Module detail

SHE level	11
SCQF credit points	15
ECTS credit points	7.5
Module code	MMI330815
Module Leader	Hanwool Jang
School	Glasgow School for Business and Society
Subject	Finance and Accounting
Trimester	A (September start)

Summary of content

This module aims to develop students' advanced programming capabilities in Python and R for solving complex business analytics challenges, enabling them to bridge the technical-business divide through effective implementation of computational solutions. Students will acquire proficiency in data manipulation, analysis, visualisation, and modelling techniques essential for extracting actionable insights from business data. The module emphasises practical application of programming skills to real-world business problems, developing students' ability to implement appropriate analytical methodologies, evaluate their effectiveness, and communicate results persuasively to diverse stakeholders.

It offers a practical and in-depth introduction to programming for business analytics, equipping students with the skills to use Python and R to process, analyse, and communicate business data effectively. With a strong emphasis on real-world application, the module begins by building foundational programming skills, including data types, structures, control flow, and syntax in both languages. Students are then introduced to core data analysis tasks, such as data cleaning, transformation, and exploratory data analysis (EDA), using business-focused datasets to identify trends, detect anomalies, and uncover actionable insights.

As the module progresses, students gain hands-on experience in applying statistical methods, machine learning techniques, and time series forecasting within business contexts. Using widely adopted libraries and packages such as pandas, dplyr, ggplot2, matplotlib, and scikit-learn, students will solve complex problems related to marketing, finance, operations, and customer behaviour. Through these applications, they will learn to manage large and often messy datasets, develop predictive models, and present their findings in a clear and impactful way using interactive visualisations and dashboards.

The module concludes in developing the ability to communicate technical finding

Module details

This module integrates programming proficiency with applied data analysis to equip students with both conceptual understanding and the practical skills needed to solve complex business problems using Python and R. The focus is on developing technical fluency alongside analytical thinking and communication skills, enabling students to generate actionable insights from large and varied datasets. Through a combination of lectures, extended computer lab sessions, and structured independent learning, students will develop the ability to build, implement, and interpret data-driven solutions in real-world business contexts.

Lectures:
Lectures will introduce key programming principles and data analysis techniques, including data structures, control flow, data preparation, statistical analysis, and introductory machine learning. Emphasis will be placed on the practical application of these techniques using business-relevant examples such as customer segmentation, time series forecasting, and text analysis. Active learning strategies, such as live coding demonstrations, in-class exercises, and problem-solving discussions, will be employed to engage students, strengthen understanding, and encourage the development of practical skills.

Computer Lab Sessions):
Lab sessions provide students with extended hands-on experience in applying programming concepts using Python and R. Students will complete structured exercises in data cleaning, transformation, exploratory data analysis, visualisation, and predictive modelling using real-world business datasets. These sessions will introduce widely used libraries and tools (e.g., pandas, dplyr, matplotlib, ggplot2, scikit-learn) and provide opportunities for students to build coding workflows that reflect industry practices. Tasks will include building interactive dashboards, developing machine learning models, and conducting time series forecasting. Lab activities will also promote the application of best practices in coding style, documentation, and

Module structure

Activity	Total hours
Lectures	22
Tutorials	0
Practicals	10
Seminars	0
Independent Learning	108
Assessment	10
Placement	0

Assessment methods

Component	Duration	Weighting	Threshold	Description
Course Work001		50	45	CW1: Class Test
Course Work002		50	45	CW2: Practical Assignment (Project)

Academic essentials

Data Programming for Business Decision-Making

Summary of content

Module details

Module structure

Assessment methods