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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Cyber Security / Penetration Testing
2,400 words
Grey-Box Penetration Testing: Vulnerability Assessment, Exploitation and Mitigation
This technical cyber-security project presents an authorised grey-box penetration test conducted within a controlled virtual laboratory environment. The objective is to assess the security posture of a deliberately vulnerable target system, identify weaknesses in exposed network services, demonstrate how those weaknesses could be exploited, evaluate their security and organisational impact, and recommend appropriate mitigation measures. The assessment follows a practical penetration-testing workflow supported by technical evidence, screenshots, activity records and academic research. The project begins with laboratory configuration, network discovery, service enumeration and vulnerability analysis. Tools including Kali Linux, Metasploitable, VMware, Nmap, Netcat and Metasploit are used across the testing lifecycle. Identified services are mapped to known vulnerabilities before controlled exploitation is undertaken and the resulting access is documented. The activity log records the progression from environment setup and network scanning through vulnerability identification, exploitation, evidence collection and final reporting. Five principal attack vectors are examined. These include the vsftpd 2.3.4 FTP backdoor, Samba username-map-script exploitation, an UnrealIRCd backdoor, insecure Java Remote Method Invocation and a misconfigured DistCC service. The practical demonstrations show how vulnerable or incorrectly configured services can permit unauthorised command execution and, in several cases, privileged shell access. For each vulnerability, the report explains the weakness, exploitation process, observed result, security impact and proposed mitigation. Recommended controls include patching or upgrading obsolete services, disabling unnecessary services, implementing firewall restrictions, strengthening authentication and input validation, restricting access to authorised systems, applying least privilege and monitoring suspicious activity. The project also incorporates group management and reflective practice. Team members perform specialised roles covering laboratory configuration, reconnaissance, vulnerability analysis, exploitation and documentation. Individual reflection considers technical performance, teamwork, evidence management and future skills development, demonstrating how structured collaboration contributes to an effective penetration-testing engagement. Important: unlike the earlier assignment briefs, these uploads appear to be completed student/project materials rather than the official 7COM1068 assessment brief. Therefore I would not invent the university, academic level or academic year. If you upload the actual 7COM1068 assignment guideline, I can fill those fields exactly.
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Machine Learning / Artificial Intelligence and Data Science
1,011 words
End-to-End Machine Learning Model Development, Testing and Evaluation
This Level 7 Machine Learning and Intelligent Agents assignment requires students to develop and document an end-to-end machine-learning solution, covering the complete process from data preparation through model training, tuning, testing and evaluation. Students independently select a suitable dataset or scenario, formulate an appropriate research question and determine whether the problem should be addressed using supervised learning, unsupervised learning or reinforcement learning. Appropriate machine-learning algorithms must then be implemented to create a model capable of being trained and objectively tested. The assessment encourages the use of a structured machine-learning development methodology. Students are expected to explain the selected scenario and data source, perform suitable data preparation and Exploratory Data Analysis (EDA), and provide a reasoned justification for the machine-learning methods selected. The development process should demonstrate how the chosen algorithms are trained and fine-tuned before their performance is evaluated using established and relevant metrics. Published academic research should be incorporated to justify methodological choices and support the interpretation of results. The technical work is normally documented within a Jupyter Notebook, combining Markdown explanations with executable code cells. Alternatively, the report may be produced in Microsoft Word provided that the Python implementation is included. The assignment therefore assesses both conceptual understanding and practical programming competence. Students must demonstrate an ability to identify the performance of machine-learning algorithms, implement machine-learning techniques using an object-oriented programming language, and evaluate which algorithms are appropriate for a particular analytical brief. Assessment places particular emphasis on three areas: the Introduction, the Machine Learning Process, and the Evaluation of Model Performance. The marking criteria reward strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms, and critical evaluation of the final solution. At the highest achievement level, implementations are expected to work without exception, satisfy the required functionality, demonstrate comprehensive testing and extend beyond the basic requirements. Overall, the assignment combines research-question formulation, data analysis, algorithm selection, machine-learning implementation, model optimisation and evidence-based evaluation within a reproducible technical workflow. All academic sources and supporting material must be presented using Harvard referencing.
