This course is an Open Risk Academy CrashCourse introduction to using Linux command line tools to work (in particular) with large text files encoding numerical data in matrix format. A central role is played by awk, the venerable UNIX pattern-matching language and tool.
This course is an introduction the Copernicus Satellite Data Ecosystem. It covers the following topics:
Data Scientists / Data Engineers in any Domain that need to use satellite data
A brief review of the linear algebra and matrix theory concepts underpinning standard environmentally extended input-output models.
This brief introduction in Input-Output Model mathematics discusses the basic elements of linear algebra that are necessary to understand the standard Input-Output models, including their Environmental impact extensions.
Matrix theory is the main subject covered here as it is the most dominant mathematical tool used in this area (though other parts of mathematics do find applications in Input-Output analysis). The focus is on the notation and mathematical objects commonly used, not the economic interpretation or any mathematical proofs.
This course is an introduction to Tensor calculations with Eigen, a popular C++ library for working with numerical arrays and linear algebra. It covers the following topics:
Developers in any Domain that need to use higher-dimensional numerical data containers
What do we mean by credit data? This post is a discussion around mathematical terminology and concepts that are useful in the context of working with credit data, taking us from network graph representations of credit systems to commonly used reference data sets
Digging into the meaning of credit data collections, the logic that binds them together towards understanding what they can be used for and what limitations and issues they may be affected by, this new course in the Credit Portfolio Management category explores a new angle to look at an old practice.
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:
We discuss the relation of economic input-output models with graph theory and networks
The objective of the course is to discuss the relation of economic input-output models with graph theory and networks.
This course is a CrashProgram (short course) introducing the different ways in which the graph concept enters various data science applications. The course is at an introductory technical level. It requires comfort with mathematical notation and (optionally) ability to run the provided python code with various graph examples. Reviewing the very diverse uses of graph structures will reveal the richness of graphs as data science tools.
With the ever more widespread adoption of Data Science tools (defined loosely as the intensive use of data in decision-making), there is strong interest in Visualization as an effective means for humans to understand information at various stages of the data lifecycle.
There is a large variety of open source data visualization tools which can produce an ever more bewildering variety of visualization types:
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 collects resources and activities to facilitate those involved in open source risk modelling projects.
| Introductory Level | Core Level | Advanced Level | |
| Non-Technical | |||
| Technical | GEN13031 |
Enroll and Get Started with GEN13031
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 (short course) introducing the GeoJSON specification for the encoding of geospatial features.
The course is live at the Open Risk Academy, this repository hosts the python scripts used in the course. The scripts can be used standalone but documentation is minimal.
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.
A Crash Course on Modelling Credit Contagion
| Introductory Level | Core Level | Advanced Level | |
| Non-Technical | |||
| Technical | MCC03008 |
Enroll and Get Started with MCC03008
This course is a CrashProgram in the use of the MongoDB database in conjunction with Python for credit portfolio management purposes.
The course covers the following topics: