Analytics in Finance
Analytics in Finance
The tools used by an engineer in Business Analytics, are often grouped in:
- Descriptive Analytics, which can answer the question “What has happened?”
- Predictive Analytics, that focus more on finding out “What will happen?”
- Prescriptive Analytics, where actions are taken and thus answers the question “What should we do?”
The MSc in Business Analytics builds upon this concept and offers three optional study lines, each allowing you to become a specialist in certain areas:
- Predictive Analytics
- Prescriptive Analytics
- Analytics in Finance
Analytics in Finance is all about data analysis and data-based decision making. From the performance of financial markets to political decisions impacting those markets to the financial dispositions of an individual household on their savings, loans and insurance needs, it all is reflected in large amounts of data.
Within this study line, the students can follow courses which teach them about the workings of financial markets, financial products and risk management and optimisation in financial decision making. Combining courses in finance, machine learning and optimisation, enables the students to analyse financial data and set up mathematical models for financial problem solving, be it asset allocation, risk management or household financial advice.
There are many companies, both within the Fintech ecosystem and the established financial sector who are interested in working with students and later hiring them to work on these topics.
Aside from the mandatory courses from the general competencies, the study line Analytics in Finance requires you select technical specialization courses in the following matter.
Select the following courses:
| 02417 | Time Series Analysis | 5 | point | Spring F4B (Fri 8-12) |
| 42104 | Introduction to Financial Engineering | 5 | point | Autumn E5A (Wed 8-12) |
| 42108 | Advanced Financial Engineering | 5 | point | Spring F5B (Wed 13-17) |
and at least two of the following courses:
| 02425 | Diffusions and stochastic differential equations | 5 | point | Autumn E1B (Thurs 13-17) |
| 02427 | Advanced Time Series Analysis | 10 | point | Autumn E5 (Wed 8-17) |
| 02435 | Decision-Making Under Uncertainty | 5 | point | Spring F4A (Tues 13-17) |
| 42015 | Energy Economics | 5 | point | Autumn E2A (Mon 13-17) |
| 46755 | Renewables in electricity markets | 5 | point | Spring F2A (Mon 13-17) |
Students following this study line can benefit from taking elective courses at Copenhagen Business School (CBS) or the University of Copenhagen (KU). Course registration is subject to the rules and regulations for visiting students at CBS and KU, hence, you are not guaranteed access.
Beware that the deadline for course registrations is not the same as at DTU. It is also a good idea to apply early for course pre-approval.
CBS course suggestions:
KAN-CMECV1251U Financial Engineering (Advanced)
KAN-CMECV1250U Mathematical Finance 2: Continuous Time Finance (Advanced)
KAN-CCMVV2413U Python for the Financial Economist (Advanced)
KU course suggestions:
NMAA05113U Continuous Time Finance (FinKont) (Advanced)
NMAK15010U Continuous Time Finance 2: (FinKont2) (Advanced)
NMAA09045U Finance 2: Dynamic Portfolio Choice (Fin2) (Advanced)
NMAK16004U Computational Finance (Advanced)
Example study plans
Analytics in Finance_skema
Electives
Thesis
Winter start is a bit more complex, as most introductory courses will only be available on the second semester. The following study plan is an example of how a to follow the Analytics in Finance study line with winter start.
Thesis