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Module code and Title
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IFB113TC Programming for Business Application
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School Title
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School of Intelligent Finance and Business
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Assignment Title
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Submission Deadline
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5:00 PM, 30th October 2025
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General Information
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Category
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Details
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Submission Deadline
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5:00 PM, 30th October 2025
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Weight
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100% of course grade
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Assessment Type
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Individual project
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Format Requirements
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- Must include a cover page with student ID
- Recommended report format: See Appendix
- Typed, be proofread, professional appearance
- Font: ‘Times New Roman,, ‘Arial,, or ‘Calibri,, size line spacing
- Text alignment: ‘Justified,
- Harvard Referencing for citations and references
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Submission
Requirements
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- Submit the PDF report (. and the Python code file (.py) via LMO
- Report name: “IFB113TC- -Your ID”
- Python code name: “ -CWC- ID” (executable, well- commented)
- Ensure files viewable submission
- Late submissions subject to university,s late submission policy
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Word Limit
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- PDF 1,500 words 10%) and not exceed 40 pages (Exclusions: table contents, tables, figures, appendices, references, Python codes)
- Use tables charts for concise communication
- Reports that or fall short of the 1,500-word limit by more than may be subject to a penalty.
- Appendix Limitation:
Appendices are an important part of your report, providing supplementary information and evidence. While appendices are excluded from the main word and page count, to encourage conciseness and relevance, only the first 10 pages of appendices will be marked. This is to ensure that the main body of your report contains all critical analysis and discussion, while the appendices support these findings.
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Guidelines for Using AI Tools in
Programming
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See Appendix B
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Assessment Tasks
Instruction: This project is designed to develop the programming logic and technical skills needed to solve business challenges in today's data-driven retail environment. The project emphasizes not just coding proficiency, but the ability to translate business requirements into effective technical solutions that create measurable impact.
1. Project Background
In the competitive retail sector, data-driven insights are essential for optimizing strategies and driving growth. As a business analyst for a major US superstore, you will leverage a dataset containing transactional records (sales, profits, discounts) and product classifications. Currently, the company relies on manual spreadsheet analysis, leading to delayed insights. To address the challenges, you will develop a Python-based analytics application to automate critical processes, including sales performance tracking, profit trend visualization, and the identification of high-value customers. This solution will transform. raw data into actionable insights, enabling leadership to refine pricing strategies, allocate marketing resources effectively, and optimize inventory planning. By implementing this tool, the company aims to enhance decision-making, improve operational efficiency, and maintain a competitive edge in the evolving retail landscape.
1.1 Key Objectives:
1). Data Visualization: The company requires dynamic and interactive visualizations to understand sales and profit trends better. The application should generate different types of charts of sales and profit trends for different product categories over time. These visualizations will help identify peak sales and profit periods, emerging trends, and areas needing attention
2). Data Summary and Reporting: The application should provide essential statistical summaries of data, including the sum of all sales and profits, the highest transaction sales value, the average profit, and average customers score across all transactions, etc. This will help the company quickly grasp key metrics and performance indicators. The ability to generate and export these summaries in a structured report format is crucial for presenting findings to stakeholders and making strategic decisions.
3). User Interaction Features: To ensure ease of use, the application must allow users to upload the dataset files in CSV format, select the type of analysis they wish to perform, and view results in an intuitive interface. Features like file upload dialogs, analysis selection menus, and result displays are essential for facilitating smooth and efficient data handling.
1.2 Challenges and Considerations:
• Data Integrity and Quality: Ensure the application handles various data formats and potential data quality issues effectively. Users may submit data containing inconsistencies or errors, including: temporal discrepancies (e.g., ship dates before order dates), invalid value ranges (e.g., discounts exceeding 100%, negative sales or scores), and illogical relationships (e.g., profit calculations that don't align with sales and costs data). To ensure reliable results, the application should include basic validation and error handling mechanisms.
• Scalability and Performance: As the company’s transaction data grows, the application should be able to handle large datasets efficiently. Performance optimization techniques should be considered to ensure quick data processing and visualization.
• User Experience: The user interface should be designed with simplicity and functionality in mind. It should accommodate users with varying levels of technical expertise, providing clear instructions and feedback throughout the data analysis process.
1.3 Deliverables:
The final deliverables will include the fully functional data analysis application with the aforementioned features (.py file), a detailed project report documenting the development process and showcasing the application’s capabilities and applications in real-world scenarios. This project aims to empower the company with a robust solution for data analysis, helping them leverage their transaction data to drive strategic decisions and enhance overall business performance.