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Management / Digital Transformation and Leadership
1,500 words
Leading Through Digital Change: Digital Transformation Report and Future Technology Poster
This Masters-level assessment for the Leading Through Digital Change module examines how organisations can respond strategically and effectively to rapid technological and digital transformation. Students take the role of a Digital Transformation Manager for one selected international organisation and prepare a professional Digital Transformation Report accompanied by an A4 digital poster. The purpose is to evaluate the organisation's current digital context and recommend changes that can strengthen competitive advantage and create sustainable business value. The first component requires critical evaluation and recommendation of one appropriate digital transformation strategic framework. Students may apply frameworks such as the McKinsey 4Ds, BCG Three Stages, Gartner's Six Steps or Cognizant's Four Pillars. The analysis should establish clear digital transformation objectives relevant to organisational functions such as operations, ICT and marketing, while using organisational evidence, academic research and practical examples to justify the proposed strategic direction. The second component is an academic poster evaluating two disruptive technologies or techniques expected to affect the chosen organisation, its industry, employment and the labour market over the next five years. Potential technologies include Artificial Intelligence and Machine Learning, 5G connectivity, the Internet of Things, robotics, drone delivery, blockchain, augmented reality and virtual reality. The poster should combine academic literature with real-world examples to demonstrate the likely opportunities, challenges and wider organisational implications of technological disruption. The final component focuses on digital leadership. Students analyse and recommend two suitable leadership approaches for managing and supporting digital transformation. Relevant approaches may include agile leadership, ethical-tech leadership, people-oriented leadership, hyperaware agile leadership and Goleman's leadership styles. Overall, the assessment integrates digital strategy, innovation, emerging technologies and leadership. The wider module also covers digital transformation strategies, data-driven decision-making, leadership in the digital age, artificial intelligence in contemporary business, digital risk management and planning for the future. Reference style: Harvard. Main report word limit: 1,500 words. Poster: A4 size with no specified word count.
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Computer Science / Research Methods
500 words
Interim Report: Systematic Literature Review Research Protocol
This postgraduate computer science assignment requires students to prepare an Interim Report establishing the research protocol for a systematic literature review. The assessment focuses on whether the proposed research question is suitable for computer science research, clearly formulated, appropriately motivated by existing literature, and capable of being investigated through a systematic review. The main body of the report must not exceed 500 words, while supporting evidence may be provided separately where appropriate. The report is structured around three main chapters. Chapter 1 introduces the selected research area and summarises the purpose and structure of the report. Chapter 2 provides relevant background and a brief history of the chosen research domain, identifies the problem being addressed, and supports the discussion with at least two relevant academic citations. Chapter 3 presents the formal Literature Review Protocol, including the research question, its context, and the associated PICO elements: Population, Intervention, Comparison and Outcome. Students must also develop and test a Boolean search string using IEEE Xplore and report the number of papers returned by that search. The search strategy is accompanied by explicit inclusion and exclusion criteria governing which studies will be considered for the review. The supplied protocol template requires students to document these elements through two structured tables: one covering the research question and PICO framework, and another recording the search string, paper count, and study-selection criteria. The assessment places significant emphasis on methodological consistency. The research question, PICO elements, search strategy, paper count and inclusion/exclusion criteria must align logically with one another. Students are also assessed on the justification and motivation of the research question, document structure, presentation quality, spelling, grammar and academic referencing. The report must include a title page, table of contents, bibliography and the required research-protocol tables. Harvard referencing is required for both in-text citations and the final reference list. Overall, the assignment develops the foundational skills required for conducting a rigorous systematic literature review, including research-question formulation, structured evidence searching, transparent study-selection procedures, academic justification and professional research reporting.
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