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 - 2nd call for applications
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 7th to May 13th, 2021. The selection process is based on the analysis of the applicant's academic and professional curriculum.
General characterization
DGES code
4977
Cicle
Postgraduate programmes
Degree
None
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.
Coordinator
Opening date
September 2021
Vacancies
Fees
€4.100
Schedule
After Working Hours
Teaching language
English
Degree pre-requisites
Conditions of admittance
The requirements for the applications are: a degree in a compatible field (complete until September 2021); analysis of the applicants' academic and professional curriculum.
Evaluation rules
Structure
| 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 |
| Options | ||
| 200012 | Business Intelligence I | 7.5 |
| 200163 | Experimental Design | 4.0 |
| 200195 | Information Systems Development | 4.0 |
| 400082 | Digital Analytics | 7.5 |
| 400020 | Information Systems Governance | 3.5 |
| 200197 | Brand Management | 3.5 |
| 200070 | Information Technologies Services Management | 4.0 |
| 200071 | Knowledge Management | 7.5 |
| 200073 | Information Systems Management | 3.5 |
| 200193 | Data Management and Storage | 4.0 |
| 200196 | Digital Marketing & E-Commerce | 7.5 |
| 200189 | Descriptive Analytics in Marketing | 0.0 |
| 200165 | 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 | 7.5 |
| Options | ||
| 200210 | Architectures for Information Systems | 3.5 |
| 200013 | Business Intelligence II | 7.5 |
| 200014 | Business Process Management | 3.5 |
| 200170 | Consumer Behavior Insights | 7.5 |
| 200049 | Market Research | 7.5 |
| 200068 | Information Project Management | 4.0 |
| 200190 | Predictive Analytics in Marketing | 7.5 |
| 200200 | Search Engine Optimization | 4.0 |
| 400081 | Social Media Analytics | 7.5 |
| 200194 | Digital Transformation | 3.5 |