PostGraduate in Data Science for Finance
Education objectives
The Postgraduate Program in Data Science for Finance is an highly innovative quantitative orientated one year program geared towards technically-minded graduates wanting a deeper, more analytical study of finance, risk management and financial engineering than is found in general finance programs. The program is designed to prepare students for successful careers in asset management, financial engineering and risk management in the financial sector (financial markets, financial institutions, regulators and supervisors) using technologies and methodologies of last generation.
Graduates are typically employed in investment banking, asset management, hedge funds and investment advisory, risk management, sales and trading, hedge funds, financial engineering, financial technology and consulting/advisory.
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
4974
Cicle
Postgraduate programmes
Degree
None
Access to other programs
This Postgraduate Program gives access to the Master degree program in Statistics and Information Management, with a specialization in Risk Analysis and Management, This Master program is ranked as the best Master degree program in Insurance, Risk and Actuarial Sciences in Portugal and 4th best in the World by Eduniversal, an international agency that publishes an annual ranking of the best MBA and Master degrees in the world.
Coordinator
Jorge Miguel Ventura Bravo
Opening date
September 2023
Vacancies
Fees
€5.100
Schedule
After Working Hours
Teaching language
English
Degree pre-requisites
To earn the postgraduate program diploma in Data Science for Finance, students complete a total of 60 ECTS, which correspond to 13 course units.
Conditions of admittance
The requirements for the applications are: a degree in a compatible field (complete until September 2023); 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 |
| 400101 | Asset Pricing & Portfolio Theory | 7.5 |
| 400098 | Computational Finance | 7.5 |
| 400097 | Fixed Income Securities | 7.5 |
| 400103 | Machine Learning in Finance | 7.5 |
| 400105 | Text Mining | 4.0 |
| 1º year - Spring semester | ||
|---|---|---|
| Code | Name | ECTS |
| 400176 | Algorithmic Trading | 4.0 |
| 400174 | Credit Risk Scoring | 4.0 |
| 400173 | Decentralized Finance | 7.5 |
| 400107 | Deep Learning Methods in Finance | 3.5 |
| 400175 | Financial Derivatives & Risk Management | 7.5 |
| 400177 | Insurance Data Science | 3.5 |