Course Catalogue

Machine Learning and Data Science in Business MGT5511

  • Academic Session: 2026-27
  • 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
  • Curriculum For Life: No

Short Description

This course offers a hands-on introduction to machine learning and data science solutions tailored to today's multimodal business environment. It emphasises the strategic applications rather than a focus on mathematical foundations or technical implementation alone, equipping students to apply these methods effectively in business contexts and to engage in data-informed decision-making.

Timetable

10 x 2 hour lectures

5 x 1 hour workshops

Requirements of Entry

Students must be registered on one of the associated programmes listed in this course specification.

Excluded Courses

None

Co-requisites

None

Assessment

1. Portfolio; Individual; 1750 words; 50%; ILOs 1-3.

2. Portfolio; Individual; 1750 words; 50%; ILOs 3-5.

Course Aims

This course aims to:

 

■ Transform raw and unstructured data into actionable business intelligence using modern data science frameworks.

 

■ Leverage multimodal data (e.g., text, images, and video) to support informed business decision-making.

 

■ Analyse real-world case studies to identify and implement practical machine learning solutions.

 

■ Navigate complex data environments with a focus on the effective application of analytical methods.

Intended Learning Outcomes of Course

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

 

1. Critically assess machine learning frameworks to determine their strategic advantages, limitations, and suitability for diverse business contexts.

 

2. Utilise data science tools to transform complex and unstructured data (e.g., text, images, and video) into structured, actionable business intelligence.

 

3. Translate business problems into well-defined machine learning tasks and select appropriate methodologies to address them.

 

4. Evaluate the performance of machine learning solutions using relevant business-oriented metrics and contribute to the optimisation of model selection and deployment.

 

5. Interpret outputs from machine learning models to generate data-informed insights and support strategic decision-making.

Minimum Requirement for Award of Credits

No exceptions