Data Science in Insurance

Introduction to Data Science in Insurance (C1)

In this introduction of data science in insurance, we discuss data analysis and data sources and provide an overview of some data science techniques, before touching on the ethical considerations in data science. £100 once-off fee, 3 months' access

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Course curriculum

1. Course Overview: Conceptual Introduction to Data Science in Insurance
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1. 1. Introduction to Data Science
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2. 1.1 Example Origins
3. 1.2 Definition in the case of our example
1. 2.1 Introduction
2. 2.2 Risk analysis
3. 2.3 Operational Benefits
4. 2.4 Wider Market Research
1. 3. Introducing the Data Science Pipeline
1. 4.1 Data value chain
2. 4.2 Internal versus External Data
3. 4.3 Examples of collecting additional data points to understand risk
1. 5.1 Different types of Data
2. 5.2 Example uses of different types of structured data
1. 6.1 Overview
2. 6.2 Focus on modelling aspects
3. 6.3 Example linear regression model
1. 7. Wider Data Science Considerations
2. 7.1 Legal, Regulatory, Professional and Ethical Considerations
3. 7.2 Explainable or Interpretable Models
4. 7.3 Conduct and Governance
5. 7.4 Other Considerations
1. 8. Data Science Case Studies
1. 9. Summary of Introduction to Data Science
1. A1 Terminology Introduced
2. A2 Additional Reading
3. A3 References

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£100.00
27 lessons
0 hours of video content

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Introduction to Data Science in Insurance (C1)

Course curriculum

Welcome to Introduction to Data Science in Insurance

1. Introduction to Data Science

2. Data Analysis in the Context of Insurance

3. Introducing the Data Science Pipeline

4. Sources of Data

5. Types of Data

6. Introduction to Data Science Techniques

7. Wider Data Science Considerations

8. Data Science Case Studies

9. Summary of Introduction to Data Science

Appendix

About this course

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