Major in Business Analytics
Education objectives
The popularity of Data Science and Analytics has been steadily growing in the last few years, both in Industry and Academia. The Master degree Program in Data Science and Advanced Analytics, with a Major in Business Analytics, is aimed at market oriented people, who want to apply effective analytical models to different business problems, interpreting the results and their implications to the business, with the objective of taking data driven decisions to optimize the business process.
The best students of the 1st year of the Master in Data Science and Advanced Analytics will be invited for a 6 month paid internship, to be held during the 2nd year, in one of the program's partner institutions (Accenture, Grupo Ageas Portugal, SAS, Millennium BCP, Rebis Consulting, NOVA IMS and Tranquilidade).
This Program will provide a set of interdisciplinary skills and tools such as:
- Understanding of the main paradigms associated with large databases and data warehouses;
- Understanding the processes of decision making;
- Mastering data mining tools, in particular for "Big Data" related problems;
- Mastering the processes of creation and maintenance of descriptive and predictive models;
- Recognizing and applying the most effective analytical models to different business cases;
- Interpreting models and their implications to the business.
Applications - academic year 2019/2020
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, until May 9th. The selection process is based on the analysis of the applicant's academic and professional curriculum.
General characterization
DGES code
75121
Cicle
Specialization Area
Degree
Master
Access to other programs
Coordinator
Leonardo Vanneschi
Opening date
Vacancies
Fees
Schedule
Teaching language
Available soon
Degree pre-requisites
This path is an option of
Master in Data Science and Advanced Analytics
Conditions of admittance
The requirements for the applications are: a degree in a compatible field (complete until September 2019); analysis of the applicants' academic and professional curriculum.
Evaluation rules
Structure
1º year - Autumn semester | ||
---|---|---|
Code | Name | ECTS |
200011 | Business Intelligence | 6.0 |
200142 | Computational Intelligence for Optimization | 7.5 |
200175 | Data Mining | 7.5 |
200178 | Statistics for Data Science | 7.5 |
400090 | Programming for Data Science | 3.5 |
Options | ||
200011 | Business Intelligence | 6.0 |
200142 | Computational Intelligence for Optimization | 7.5 |
1º year - Spring semester | ||
---|---|---|
Code | Name | ECTS |
200179 | Machine Learning | 7.5 |
200180 | Deep Learning | 3.5 |
200174 | Storing and Retrieving Data | 4.0 |
200167 | Big Data Analytics | 7.5 |
200014 | Business Process Management | 3.5 |
200208 | Business Cases wit Data Science | 7.5 |
200207 | Big Data Modelling and Management | 3.5 |
200181 | Text Mining | 4.0 |
200194 | Digital Transformation | 3.5 |
200176 | Data Visualization | 4.0 |
Options | ||
200180 | Deep Learning | 3.5 |
200167 | Big Data Analytics | 7.5 |
200014 | Business Process Management | 3.5 |
200208 | Business Cases wit Data Science | 7.5 |
200181 | Text Mining | 4.0 |
200194 | Digital Transformation | 3.5 |
2º year - Spring semester | ||
---|---|---|
Code | Name | ECTS |
200045 | Internship | 35.0 |