ENG501 Software Design and Development for Data Science Subject Title Software Design and Development for Data Science  Subject Code ENG501 Assessment Assessment 3 – Final Project (Individual):  Individual/Group Individual

ENG501 Software Design and Development for Data Science Subject Title Software Design and Development for Data Science

 Subject Code ENG501 Assessment Assessment 3 – Final Project (Individual):

 Individual/Group Individual

 Length 2,500 words (±10%)

 Use of AI Category (A/B/C/D)   Category B – Limited and Transparent Use of AI

 Learning Outcomes SLO-3,4 Submission Week 12 Weighting 40% Total Marks 40 Context

In this assessment, students take the design and recommendations of their group from assessment 2 and prepare a report discussing processes and issues relating to security, reliability and testing, including issues relating to the data the system is utilising, and recommend specific processes that must be included when the system is implemented.

  Instructions  

Section Description Approx. Words

1. Introduction & System Context Overview of system from Assessment 2 and purpose of report 250–300
2. Security Considerations Security risks, controls, compliance, and processes 500–550
3. Reliability & Availability Fault tolerance, monitoring, backups, resilience 400–450
4. Data Issues & Governance Data quality, bias, drift, lifecycle, governance processes 400–450
5. Testing & Validation Software, data, model testing, UAT, continuous testing 350–400
6. Recommended Implementation Processes Governance, change management, training, monitoring 350–400
7. Conclusion Summary and reflection on implementation readiness 200–250 References  APA style Will not be counted in word limit Total   ~2,500 words  

Submission Instructions  

·   Submit one individual report via Open Learning 

·   File format: Word 

·   Usea cover sheet, logical section headings, and explicit references guidance, aligned with ENG501 and academic expectations.

·   Late submissions are subject to Institutional penalties.

Criterion HD (85–100%) D (75–84%) C (65–74%) P (50–64%) F (<50%) Marks

1. System Understanding & Context (SLO 3) Excellent understanding; strong continuity with Assessment 2; context clearly articulated Clear understanding with good continuity Adequate understanding; minor gaps Basic summary; limited linkage Poor or unclear system understanding 6
2. Security Analysis & Recommendations Comprehensive, well‑justified security analysis with realistic processes Strong analysis with appropriate recommendations Sound but limited depth Basic discussion; mostly descriptive Weak or inaccurate analysis 6
3. Reliability & Availability (SLO 4) Insightful analysis of resilience, fault tolerance, and monitoring Clear and relevant discussion Adequate but surface‑level Limited consideration of reliability Minimal or incorrect discussion 6
4. Data Quality & Governance Excellent treatment of data issues, bias, lifecycle, and governance Strong understanding with good examples Competent discussion Basic coverage only Poor understanding of data issues 6
5. Testing & Validation Comprehensive, realistic testing strategy including AI models Well‑structured testing discussion Adequate but incomplete Limited testing coverage Inadequate or missing testing 6
6. Implementation Processes & Practicality Highly practical, well‑reasoned processes Clear and feasible recommendations Generally appropriate processes Vague or generic recommendations Unrealistic or missing processes 4
7. Conclusion & Integration Excellent synthesis of key points; clearly demonstrates how processes support secure and reliable implementation Clear and coherent conclusion summarising insights Adequate summary with limited integration Weak or repetitive conclusion Missing or irrelevant conclusion

2 8. References & Academic Sources High‑quality, credible sources; consistently and correctly referenced using an academic style Appropriate sources with minor referencing issues Adequate sources; some inconsistencies Limited or weak sources; formatting issues Missing, incorrect, or uncited references 2 9. Use of AI & Academic Integrity, References  Appropriate, transparent, critical use with clear declaration Appropriate use declared Declaration present but limited detail Minimal compliance No declaration or misuse 2 Total           40  

Note: This report is provided as a sample for reference purposes only. For further guidance,