Academic Model Answers Library — Megaminds (UK Postgraduate)

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Research Methods

Assignment 3 – Large Language Models: LLM Coding and Report

This individual assessment for the Research Methods module focuses on the application of Large Language Models (LLMs) to a practical data science problem. The assignment is worth 25% of the module and is designed to develop students’ knowledge and understanding of research methods, investigative planning, data analysis, model evaluation and effective technical communication. Students are required to complete both a coding component and a concise written report demonstrating how an LLM has been selected, trained or fine-tuned, applied to a suitable task and evaluated against an appropriate baseline. The assessment begins with familiarisation with relevant literature. Students are expected to investigate the history and development of their chosen problem and examine the methods that have previously been used to address it. The brief provides key papers on common types of LLMs as a starting point for the literature review. Students then select a task that can be addressed through fine-tuning an LLM, with examples including sentiment analysis, fake news detection and topic classification. A publicly available dataset suitable for the selected text-classification problem must also be identified. Suggested sources include Kaggle and Hugging Face Datasets. The data must be appropriately preprocessed, including tokenisation using BERT's tokenizer and division into training and testing sets. Students then fine-tune a pre-trained BERT or BERT-style model using suitable tools such as the Hugging Face Transformers library and PyTorch. Possible model choices include BERT, RoBERTa and T5. The selected model should be appropriate for the specific problem, recognising that different language models may perform differently across tasks. Students are expected to implement a suitable training process using an appropriate optimiser and loss function. Model performance must be evaluated using relevant classification metrics, including accuracy, precision, recall and F1-score. The performance of the selected LLM should also be compared with a baseline model, such as Logistic Regression, Naive Bayes or a pre-trained BERT model. The analysis should explain the results and consider their relevance to the chosen problem. Two main submission components are required. The first is a code notebook, such as a Jupyter or Google Colab notebook, containing annotations explaining the purpose and operation of the relevant code so that another person can understand and reproduce the work. The second is a report of no more than three pages, including appropriate figures, tables and references. The report should cover the motivation and dataset, methodology, model training and evaluation, results and discussion, limitations, conclusion and possible future improvements. The assessment rubric places particular emphasis on coding quality and implementation, model architecture, analysis and interpretation, and report presentation. Strong work should demonstrate well-structured and reusable code, clear explanation of the model architecture and configuration, appropriate evaluation metrics and visualisations, meaningful comparison with relevant literature or baseline models, and critical evaluation of the model's success and possible improvements.

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Engineering and Environment Advanced Practice London Campus Consultancy Project 5,000 words

Group Consultancy Report – Engineering and Environment Advanced Practice

This assessment is a group consultancy project for the LD7119 Engineering and Environment Advanced Practice London Campus Consultancy Project module at Northumbria University. Students work in consultancy groups of four to five members and are assigned a live project in collaboration with an organisation and the university. The purpose of the assessment is to investigate an organisational issue or requirement and develop practical, evidence-based solutions that can support the organisation in implementing improvements. The main assessment consists of a 5,000-word Group Consultancy Report and a compulsory 10-minute client presentation. The report is expected to demonstrate a professional and commercially appropriate approach to consultancy work. It should provide a clear introduction to the project, establish the organisational context, and analyse the business requirements and needs of the client organisation. The report should then explain the research methodology used, including appropriate ethical considerations and the design of the practical work undertaken. A substantial part of the assessment focuses on research, discussion and findings. Students are expected to collect and analyse relevant evidence, present their findings clearly, and provide evidence of practical implementation and testing where applicable. The assessment therefore requires students to connect academic research and relevant theories with practical consultancy activities and organisational requirements. Evidence from diagnostic tools, feedback and other appropriate sources can be incorporated into the report to support the analysis and conclusions. The final part of the report focuses on recommendations and improvements. Students should develop practical and actionable recommendations that are relevant to the organisation and can contribute to the successful implementation of the proposed ideas. Recommendations should be supported by the research findings and should demonstrate an understanding of the organisation's requirements and potential implementation considerations. The assessment is worth 50% of the total marks available for the module, which is assessed on a pass/fail basis. The report has a 5,000-word limit, excluding the table of contents, page numbers and captions for figures and tables. The client presentation is compulsory, although it does not have marks directly allocated to it; its purpose is to help the client and supervisor understand the consultancy project. The assessment is evaluated across professional and commercial presentation and introduction, business and requirement analysis, research methodology including ethics and practical work design, research and findings including implementation and testing, and recommendations including improvements. Students are also required to acknowledge sources appropriately and complete the assessment declaration regarding their work and any use of generative AI. The assessment brief states that AI may assist with activities such as improving grammar, structure, organising ideas and providing suggestions, but the main content, analysis and conclusions must remain the student's own work.

