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.
Summary: In this short course we explore how some programming languages, data formats, database API’s and web frameworks handle hierarchical classes.
Content: 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).
Summary: The Open Risk Academy course NPL270672 is a CrashCourse introducing the EBA NPL Templates.
Content: 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).
Summary: This crash course is an introduction to the main elements of an Academy course (it is a course about courses!). The course takes less than an hour to complete. It is recommended to new users that are not familiar with eLearning environments.
How Does The Course Help: You can also use the course to test whether your can get full access to the academy courses using your particular device / browser.
Summary: This course is a CrashProgram (short course) in the use of Python to work with Semantic Data (RDF / OWL)
Requirements: 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.
Outcomes: Step by step we build a high-level knowledge base around using python to work with a variety of semantic data:
Summary: 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.
Content: The course covers the following topics:
The regulatory framework around credit concentration risk as captured in the BIS documentation
The Large Exposures Framework
Regulatory requirements around Pillar II name, sector and geographic concentration risk
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
CRP13026
Technical
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Summary: A Crash Course on Modelling Credit Contagion
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
Technical
MCC03008
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Discussion @ the Commons
Summary: This course is a CrashProgram (short course) in the use of Python and the package TransitionMatrix for analysing credit migration data.
Requirements: 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.
Outcomes: Step by step we build the knowledge required to use python to analyse credit migration data:
Summary: Open Risk Crossword Puzzles: A collection of crossword puzzles build around specific themes
Content: Non-Performing Loan Crossword Puzzle Game Sustainable Finance Crossword Puzzle Game Cyber Risk Crossword Puzzle Game Fintech Crossword Puzzle Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
QUI29052
Technical
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Discussion @ the Commons
Summary: A quiz about financial regulation in the European Union. Test your knowledge about the diverse initiatives that are reshaping the financial landscape in Europe.
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
QUI29049
Technical
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Discussion @ the Commons
Summary: This course is a CrashProgram (short course) introducing exploratory data analysis.
Prerequisites: 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.
Outcomes: Step by step we build the knowledge required to perform a comprehensive exploratory data analysis
We learn the concept and techniques of Exploratory Data Analysis
Touch upon the issue of bias and how to mitigate it
Learn about more advanced formats such as HDF
Basic exploratory analysis using pandas
Easy visual analysis of association using seaborn
Contingency tables, WoE and Information Value using pandas, scipy and statsmodels
We summarize our findings in terms of numerical and graphical results in a mock report written in Markdown format
Course Level and Type: The course is at an introductory technical level.
Summary: If this is your first time in the Academy you can familiarize yourself by going through the Demo which includes manuals, study plan suggestions and more.
Requirements The course is completely introductory and does not have any prerequisite except a clear and open mind!
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
GEN13041
Technical
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Summary: This course is a CrashProgram in the use of python for risk modelling purposes, in particular credit concentration measurement.
Who Is This Course For: There is little or no prior knowledge of concentration risk measurement but general background in credit risk management is useful for context.
Content: The course covers the following topics:
Step by step we build the knowledge required to use python to meet a typical modern risk management challenge Examples are drawn from the two primary areas of credit concentration: name and sector concentration Course Level and Type: The course requires little or no prior knowledge of python (but indeed prior programming knowledge in some language is required).
Summary: This course is a CrashProgram introduction to the topic of credit risk concentrations: how to identify, measure and manage excess credit exposure, an essential risk management discipline for any credit portfolio.
Content: The course covers the following topics:
The concept of credit concentration, its causes and motivation for its management
Overview of regulatory guidance, the large exposure framework and Pillar II
Introduction to measurement techniques using simple or advanced concentration metrics
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
ICC13024
Technical
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Summary: This course is a CrashProgram (short course) introducing the GeoJSON specification for the encoding of geospatial features.
Course Level and Type: The course is at an introductory technical level. It requires some familiarity with data specifications such as JSON and a very basic knowledge of Python
Introductory Level
Core Level
Advanced Level
Non-Technical
Technical
DAT31053
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Summary: A brief introduction to the Open Risk Manual
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
GEN13054
Technical
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Discussion @ the Commons
Summary: This course is a CrashProgram in the use of python for credit portfolio modelling purposes, in particular working with data templates and spreadsheets.
Content: The course covers the following topics:
Overview of the loan level templates (ECB SME version)
Manipulating spreadsheets with Python
The python dictionary data type
Organization of the loan data fields
Generating test portfolios
Course Level and Type: The course requires some prior knowledge of python (and indeed prior programming knowledge in some language is required) and, of course, also spreadsheets.
Summary: This course is a CrashProgram in the use of the MongoDB database in conjunction with Python for credit portfolio management purposes.
Content: The course covers the following topics:
Introduction to MongoDB its data model and operations on data
Using python to access the database
Developing LoanDB, a document design conforming to SME Loan Level Data standard
Using map/reduce to extract data and python to apply a simple credit scoring model
Course Level and Type: The course requires little or no prior knowledge of mongoDB, and basic knowledge of python (but prior programming knowledge in some language is required).
Summary: This course is a DeepDive into measuring name concentrations in credit portfolios.
Content: It covers the following topics:
The measurement of credit name concentrations using basic indicators and indexes such as the concentration ratio and the HHI index
The measurement of credit name concentrations using more advanced (risk based) indicators such as volatility and economic capital based indexes
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
CNC13010
Technical
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Summary: This course is a 4 Session DeepDive into measuring sector concentrations in credit portfolios.
Content: The course covers the following topics:
Measurement of credit sector concentrations using basic indicators and indexes such as the concentration ratio and the HHI index
Measurement of credit sector concentrations using risk based indicators such as volatility based indices and economic capital models
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
CSC13022
Technical
Summary: In the Shortest Possible Course on Risk Management we try to condense in 15 intense minutes the essence of risk management!
Requirements The course is completely introductory and does not have any prerequisite except a clear and open mind!
Course Level and Type:
Introductory Level
Core Level
Advanced Level
Non-Technical
MCR29037
Technical
Enroll and Get Started with MCR29037
