Programming in Business Analytics MGT5493

  • Academic Session: 2025-26
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

Programming in Business Analytics is a hands-on course designed to transform business students with no prior coding experience into proficient users of Python for data analysis. Through real-world business scenarios, students will develop essential programming skills, including manipulating and analysing business data with Python libraries like Pandas and NumPy, creating professional visualizations with Matplotlib and Seaborn, and automating routine analysis tasks. With a strong emphasis on practical applications and industry-relevant tools, students will build a portfolio of business analytics projects, synthesizing insights and gaining technical expertise highly sought by employers.

Timetable

Campus Lectures: 16 hours (8 x 2 hours)

Campus Lab workshops: 10 hours (5 x 2 hours)

Requirements of Entry

Please refer to the current graduate prospectus at http://www.gla.ac.uk/postgraduate/prospectus

Excluded Courses

None

Co-requisites

None

Assessment

Summative assessment will involve the creation of a portfolio containing two individual data programming artefacts, each contributing 50% of the final grade.

Each artefact will be developed through up to 15 scaffolded questions, requiring students to apply programming, data analysis, and critical thinking skills to solve real-world business problems.

 

ILOs

Assessment

Weighting

Length/Duration

1, 2, & 3

Individual Artefact 1

50%

15 scaffolded questions

1, 3 & 4

Individual Artefact 2

50%

15 scaffolded questions

 

Course Aims

This course aims to provide students with essential Python programming skills for business analytics, focusing on practical data handling, analysis, and visualization using libraries like pandas, NumPy, Matplotlib, and seaborn. By combining theory with practical application, the course develops problem-solving skills to address real-world business challenges.

Intended Learning Outcomes of Course

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

1. Demonstrate proficiency in Python programming to perform advanced data management and analysis.

2. Critically analyse business data using Python libraries such as Pandas and NumPy, focusing on data cleaning, transformation, and statistical analysis to derive insights.

3. Integrate and apply programming concepts with visualization tools (e.g., Matplotlib, Seaborn) to extract and present meaningful business insights from complex datasets.

4. Synthesize actionable insights from real-world business problems by combining advanced programming techniques with critical thinking and analytics.

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.