Course curriculum

  • 1

    1 - Introduction

    • 1.1 - Overview of the Course

      FREE PREVIEW

    • 1.2 - Introducing the Full Data Science Pipeline

    • 1.3 - Introduction to the Case Studies and Datasets Used

    • 1.4 - High level Principles and Considerations

    • 1.5 - Techniques for Visualising Data in R

    • 1.6 - Conclusion

    • ggplot2 Essentials
  • 2

    2 - Natural Catastrophes Visualisation Case Study

    • 2.1 - Introduction

    • 2.2 - Importing and Preliminary Analysis

    • 2.3 - Visually Comparing Trends in Natural Disasters in Asia and the Americas

    • 2.4 - Comparing the Frequency, Deaths, and Economic Damage by Disaster Type

    • 2.5 - Conclusion
  • 3

    3 - COVID-19 Case Study

    • 3.1 - Introduction

    • 3.2 - Techniques for Visualising COVID-19 Data

    • 3.3 - Understanding the Effect of Lockdown Restrictions on Daily Case Numbers

    • 3.4 - Conclusion
  • 4

    4 - Building a Simple COVID-19 Dashboard

    • 4.1 - Introducing Shiny

    • 4.2 - Creating a Simple Dashboard

    • 4.3 - Expanding to a COVID-19 Dashboard

    • 4.4 - Complete COVID-19 Dashboard as a Zip File

    • 4.5 - RShiny Dashboard

    • R Shiny Gallery of Examples

    • Deploying R Shiny Dashboards
  • 5

    Appendix and Further References

    • Blank R Notebook (Optional Use)

    • How to Install R, External Packages, Jupyter, and RStudio (Reference)

    • R Shiny Tutorial (provided by R Studio) [Ref 1]

    • Mastering Shiny [Ref 2]

    • dplyr Helpful Guide [Ref 3]

    • ggplot2 Helpful Guide [Ref 4]

    • Technical Actuarial Standards 100 (TAS 100): Principles for Technical Actuarial Work [Ref 5]

    • Reference List