PostGraduate in Enterprise Data Science & Analytics
Education objectives
The Postgraduate Program in Enterprise Data Science & Analytics is aimed at professionals interested in deepening their knowledge in data science, particularly in enterprise environments.
This program will present the methodologies and tools that will transform data into information, on which enterprises can base strategic information on entering new markets, launching new product or service lines, optimizing processes, transforming business models and, generally, competing in a market increasingly driven by data-driven decisions.
It is aimed at professionals interested in deepening their knowledge in data science and advanced analytics, as well as in the creation of predictive models and their practical application in enterprise environments.
It tackles the shortage of highly qualified professionals in this field, with one of the highest growth in the current market of information systems.
Applications - 6th 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 September 24th until October 31st, 2024. The selection process is based on the analysis of the applicant's academic and professional curriculum.
General characterization
DGES code
E242
Cicle
Postgraduate programmes
Degree
None
Access to other programs
This Program gives access to a Master Degree, confirm ours here.
Coordinator
Roberto Henriques, Henrique José de Jesus Carreiro
Opening date
February 2025
Vacancies
Fees
€5.100
Schedule
After Working Hours
Teaching language
English
Degree pre-requisites
To earn the postgraduate program diploma in Enterprise Data Science & Analytics, students complete 8 course units (a total of 60 ECTS).
Conditions of admittance
The requirements for the applications are: a degree in a compatible field (complete until January 2025); 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 |
| 400087 | Analyzing and Visualizing Data | 4.0 |
| 400083 | Analyzing Big Data | 7.5 |
| 200209 | Big Data Foundations | 7.5 |
| 200206 | Deep Learning Neural Networks | 3.5 |
| 400086 | Enterprise Data Science Bootcamp | 7.5 |
| 1º year - Spring semester | ||
|---|---|---|
| Code | Name | ECTS |
| 400088 | Data Science and Machine Learning | 7.5 |
| 400084 | Managing Relational and Nom-Relational Data | 7.5 |
| 400090 | Programming for Data Science | 7.5 |
| 400089 | Statistics for Enterprise Data Analysis | 7.5 |