Foundations in R for Actuaries Presentation
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)
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
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)
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
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
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)
6.1 - Final Assignment Notebook
6.2 - Final Assignment Submission
1.1 - Introduction
1.2 - Business Context
1.3 - Overview of the Lapse Study
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
3.1 - Introduction
3.2 - Preliminary Analysis
3.3 - Feature Engineering
3.4 - Conclusion
4.1 - Introduction
4.2 - Standard Multiple Linear Regression Model
4.3 - CART Models
4.4 - Advanced Regression Tree Models
4.5 - Conclusion
5.1 - Introduction
5.2 - Visualisation
5.3 - Merits of the Models
5.4 - Recommendations
5.5 - Conclusion
1.1 - Introduction to Case Study
1.2 - Business Context
1.3 - Overview of Mortality Modelling Approaches
2.1 - Introduction
2.2 - Outline of the Problem
2.3 - Overview of the Data
2.4 - Relevant R Packages
2.5 - Conclusion
3.1 - Introduction
3.2 - Preliminary Analysis of the Data
3.3 - Feature Engineering
3.4 - Conclusion
4.1 - Introduction
4.2 - Parametric Regression Models
4.3 - Advanced Regression Models
4.4 - Conclusion
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
6.1 - Introduction
6.2 - Visualisation of Results
6.3 - Merits of the Models
6.4 - Recommendations
6.5 - Conclusion
RStudio Helpful Guide
Data Importing Helpful Guide
dplyr Helpful Guide
ggplot Helpful Guide
1.1 - Overview of the Course
1.2 - Principles of Visualisation
1.3 - Visualisation Best Practices
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
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
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
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
6.1 - Introduction
6.2 - Creating an R Shiny Dashboard
6.3 - Deploying a Dashboard
6.4 - Conclusion