Postgraduate taught 

International Financial Analysis MSc

Data Science and Machine Learning in Finance ACCFIN5246

  • Academic Session: 2025-26
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: 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 three-hour interactive workshops (involving 16 hours of main workshops and extended with an additional hour to cover empirical and computational course contents), followed by two final weeks of 2-hour workshops.

Excluded Courses

None

Co-requisites

None

Course Aims

The course aims to

■ Develop a thorough understanding of financial and economic data classes, implementing dynamic data acquisition routines, pre-processing information, context-dependent anomaly detection procedures and structuring heterogeneous data for the purpose of financial analysis. 

■ Provide a critical examination of linear, constrained linear and nonlinear estimation methods aimed at analysing large-dimensional datasets, including reduction and regularisation methods, variable selection and cross-validation techniques.

■ Provide an in-depth examination of supervised statistical learning and data-driven decision-making methods intended for formulating and addressing 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 heterogeneous financial datasets.

3. Critically examine reduction and regularisation methods to summarise large-dimensional datasets based on linear, constrained linear and nonlinear estimation methods.

4. Evaluate model performance and critically assess cross-model validation.

5. Develop data-driven decision-making routines with applications to risk management and asset allocation.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.