Course curriculum

  • 1

    Welcome to Introduction to Data Science in Insurance

    • Course Overview: Conceptual Introduction to Data Science in Insurance

  • 2

    1. Introduction to Data Science

    • 1. Introduction to Data Science

    • 1.1 Example Origins

    • 1.2 Definition in the case of our example

  • 3

    2. Data Analysis in the Context of Insurance

    • 2.1 Introduction

    • 2.2 Risk analysis

    • 2.3 Operational Benefits

    • 2.4 Wider Market Research

  • 4

    3. Introducing the Data Science Pipeline

    • 3. Introducing the Data Science Pipeline

  • 5

    4. Sources of Data

    • 4.1 Data value chain

    • 4.2 Internal versus External Data

    • 4.3 Examples of collecting additional data points to understand risk

  • 6

    5. Types of Data

    • 5.1 Different types of Data

    • 5.2 Example uses of different types of structured data

  • 7

    6. Introduction to Data Science Techniques

    • 6.1 Overview

    • 6.2 Focus on modelling aspects

    • 6.3 Example linear regression model

  • 8

    7. Wider Data Science Considerations

    • 7. Wider Data Science Considerations

    • 7.1 Legal, Regulatory, Professional and Ethical Considerations

    • 7.2 Explainable or Interpretable Models

    • 7.3 Conduct and Governance

    • 7.4 Other Considerations

  • 9

    8. Data Science Case Studies

    • 8. Data Science Case Studies

  • 10

    9. Summary of Introduction to Data Science

    • 9. Summary of Introduction to Data Science

  • 11

    Appendix

    • A1 Terminology Introduced

    • A2 Additional Reading

    • A3 References