Course curriculum

  • 1

    Welcome to Foundations in R for Actuaries Plus Experience Case Studies

    • Foundations in R for Actuaries Presentation

  • 2

    Getting Started

    • How to use Actuartech Academy

    • 0.1 - Objectives of the Course

    • 0.2 - Introducing R

    • 0.3 - Introducing RStudio

    • 0.4 - Introducing Jupyter Notebooks

    • 0.5 - Quizzes and Assessments: how it works

    • RStudio Sandbox (Optional IDE to run code)

    • How to install R, Jupyter and RStudio (References)

  • 3

    Foundations: 1 - Problem Specification

    • 1.1 - Overview

    • 1.2 - Basic Concepts

    • 1.3 - Example: Using R as a Calculator

    • 1.4 - Example: Linear Regression and Plotting

    • 1.5 - Summary

    • RStudio Sandbox (Optional IDE to run code)

    • Coding Challenge: Week 1

  • 4

    Foundations: 2 - Data Collection

    • 2.1 - Overview

    • 2.2 - Introducing Vectors

    • 2.3 - Subsetting Vectors and Extracting Elements

    • 2.4 - Matrix

    • 2.5 - Data Frames

    • 2.6 - Useful Functions for Data Frames

    • 2.7 - Reading External Files

    • 2.8 - R's Built-in Datasets

    • 2.9 - Example: Natural Disaster Data

    • 2.10 - Summary

    • 2.11 - Data Collection Quiz

    • RStudio Sandbox (Optional IDE to run code)

  • 5

    Foundations: 3 - Data Management

    • 3.1 - Overview

    • 3.2 - Conditionals

    • 3.3 - for Loops

    • 3.4 - while Loops

    • 3.5 - Functions

    • 3.6 - Example: Cash Flow

    • 3.7 - Summary

    • 3.8 - Data Management Quiz

    • RStudio Sandbox (Optional IDE to run code)

    • Coding Challenge: Week 2

  • 6

    Foundations: 4 - Model Building

    • 4.1 - Overview

    • 4.2 - Object Functions

    • 4.3 - Mathematical Functions

    • 4.4 - Statistical Functions

    • 4.5 - apply Functions

    • 4.6 - Statistical Models: Introduction to Linear Regression

    • 4.7 - Summary

    • 4.8- Model Building Quiz

    • RStudio Sandbox (Optional IDE to run code)

    • Live Lesson Week 3: GLM Model Building Sandbox [Includes Terminal]

    • Coding Challenge: Week 3

  • 7

    Foundations: 5 - Data Visualisation

    • 5.1 - Overview

    • 5.2 - Creating Scatter Plots

    • 5.3 - Creating Line Plots

    • 5.4 - Creating Bar Charts

    • 5.5 - Creating Histogram and Density Plots

    • 5.6 - Creating Box Plots

    • 5.7 - Summary

    • 5.8 - Visualisation Quiz

    • RStudio Sandbox (Optional IDE to run code)

