Open Risk Academy

This course is a CrashProgram (short course) introducing exploratory data analysis using credit risk data as the use case

• 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
• Learn basic exploratory data analysis using pandas
• Create standard graphs using seaborn
• Calculate Contingency tables, WoE and Information Value using pandas, scipy and statsmodels

The python documentation describes data types and their properties primarily in those two pages:

• Python Data Types
• Python Built-In Data Types

A new crash course offering at the Open Risk Academy

The course offers a gentle introduction to the concepts of

• Data Provenance
  
• Data Validation levels as categorized by Eurostat
  
• Creating a report in a modern format like R Markdown

The Open Risk Manual has an entry offering an in-depth comparison of the to Python and R ecosystems. Learning Python and/or R along side the Academy courses is a great way to deepen your Quantitative Risk Management knowledge

A very big thank you to all of you for joining the Open Risk Academy! We are striving to make this platform a unique resource in the financial risk management universe. There is still much ground to cover... Any and all feedback will help us get there sooner!

The Open Risk Manual is a great complement to Academy Courses! The Manual is structured a public resource and it includes definitions, usage, examples, issues and challenges for a wide variety of risk management topics. Check it out and let us know of any ideas, suggestions for further entries.