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Business 850 words

HopeLink Community Support and Food Security Database

This assignment requires students to design, develop and analyse a relational database for HopeLink Community Support and Food Security, a fictitious non-profit organisation working across rural and urban communities to reduce poverty and hunger. HopeLink currently relies on spreadsheets, which have resulted in inefficiencies and reporting errors. The proposed database is intended to improve operational efficiency, transparency and evidence-based reporting while supporting the organisation's work towards United Nations Sustainable Development Goals (SDGs) 1 and 2: No Poverty and Zero Hunger. The database development project involves five main entities: Beneficiary, Donor, Product, Collection and Collection Detail. Students are required to create a data dictionary defining the tables, attributes and appropriate validation rules. They must then use SQL within Microsoft Access to create the required tables through SQL View, rather than using the Access front end. Primary keys and foreign keys must be identified and implemented as part of the database design. Students must also establish relationships between the database entities and enforce referential integrity to maintain accurate information for management reporting and decision-making. Appropriate validation rules, user-friendly error messages, drop-down lists and input masks should be implemented where required. The database must be populated with sufficient realistic data for testing, with minimum requirements of 10 Beneficiary records, 10 Donor records, 20 Product records, 10 Collection records and 20 Collection Detail records. A further component requires students to develop five useful SQL queries in Microsoft Access to support analysis and reporting for HopeLink. The queries must follow specific requirements, including one parameter query, at least three queries containing a WHERE clause, at least one aggregate query and at least two queries involving joins across more than two tables. Students must provide screenshots of the SQL code and resulting outputs, together with an English explanation and a short justification of how each query could support HopeLink's operations and decision-making. The final component is an 850-word maximum business insights report investigating the use of food banks in the UK and considering how initiatives could support SDG 1 and SDG 2. Students are expected to use reliable external data, present relevant graphs and analyse current trends in food-bank usage. This section requires Harvard referencing and should use reliable academic, industry and other appropriate sources. The report is based on SDG 1 and SDG 2 and is not directly linked to the database developed for the assignment. Overall, the assignment assesses students' ability to apply data-modelling techniques, database design principles, SQL and data analytics to support organisational operations and strategic decision-making. It combines practical database development in Microsoft Access with data analysis and a business-focused evaluation of food-bank trends and sustainability goals.

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Operating Systems and Networks 2,000 words

Security of Operating Systems and Networks

This individual assignment for the Operating Systems and Networks module focuses on the security of operating systems and computer networks within a business environment. The assessment requires students to produce a professional technical report of approximately 2,000 words, supported by appropriate images demonstrating practical steps and testing activities. The assignment assesses students' knowledge and understanding of operating system and network security threats, vulnerabilities, testing approaches and practical security solutions. It also addresses the ability to develop and write complex scripts to solve problems relating to operating systems and computer networks. The assignment is based on a business scenario involving Net-Tech, a small and medium-sized technology-services enterprise. The organisation is developing a web server to provide its website to customers and is concerned about the security of the proposed system. Key concerns include operating system attacks such as buffer overflow attacks, as well as network threats involving hackers and phishing. Students are required to investigate, design and experiment with the features and functions of a web server and assess the security landscape so that appropriate security requirements can be identified. As part of the practical work, students must create a prototype Net-Tech network test rig. This involves creating two users, including a superuser and a standard user, applying appropriate baseline security, installing relevant software and security tools, conducting security tests and writing scripts to automate repetitive tasks. Students must document assumptions and parameters used in their implementation and may also make recommendations for further security measures and database-driven website functionality. The report covers several stages of the security assessment. The introduction establishes the business scenario, assumptions, aims, objectives, deliverables, available skills and resources, constraints and project scope. The background research examines common vulnerabilities, security threats, risk models, testing approaches and attack or testing tools, together with relevant legal, organisational, ethical, social and professional considerations. The pre-engagement stage focuses on setting up a suitable Linux or Kali Linux testing environment and developing a justified test plan. The engagement stage requires practical investigation of operating-system and network security. Areas include user authentication, file and directory permissions, protection against stack overflow attacks, network weaknesses, operating-system discovery, firewall configuration, listening ports, network statistics, denial-of-service prevention and script-based automation. The final post-engagement section requires students to summarise their work, identify deductions and limitations, propose mitigation measures and recommendations, and provide a self-reflection. The assessment is aligned with learning outcomes relating to understanding network security threats and developing software or scripts to solve problems in operating systems and computer networks. Students are expected to use relevant sources, reference them appropriately and include a bibliography. Practical tools such as Wireshark are specifically identified in the assignment brief, and the work should demonstrate appropriate testing methodology and security practices.

