BST851 Business Data Analytics Coursework Assignment 2026

BST851 COURSE TASKS

Data analytics is generally used to provide evidence and inform decisions. In a typical business data analytic task, you can use data to inform decisions, verify claims and assumptions, and answer or refine questions. In this coursework, you are first asked to choose a dataset and discuss a relevant problem to the dataset that needs to be informed by data analysis; it could be in the form of questions, claims or assumptions.  

Steps:

1.Dataset Selection

• Find a suitable dataset that is accessible, manageable in size, and rich enough for multiple analyses.
• Do not reuse datasets from class examples, homework, or labs.
• Submit your dataset to the module leader for approval.

You can use any dataset you find interesting to analyse on the web. A good option would be to use Dataset Research
Below you see some links to find publicly available datasets as well, but there are plenty more that might not be 

listed below: 

• NHS Scotland Open Data 
• NHS UK Data 
• Bikeshare data portal 
• UK Gov Data 
• TidyTuesday 
• Kaggle datasets 
• OpenIntro datasets 
• Awesome public datasets 
• Youth Risk Behavior Surveillance System (YRBSS) 
• PRISM Data Archive Project 
• Harvard Dataverse 
• State Wales dataset 
• Edinburgh Open Data 
• Open access to Scotland’s official statistics

2. Project Setup

• Download the project template (zip) from Learning Central → Assessment Area.
• Rename the folder as firstname_lastname_studentnumber.
• Place your dataset in the data folder.
• Save R or Python scripts in their dedicated folders (e.g., rscripts, pscripts).
• Create additional folders, if required (e.g., images).

3.Report Writing (in Quarto)

You will use Quarto to write your report; more information about Quarto is accessible here: Quarto

Your report should include:

• Executive Summary: summarizes the entire report for a non-technical manager (the business problem, data, the analytics solution and recommendations)
• Introduction: Describe the general topic, aims, and motivation.
• Business Problem: Define the business problem, assumption, or question that the analysis will address.
• Data source and preparation: Provide the dataset source and variable descriptions, and discuss the data quality process.
• Data Analysis:
  o Data wrangling
  o Visualisations that follow good design principles.
  o Code in R and/or Python to demonstrate analysis.
• Methods:  
  o Which modeling technique is used? (supervised ML, forecasting, or text analysis).
  o Which performance measures are relevant?
• Discussion: Interpret findings, evaluate whether questions are answered, and note surprises or interesting patterns.
• Conclusions: Summarise insights and implications.

4.Deliverables

• A zip file containing the entire project folder (including qmd file, data, images, and scripts, etc).
• A PDF file rendered from the Quarto report.

GENERAL GUIDANCE

Referencing:

All materials cited by your work should be properly referenced using Zotero, which is connected to Quarto, with a list at the end of your essay.

Submission

Students must submit their assignment via two separate links on Learning Central by 06/01/2026: one for a single PDF file and another for a ZIP file. Two submission points will be created there for that purpose 5 weeks before the deadline. Work submitted late without an extension will be capped at the pass mark for the first 24hrs and set to zero after that time. 

Should you experience any difficulties in submitting your work, please contact the [UG/PG] Hub

Extenuating Circumstances

If you experience extenuating circumstances which means you are unable to submit your assessment on time or where you have submitted your work but feel that your circumstances are related to a long-term health condition, protected characteristic and/or caring responsibility, you can declare your extenuating circumstances and request one of the following remedies: -

• Deadline Extension to your submission deadline
• A deferral to your next submission opportunity
• An Exam Board remedy (only applies to students who have participated in an assessment but have circumstances related to a long-term health condition, protected characteristic and/or caring responsibilities. (evidence required) 

You will NOT be able to declare your extenuating circumstances more than 2 weeks before a deadline.

Mark Return and Feedback

Students should expect their mark and the mark distribution of the whole cohort (as part of generic feedback) to be returned on 03/02/2026. However, until these marks are ratified by the Examination Board, any marks given will be provisional.

APPENDIX 1: Use of Generative AI: Conditionally Open

Grounded in the principles of transparency and responsibility, this course encourages students to leverage AI for collaboration and mutual learning to enhance the quality of learning and output. 

You must briefly explain how you used generative AI for text summarisation, topic ideation, writing improvement, translation, coding, image creation, quiz answering, or any other purpose. Mention this in the footnote of the introduction of your assignments. If usage is discovered without proper disclosure, the instructor has the right to reevaluate the deliverable and adjust scores. In turn, the instructor will indicate if course materials or learning resources were derived from generative AI. 

APPENDIX 2: Generic Marking Criteria 

Skills and Abilities Expected

1.Critical Analysis and Evaluation
a.Engage deeply with complex concepts and theories.
b.Critically assess research, arguments, and evidence.

2.Independent Research Skills
a.Design and conduct independent research.
b.Use appropriate methodologies and ethical considerations.

3.Problem-Solving and Innovation
a.Tackle complex issues with creativity and originality.
b.Apply knowledge to new or unfamiliar contexts.

4.Advanced Communication Skills
a.Communicate complex ideas clearly in writing and presentations.
b.Adapt communication for different audiences.

5.Synthesis of Knowledge
a.Integrate information from various sources to create coherent arguments.

6.Reflective Practice
a.Evaluate personal learning and development critically.

APPENDIX 2: COURSEWORK RUBRIC

Level 7 (Masters) Generic Assessment Criteria