Course curriculum

  • 1

    1 - Introduction

  • 2

    2 - Data Cleaning

    • 2.1 - Introduction

    • 2.2 - Overview of Business Tasks and Models

    • 2.3 - Overview of the Data

    • 2.4 - Relevant Python Packages

    • 2.5 - Conclusion

  • 3

    3 - Data Collection and Data Management

    • 3.1 - Introduction

    • 3.2 - Preliminary Analysis

    • 3.3 - Feature Engineering

    • 3.4 - Conclusion

  • 4

    4 - Model Building

    • 4.1 - Introduction

    • 4.2 - Logistic Regression

    • 4.3 - Decision Trees

    • 4.4 - Advanced Decision Trees

    • 4.5 - Generalised Linear Model

    • 4.6 - Conclusion

  • 5

    5 - Reporting

    • 5.1 - Introduction

    • 5.2 - Visualisation

    • 5.3 - Merits of the Models

    • 5.4 - Conclusion

  • 6

    Appendix and Further Reading

    • Blank Python Notebook (Optional)

    • Techniques for Visualising Data with Pandas

    • Map() and apply() Functions

    • How to install Python, External Packages, Jupyter, and Spyder

    • Using Jupyter Notebooks in this Platform

    • Python Conventions