This crash course illustrates how to process loan-level US Agency mortgage data using awk, pandas and django. The first part of the course focuses on static (acquisition) type attributes. This part covers the following topics:
This crash course illustrates how to process loan-level US Agency mortgage data using awk, pandas and django. The second part of the course focuses on the performing book. This part covers the following topics:
This course is a CrashProgram in the use of python for risk modelling purposes, in particular credit concentration measurement.
There is little or no prior knowledge of concentration risk measurement but general background in credit risk management is useful for context.
This course is a CrashProgram (short course) introducing exploratory data analysis.
The course can be pursued on a standalone basis. It is advisable to pursue the course after DAT31046 (Risk Data Review) which discusses a review of the data from a data quality validation perspective.
This course is a CrashProgram (short course) in the use of Python to work with Semantic Data (RDF / OWL)
The course is at a medium technical level. It requires some familiarity with python (and a working installation). On the semantic data side it requires knowledge of basic concepts around files and representation formats for data.
This course is a CrashProgram in the use of python for credit portfolio modelling purposes, in particular working with data templates and spreadsheets.
The course covers the following topics:
This course is a CrashProgram (short course) in the use of Python and the package TransitionMatrix for analysing credit migration data.
The course is at a medium technical level. It requires some familiarity with python (and a working installation that includes the common numpy/scipy libraries). On the risk modelling side it requires knowledge of basic credit rating migration concepts.
This course is a DeepDive with nine segments, exploring Input-Output models using Python and the pymrio library. The course is at a core technical level. It requires working familiarity with Python, basic linear algebra and elements of economic systems. Step by step we explore how one can define and perform useful operations in Environmentally Extended Input-Output Analysis.