Course curriculum

  • 1

    1 - Introduction

    • 1.1 - Overview of the course

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    • 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

    • 1.5.1. Techniques for visualising data with Matplotlib

    • 1.5.2. Techniques for visualising data with Seaborn

    • 1.5.3. Techniques for visualising data with Pandas

    • 1.5.4. Techniques for visualising data with Plotly

    • 1.6 - Conclusion

  • 2

    2 - Natural Catastrophes Visualisation Case Study

    • 2.1 - Introduction

    • 2.2. Importing and Preliminary Analysis

    • 2.3. Visually Comparing Asia to 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. - Introduction

    • 4.2 - Creating a Simple Dashboard in Dash

    • 4.3 - Building a COVID-19 Dashboard in Dash Part 1: File Structure and Dashboard Configuration

    • 4.4 - Building a COVID-19 Dashboard in Dash Part 2: Sidebar Pages

    • 4.5 - Deploying using Heroku

    • 4.6 - Interactive COVID-19 Dashboard Built in Dash

    • 4.7 - COVID-19 Dashboard in Dash Zip File

  • 5

    Appendix and Further References

    • Blank Notebook

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

    • Using Jupyter Notebooks in this Platform

    • Lambda, map and apply functions

    • Python conventions

    • Debugging in Python