Geographical features on a map

This is the collection of Open Risk Academy Data Science courses.


This category includes courses that cover general data science techniques applicable across a range of risk management disciplines.

Techniques that might be more specialized to specific areas of risk management and (financial) risk modelling are covered in other course categories.

Class Inheritance in Data Science

class inheritance tools

This course is a CrashProgram (short course) that explores how class inheritance of related data objects can be handled in a data science context. The course is at a core technical level. It requires some familiarity with database models, data specifications such as JSON and a basic knowledge of Python.

Course Level and Type:

Introductory Level Core Level Advanced Level
Non-Technical
Technical CrashProgram
DAT31063

An overview of graph methods in data science

Python is the swiss knife of modern programming languages and a prime candidate to be also the swiss knife for risk modelling

Summary:

An overview of graph methods in data sciences

Course Level and Type:

Introductory Level Core Level Advanced Level
Non-Technical
Technical

Introduction to Risk Data Review

Different data validation levels as recommended by Eurostat

Summary:

This course is a CrashProgram (short course) introducing the concept of a structured review of risk data. The course is at an introductory technical level. It requires some familiarity with credit risk data (and an ability to open and inspect data files) Step by step we build the knowledge required to review the suitability of data for a given purpose and how to report the findings

Outcomes:

  • 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:

Introductory Level Core Level Advanced Level
Non-Technical
Technical CrashProgram
DAT31046

Visualization of Timeseries Data

Summary

Visualization of Timeseries Data

Course Level and Type:

Introductory Level Core Level Advanced Level
Non-Technical
Technical DAT31056

Exploratory Risk Data Analysis using Pandas, Seaborn and Statsmodels

Exploratory Data Analysis Visualizations

Summary:

This course is a CrashProgram (short course) introducing exploratory data analysis. The course is at an introductory technical level. It requires some familiarity with credit risk data (and an ability to open and inspect data files). Step by step we build the knowledge required to perform a comprehensive 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:

  • 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:

Introductory Level Core Level Advanced Level
Non-Technical
Technical CrashProgram
DAT31048