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Certificate in Data Science – syllabus

The purpose of this certificate is to provide an introduction to the concepts, tools and techniques used in data science, and their relevance within an actuarial context.

The certificate will also introduce you to some of the technical tools used in data science. However it is not intended to be a course on technical data science programming. The syllabus is provided below which you should refer to before signing up for the course.

Module 1: Introduction to data science, data management and processing

  • Introduction to planning a data science project
    • Professional and ethical considerations
       
  • Understanding how to create the data set
    • Data collection methods - methods by which data can be collected e.g. page scraping, survey, etc.
    • Data preparation - importance of data ‘cleaning’, validity and quality
    • Data analysis - how format and volume of data limits methods of analysis available

Module 2: Data analysis and introduction to machine learning (ML)

  • Understanding of common statistical techniques in ML
    • Analytical methods for different data types and format
  • Visualise result of data analysis
    • Factors to consider
  • Introduction to ML using Linear Regression model
    • Benefits and drawbacks of analysis
  • Application to (IFoA material) case study

Module 3: Data visualisation and communication

  • Identifying audience requirements
  • Data scientist as ‘storyteller’
  • Building a narrative
  • Explaining the technical - how to communicate the role played by ML and/or AI techniques resulting in an informed audience
  • Visualisation techniques
  • Using 'Off The Shelf'/proprietary tools
  • Issues for communication in an actuarial context

Module 4: Further analysis and artificial intelligence

  • Selection of analysis method
  • Common machine learning models
  • Comparison of AI and ML
  • AI selection criteria
  • Benefits and drawbacks
  • Application to (IFoA supplied) case study

Module 5: Good practice of data science, and responsible AI

  • Responsibilities of actuaries around data science and AI (IFoA material)
  • Awareness of ethical issues which may possibly arise within participants organisation
  • Developing ethical and professional safeguards (IFoA material)

Module 6: Future directions

  • Discussion of future directions of data science and AI in actuarial contexts
  • Question and answer (Q&A) and support to complete assignments

Optional and non-assessed materials

Module 1

  • Introduction to Python for Data Science data collection
  • Page scraping using Python
  • Interrogating an 'open data' API
  • Examining a non-relational database

Module 3

  • Visualising Data with Python libraries

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