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.

Big Data Analytics ACCFIN5231

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

The course will provide students in depth knowledge and techniques of big data analytics and machine learning, as well as some relevant programming skills.


Course is delivered over 2 or 3 weeks, comprising of 15 hours of lectures and 4 hours of practical classes.

Requirements of Entry

Registration on the MSc Financial Technology programme

Excluded Courses





ILO being assessed

Course Aims

The aim of this course is to provide students with in-depth knowledge of advanced aspects of big data analytics, learning the techniques and analytical approach to the examination of large data sets opportunities to apply appropriate machine learning techniques in order to analyse big data sets, to assess the statistical significance of data mining results, and to use statistical packages to perform basic data mining tasks on big data.

Intended Learning Outcomes of Course

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


1. Demonstrate knowledge of advanced aspects of big data analytics

2. Apply appropriate machine learning techniques to analyse big data sets

3. Assess the statistical significance of data mining results

4. Utilize statistical packages (R and Python) to perform basic data mining tasks on big data.

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.