|
| Code | Name | ECTS |
| 200265 | Dissertation/Internship Report/Project | 54.0 |
| 200086 | Research Methodologies | 6.0 |