Course curriculum

  • 1

    1 - Introduction

  • 2

    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

  • 3

    3 - Data Collection and Management

    • 3.1 - Introduction

    • 3.2 - Preliminary Analysis

    • 3.3 - Feature Engineering

    • 3.4 - Conclusion

    • Week 1 Live Lesson Recording

  • 4

    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

    • Additional Material: Using Classification Models to Understand Lapses

  • 5

    5 - Reporting

    • 5.1 - Introduction

    • 5.2 - Visualisation

    • 5.3 - Merits of the Models

    • 5.4 - Recommendations

    • 5.5 - Conclusion

    • Week 2 Live Lesson Recording

  • 6

    6 - Project

    • Project Instructions

    • Project Template

    • SOA Lapse Experience Dataset

    • Titanic Dataset

    • OWID COVID-19 Dataset

  • 7

    Appendix and Further Resources

    • Blank R Notebook (Optional)

    • Introducing R and RStudio

    • A User Guide for Jupyter Notebooks

    • RStudio Helpful Guide [Ref 1]

    • Data Importing Helpful Guide [Ref 2]

    • dplyr Helpful Guide [Ref 3]

    • ggplot Helpful Guide [Ref 4]

    • Reference List