The vast proliferation of data and increasing technological complexities continue to transform the way industries operate and compete. Over the last two years, 90 percent of the data in the world has been created as a result of the creation of 2.5 exabytes of data on a daily basis. Commonly referred to as big data, this rapid growth and storage creates opportunities for collection, processing and analysis of structured and unstructured data.
Financial services, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, modern optimisation techniques (such as linear programming) are adopted to solve many computational finance problems ranging from asset allocation to risk management, from option pricing to model calibration. The continued adoption of big data will inevitably transform the landscape of financial services.
The module will be a mix of theory and practice with big data cases in finance.
For the theoretical part, the algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structured and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.
For the pratical part, the big data in finance cases will be introduced together with the study of relevant software tools.
Learning Outcomes:
After completing this module, students will be expected to be able to:
1) Understand the principles of (data-driven) algorithms such as modern machine learning and data mining algorithms
2) Understand the application of (data-driven) algorithms on financial industry
3) Use software tools to build up data-driven algorithms and analyse the huge amount of historical data
Financial services, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, modern optimisation techniques (such as linear programming) are adopted to solve many computational finance problems ranging from asset allocation to risk management, from option pricing to model calibration. The continued adoption of big data will inevitably transform the landscape of financial services.
The module will be a mix of theory and practice with big data cases in finance.
For the theoretical part, the algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structured and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.
For the pratical part, the big data in finance cases will be introduced together with the study of relevant software tools.
Learning Outcomes:
After completing this module, students will be expected to be able to:
1) Understand the principles of (data-driven) algorithms such as modern machine learning and data mining algorithms
2) Understand the application of (data-driven) algorithms on financial industry
3) Use software tools to build up data-driven algorithms and analyse the huge amount of historical data
- Module Supervisor: Panagiotis Kanellopoulos
