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.
A visually pleasing logical decomposition of different risk types using the analogy of the periodic table. The app offers an interactive exploration of the risk profile of different business models
| Introductory Level | Core Level | Advanced Level | |
| Non-Technical | PTR29042 | ||
| Technical |
Enroll and Get Started with PTR29042
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 4 Session DeepDive into regulatory aspects around concentration risks in credit portfolios, focusing on compliance requirements of the Large Exposure Framework and the Pillar II.
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:
This course is a CrashProgram (short course) introducing the concept of a structured review of risk data.
Step by step we build the knowledge required to review the suitability of data for a given purpose and how to report the findings.
Object-oriented programming and techniques (OOP) such as using classes and inheritance are common in many application programming environments but don't travel well outside computer memory. When considering data science tasks and objectives the transition from object hierarchies to data structures (and vice versa) is not always straightforward. In this short course we explore how some programming languages, data formats, database API's and web frameworks handle hierarchical classes.
In this short course we explore how some programming languages, data formats, database API’s and web frameworks handle hierarchical classes.
Object-oriented programming and techniques (OOP) such as using classes and inheritance are common in many application programming environments but alas don’t “travel well” outside computer memory. The potentially intricate relationships of objects (both the data they hold and the meaning and possible uses of the data) are not easy to transfer (except of-course by full replication of code and data). Hence when considering data science tasks and objectives that involving exchange of data, the transition from object hierarchies that live inside memory, to data structures that can be exchanged with another computer is not straightforward.
The Open Risk Academy course NPL270672 is a CrashCourse introducing the EBA NPL Templates.
We start with the motivation for the templates and the domain of credit data (to which NPL data belongs). We discuss three core classes that capture the essence of lending operations from a lenders point of view (Counterparty, Loan, Collateral). Next we explore classes that capture events in the lending relationship lifecycle (which we term NPL Scenarios). We look into the main data types: elementary data types, choice lists, arrays and unstructured text. We close with discussing some more complex issues involving graph and timeseries data.