Close
Description
The module is primarily focused on analysing and extracting patterns, anomalies and insight from financial data using a data science perspective. Machine learning models to assess, validate, and parameterize complex financial datasets will be covered. Practical issues in the evaluation and curation of data sources will also be explored.
To provide an introduction to statistical and machine learning applications in finance. As new and more complex financial problems emerge, finance analytics faces exciting challenges in new applications of computational tools and the development of superior methods. The module will offer students a practical hands-on experience in designing, analyzing and interpreting complex financial tools/datasets, enabling students to prepare for entering specialist employment in financial/related sector or academic research.
Learning Outcomes
At the end of the course, students will:
- Be able to analyze statistical properties and probabilities of financial datasets
- Understand how to use appropriate machine learning models depending on objectives and data available
- Understand and be able to optimize and validate models and quantify their performances
- Be able to identify, interpret and present technical ideas or information through numbers and visualizations
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
听