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Business Simulation – Cyber Security 2,500 words

L7 Business Simulation – Cyber Security: Ethical Hacking and Vulnerability Assessment

This assessment is an individual cybersecurity portfolio based on a simulated business environment involving a fictional client, VulnTech Inc. Students take the role of a junior cybersecurity analyst and investigate vulnerabilities within a server deployed for the organisation’s legacy systems. The practical activities use the TryHackMe platform to simulate the web server environment and require students to apply ethical hacking techniques within an authorised scenario. The assessment focuses on the first three phases of ethical hacking: footprinting, scanning and enumeration, and gaining access. Students are required to gather information about the VulnTech server, including relevant details about its domain, IP range, network infrastructure, operating system, name servers, open ports and other technical information. They must provide evidence of the tools used, justify their selection, critically evaluate the methodology adopted and recommend appropriate mitigation strategies. The second task involves scanning and enumerating the server using information obtained during the footprinting stage. Students must identify potential vulnerabilities and provide evidence of the tools and methods used. The assessment requires students to critically evaluate their available options, explain the reasoning behind their decisions and recommend suitable strategies for mitigating identified risks. The third task requires students to gain access to the VulnTech server by exploiting one of the vulnerabilities identified during scanning and enumeration. Evidence of the tools and methodology must be provided, together with a critical evaluation of the approach, justification of decisions and recommendations for addressing the identified vulnerabilities. Students must also provide evidence of completing at least four different TryHackMe rooms or modules undertaken during the semester. The final component is a 1,500-word summary report reflecting on the key insights gained throughout the tasks. This report should consolidate the student's learning, discuss the main challenges and vulnerabilities identified, evaluate mitigation recommendations and critically examine the legal and ethical considerations associated with cybersecurity and ethical hacking practices. The assessment is designed to develop students' ability to analyse business and cybersecurity environments, identify organisational issues, evaluate alternative approaches, make informed management decisions and justify resource allocation. It also requires consideration of Environmental, Social and Governance (ESG) implications and the potential effect of cybersecurity practices on organisational perception and performance. The portfolio uses Harvard referencing, with appropriate academic and external sources cited throughout.

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Security of Connected Systems 4,500 words

CW: Security Evaluation

This assignment is an individual technical report focused on the security evaluation and strategic development of a rapidly expanding Internet of Things (IoT) systems developer based in Coventry. The client specialises in innovative IoT devices and systems for residential and commercial applications, particularly smart energy monitoring, and is planning to scale its operations and expand into new markets. The report is designed to provide practical and strategic guidance on how the organisation can achieve this growth while maintaining strong cybersecurity. The assignment requires students to act as an external IoT Security and Scaling Strategist and critically evaluate the organisation’s current and future security needs. The report covers four principal areas: organisational change strategy, secure design and development strategy, security audit strategy, and security recommendations. The organisational change section considers internal and external factors influencing growth, the development and implementation of an organisational strategy, monitoring against objectives, and approaches to leading and managing strategic change using relevant change management theories and models. The secure design and development section requires an overview and evaluation of possible secure design processes for IoT systems, including their strengths and weaknesses, followed by a recommendation for an appropriate process. The security audit section examines methods such as PTES and OWASP and develops a guide for conducting a security audit, including the testing methodology, rationale for each stage, and evaluation of different approaches. The security recommendations section requires a case study of an IoT security vulnerability, examination of where the security flaw was introduced, assessment of weaknesses in the secure design or audit process, consideration of the resulting security impact, and recommendations for improvement. The report should be written for a technical audience, particularly the client’s software development team, and should use appropriate structure, technical language, diagrams where useful, and APA referencing. The organisational strategy and strategic change proposal should be included in appendices and referenced within the main report. The assessment is worth 30 credits and has a maximum word count of 4,500 words, with the main sections weighted across organisational change, secure design, security auditing, security recommendations, and report structure.