  • 8

    Foundations: 6 - Final Assignment

    • 6.1 - Final Assignment Notebook

    • 6.2 - Final Assignment Submission

  • 9

    Lapse Rate Analysis: 1 - Introduction

    • 1.1 - Introduction

    • 1.2 - Business Context

    • 1.3 - Overview of the Lapse Study

  • 10

    Lapse Analysis: 2 - Problem Specification

    • 2.1 - Introduction

    • 2.2 - Overview of Business Tasks and Models

    • 2.3 - Overview of the Data

    • 2.4 - Relevant R Packages

    • 2.5 - Conclusion

  • 11

    Lapse Analysis: 3 - Data Collection and Management

    • 3.1 - Introduction

    • 3.2 - Preliminary Analysis

    • 3.3 - Feature Engineering

    • 3.4 - Conclusion

  • 12

    Lapse Analysis: 4 - Model Building

    • 4.1 - Introduction

    • 4.2 - Standard Multiple Linear Regression Model

    • 4.3 - CART Models

    • 4.4 - Advanced Regression Tree Models

    • 4.5 - Conclusion

  • 13

    Lapse Analysis: 5 - Reporting

    • 5.1 - Introduction

    • 5.2 - Visualisation

    • 5.3 - Merits of the Models

    • 5.4 - Recommendations

    • 5.5 - Conclusion

  • 14

    Mortality Modelling: 1 - Introduction

    • 1.1 - Introduction to Case Study

    • 1.2 - Business Context

    • 1.3 - Overview of Mortality Modelling Approaches

  • 15

    Mortality Modelling: 2 - Problem Specification

    • 2.1 - Introduction

    • 2.2 - Outline of the Problem

    • 2.3 - Overview of the Data

    • 2.4 - Relevant R Packages

    • 2.5 - Conclusion

  • 16

    Mortality Modelling: 3 - Data Collection and Management

    • 3.1 - Introduction

    • 3.2 - Preliminary Analysis of the Data

    • 3.3 - Feature Engineering

    • 3.4 - Conclusion

  • 17

    Mortality Modelling: 4 - Model Building I: Graduation Based on Age

    • 4.1 - Introduction

    • 4.2 - Parametric Regression Models

    • 4.3 - Advanced Regression Models

    • 4.4 - Conclusion

  • 18

    Mortality Modelling: 5 - Model Building II: Graduation based on Age, Gender, Salary, Industry

    • 5.1 - Introduction

    • 5.2 - Testing/Training Split

    • 5.3 - Poisson Regression

    • 5.4 - Generalised Additive Model

    • 5.5 - Gradient Boosting Machine

    • 5.6 - Conclusion

  • 19

    Mortality Modelling: 6 - Reporting

    • 6.1 - Introduction

    • 6.2 - Visualisation of Results

    • 6.3 - Merits of the Models

    • 6.4 - Recommendations

    • 6.5 - Conclusion

  • 20

    Appendix and Further Resources

    • RStudio Helpful Guide

    • Data Importing Helpful Guide

    • dplyr Helpful Guide

    • ggplot Helpful Guide

  • 21

    Visualisation Deep Dive: 1 - Introduction

    • 1.1 - Overview of the Course

    • 1.2 - Principles of Visualisation

    • 1.3 - Visualisation Best Practices

  • 22

    Visualisation Deep Dive: 2 - Problem Specification

    • 2.1 - Introduction

    • 2.2 - Overview of the Problem: Natural Catastrophe Data

    • 2.3 - Overview of the Problem: COVID-19 Data

    • 2.4 - ggplot2 Outline

    • 2.5 - Conclusion

  • 23

    Visualisation Deep Dive: 3 - Data Collection and Data Management

    • 3.1 - Introduction

    • 3.2 - Preliminary Analysis: Natural Catastrophe Data

    • 3.3 - Preliminary Analysis: COVID-19 Data

    • 3.4 - Re-shaping our Data

    • 3.5 - Conclusion

  • 24

    Visualisation Deep Dive: 4 - Visualisation for Reporting I: Natural Catastrophe Data

    • 4.1 - Introduction

    • 4.2 - Natural Disasters by Country/Region: Asia vs Americas

    • 4.3 - Natural Disasters by Type

    • 4.4 - Reporting on Deaths and Economic Loss

    • 4.5 - Conclusion

  • 25

    Visualisation Deep Dive: 5 - Visualisation for Reporting II: COVID-19 Data

    • 5.1 - Introduction

    • 5.2 - COVID-19 Cases by Country: United Kingdom vs South Africa

    • 5.3 - COVID-19 Deaths by Country: United Kingdom vs South Africa

    • 5.4 - Understanding how Government Intervention effected Case Numbers and Deaths: Lockdowns and Travel Bans

    • 5.5 - Conclusion

  • 26

    Visualisation Deep Dive: 6 - Dashboarding for Reporting: R Shiny

    • 6.1 - Introduction

    • 6.2 - Creating an R Shiny Dashboard

    • 6.3 - Deploying a Dashboard

    • 6.4 - Conclusion