Course curriculum

  • 1

    1 - Introduction

  • 2

    2 - Data Collection and Data Management

    • 2.1 - Introduction

    • 2.2 - Overview and Data Validation

    • 2.3 - R Packages Used

    • 2.4 - Exploratory Analysis

    • 2.5 - Feature Engineering

    • 2.6 - Conclusion

    • Live Lesson: Week 1

  • 3

    3 - Model Building: Graduation Based on Age, Gender, Salary, Industry Code

    • 3.1 - Introduction

    • 3.2 - Splitting Data into Training and Testing

    • 3.3 - Poisson Regression

    • 3.4 - Generalised Additive Models

    • 3.5 - Gradient Boosting Machine

    • 3.6 - Conclusion

  • 4

    4 - Reporting and Model Validation

    • 4.1 - Introduction

    • 4.2 - Visualisation and Reporting on Model Performance

    • 4.3 - Merits of the Models

    • 4.4 - Recommendations

    • Live Lesson: Week 2

  • 5

    5 - Conclusion

    • 5 - Conclusion

  • 6

    Appendix and Further Resources

    • Blank R Notebook (Optional Use)

    • A User Guide for Jupyter Notebooks

    • Introducing R and RStudio

    • RStudio Helpful Guide [Ref F1]

    • Data Importing Helpful Guide [Ref F2]

    • dplyr Helpful Guide [Ref F3]

    • ggplot2 Helpful Guide [Ref F4]

    • Reference List

  • 7

    Project

    • Project Instructions

    • Project Template