Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Financial Modelling ACCFIN2025

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 2 (SCQF level 8)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

This foundational course develops knowledge and understanding of financial modelling. Students will develop their research, presentation, data management and statistical analysis skills in this course by applying practical financial modelling in a finance context.


Workshops (2 hours x 5 weeks) 

Workshops - computer labs (2 hours x 9 weeks) 

A number of asynchronous activities will take place online via VLE (5 hours in total across the semester)

Requirements of Entry

Normally a grade D3 or above in Finance 1 (or equivalent)


Please refer to the current undergraduate prospectus at :

Excluded Courses





Intended Learning Outcomes



Word Length/ Duration

1, 3, 4, 6

Individual Presentation


10 minutes + Q&A

Course Aims

The aims of this course are to:

1. develop knowledge and understanding of financial modelling within the rapidly changing world of finance.

2. build capacity to apply financial theory to real-world problems in finance via the use of financial databases and statistical/programming software.

3. develop research, data presentation, data communication, data management, and statistical analysis skills.

Intended Learning Outcomes of Course

By the end of this course, students will be able to:

1. Apply appropriate econometric methods for data analysis in the field of finance.

2. Demonstrate foundational research skills: including the ability to locate, read, and understand relevant academic literature in finance.

3. Apply practical data management and programming skills (including locating, downloading, and processing of appropriate datasets).

4. Use information technology and statistical/programming software in relevant tasks.

5. Manage uncertainty in the context of the evolving business environment.

6. Produce and orally present data outputs for communication to the intended audience(s).

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components of the course summative assessment.