Overview of Available Open Risk Academy Courses
In this page you can find the Open Risk Academy Courses that are currently available with Self-Enrollment.
Can't find a suitable course? Use the course request form and subject to resources and subject-matter fit we will try to accommodate!
- Introduction to the Open Risk Manual
- Getting Started with the Open Risk Academy
- Academy CrashProgram Demo
- Getting Started with Open Source
- The Puzzle of Money
- Input-Output Models with Python
- The Climate Dictionary Quiz
- Crash Course on Input-Output Model Mathematics
- Analysis of Credit Migration using the Python transitionMatrix
- Analysis of Credit Migration using the Python transitionMatrix
- Loan Level Templates Using Python
- Managing Loan Portfolios Using MongoDB
- Concentration Measurement using Python
- Processing Agency Mortgage Data with Awk, Pandas and Django. Part 1, Static Data
- Processing Agency Mortgage Data with Awk, Pandas and Django. Part 2, Dynamic Data
- Class Inheritance in Data Science
- Overview of Python Semantic Web Tools
- An introduction to GeoJSON
- Exploratory Risk Data Analysis using Pandas, Seaborn and Statsmodels
- Introduction to Risk Data Review
- Open Risk Crossword Puzzles
- The Periodic Table of Risk Elements
- The Shortest Possible Course on Risk Management
- Risk Management Questions and Answers
- Introduction to Credit Concentration
- Basel on Credit Concentration Risk
- Credit Concentration in the UK Pillar II
- Measuring Name Concentrations
- Measuring Sector Concentrations
- CrashProgram: Credit Contagion
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 Enroll and Get Started with DAT31053 Discussion @ the Commons
Summary 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 Course Level and Type Introductory Level Core Level Advanced Level Non-Technical PTR29042 Technical Enroll and Get Started with PTR29042 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 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 Enroll and Get Started with CRP13026
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 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. Content This course is the first installment of a series dedicated to EEIO models.
Summary A Crash Course on Modelling Credit Contagion Course Level and Type Introductory Level Core Level Advanced Level Non-Technical Technical MCC03008 Enroll and Get Started with MCC03008 Discussion @ the Commons
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 CrashProgram (short course) introducing the concept of a structured review of risk data. Outcomes Step by step we build the knowledge required to review the suitability of data for a given purpose and how to report the findings. We learn the concept of Data Provenance We get a first exposure to the different levels of Data Validation as recommended by EuroStat We summarize our findings in a mock report written in Markdown format Course Level and Type The course is at an introductory technical level.
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).