International Finance MFin
Data Science and Machine Learning in Finance ACCFIN5246
- Academic Session: 2026-27
- School: Adam Smith Business School
- Credits: 15
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course examines how the combination of data science and statistical learning techniques enable practitioners to translate information embedded in large-dimensional datasets to more efficient financial decisions. The course content comprehensively covers frontier theories, empirical methods, computational implementations, and applications used to formulate and address real-world financial problems.
Timetable
8 x 2 hour practical classes and workshops
5 x 1 hour (programming and computational) tutorials
Excluded Courses
None
Co-requisites
None
Assessment
1. Portfolio; Individual; 1000 words and numerical objective questions; 100%; ILOs 1-4.
Course Aims
The course aims to:
■ Evaluate the characteristics, structure, and limitations of financial and economic data.
■ Analyse high-dimensional datasets to extract and interpret underlying economic and financial factors using data reduction techniques.
■ Apply and critically assess statistical learning methods in the formulation and solution of complex financial problems.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Formulate real-world financial problems into statistical frameworks.
2. Implement data analytic software routines to acquire, structure and examine financial datasets.
3. Critically examine reduction and regularisation methods to summarise large-dimensional datasets.
4. Evaluate model performance and critically assess cross-model validation.