This course aims to provide a comprehensive framework to understand modern credit applications. To achieve this goal, students must get familiarized with state-of-the-art technologies in the field of data engineering, statistical modeling, finance, and machine learning.
We embrace the open-source philosophy, and so this course contents and the software/datasets used are open to the public under a permissive license.
Prospectus students must:
- Have their own hardware.
- At least 4GB of RAM. Recommended: 8GB or more.
- Access to the internet.
- Have basic knowledge about the following topics.
- Finance (e.g. interest rates, cash-flow analysis, profiling)
- Mathematics (e.g. linear-algebra, calculus, probability & statistics)
- Computer Science (e.g. programming languages, data structures, algorithms).
Successful completion of this course should enable students to:
- Understand the financial theory behind credit risk.
- Leverage different data-systems involved in the credit analysis process.
- Create credit models using machine learning techniques.
- Get exposure to the most relevant big-data technologies on the financial industry.
The grading system is fairly simple:
| Activity | # | % |
|---|---|---|
| Homework | n | 40 |
| Exams | 3 | 30 |
| Projects | 2 | 20 |
| Class Work | m | 10 |
Where n and m are undefined integers greater than 5.