Certificate in Data Science (30th April 2020)

Module 1 Learning Outcomes • Discuss why Data Science is important in the actuarial profession • Explain the Data Science pipeline • Organise tabular data in a spreadsheet • Provide an informed justification of the importance of data collection and cleaning Module 2 Learning Outcomes • Perform statistical analysis to real-life datasets • Extract insights from raw data • Align different data analysis approaches to different kinds of data • Discuss the implications of analysing data with machine learning techniques Module 3 Learning Outcomes • Explain the purpose of data visualisation and its role within data science • Critically analyse the differences between exploratory and explanatory visualisations • Compare different data types and the sorts of visualisation they can be used in • Describe the process of encoding data in a visual and the different encodings that can be used • Evaluate the effectiveness of particular data encodings Module 4 Learning Outcomes • Demonstrate high level understanding of AI and the role it can play to deliver benefits for your organisation • Identify potential applications of AI in practice • Discuss the main capabilities of AI and the core technologies that help deliver them • Explain different types of data used in AI and their characteristics and uses • Discuss the ethical implications of AI in different areas of the economy, government and society Module 5 Learning Outcomes • Analyse the main issues with the use of Data Science in key actuarial practice areas • Summarise the regulatory issues with using Data Science techniques (data protection, etc) • Appraise the requirements of professional standards actuaries should consider with regards to using Data Science • Distinguish the ethical considerations with Data Science • Explain the main principles of 'good data governance' Module 6 Learning Outcomes • Discuss further considerations around the use of Data Science and Artificial Intelligence techniques in the actuarial profession