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Time Series Modelling

Time Series Modelling Case Study – Oil Price Forecasting

This individual Time Series Modelling Case Study focuses on analysing and forecasting oil price data using established time-series techniques and an alternative modelling approach. The assessment requires students to work with daily oil price information covering the period from 2024 to 2026 and investigate the underlying patterns, stationarity and forecasting behaviour of the data. The first part of the assignment involves exploratory data analysis and time-series modelling using an ARMA-based approach. Students are required to create appropriate visualisations of the data, perform exploratory analysis and conduct tests for non-stationarity, including relevant stationarity diagnostics such as ADF, ACF and PACF analysis and differencing where required. An ARMA model must then be defined, with suitable model parameters identified using the AIC likelihood approach. The assessment requires the student to examine possible combinations of model parameters, assess model residuals, evaluate model performance using appropriate metrics such as RMSE, and produce forecasts extending 24 months into the future. Confidence intervals must also be included with the forecasts. The second part requires students to investigate an alternative modelling solution for the same oil-price time-series data. Possible approaches discussed in the assignment include models such as LSTM and Prophet. Students are expected to conduct a literature review relating to the selected alternative model, build and apply the model, tune relevant hyperparameters where appropriate, generate 24-month forecasts, create suitable visualisations and calculate appropriate evaluation metrics. The final part of the assessment requires a 6–8 page report describing the modelling process, forecasts, analysis and inferences. The report should explain the reasoning behind the analytical and modelling choices rather than simply presenting numerical results. Students are expected to critically discuss why particular approaches were selected, how the modelling decisions may have influenced the results, how forecasts compare with subsequently collected real data where available, and what improvements could be made in future work. The assessment evaluates both the technical implementation and the quality of the written analysis. The code component assesses completion of the modelling and forecasting tasks, stationarity testing, the alternative solution, code quality and annotation. The report component assesses discussion of the analysis and inferences, comparison of the modelling approaches, clarity of interpretation, report structure, appropriate use of figures and suitable academic references. The submission consists of a report in PDF or Word format, with the code submitted separately or through an appropriate repository.

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Artificial Intelligence / Natural Language Processing / Deep Learning

Applying Advanced AI Methods for Analysing Text Documents

This Advanced Artificial Intelligence coursework requires students to implement and evaluate natural language processing, natural language understanding and neural-network techniques for analysing text documents. The assessment uses a supplied social-media dataset containing more than 89,000 posts linked to 947 news headlines, with each post labelled as real or fake through a combination of headline ground truth and majority-vote annotation. Each social-media post is treated as an individual text document for classification and topic-analysis purposes. CMP_6059B_7059B_2025_26_CW1-pre… The first major task focuses on identifying fake text documents. Students must preprocess the text using appropriate NLP techniques, transform documents into numerical feature representations and experiment with alternative preprocessing approaches to determine which performs best. A separate unseen test set must be reserved to evaluate generalisation, while the remaining data is used for training both shallow and deep neural-network classifiers. Students are expected to explain and justify how the data is split. CMP_6059B_7059B_2025_26_CW1-pre… Students first design a multi-layer perceptron (MLP) capable of predicting whether documents are real or fake. The architecture must be justified in terms of input dimensions, number of layers, neuron counts, activation functions and outputs. A second deep-learning neural network must then be developed for the same classification problem, with justification of the chosen network structure, layer types, activations and other configuration decisions. CMP_6059B_7059B_2025_26_CW1-pre… For both networks, students train baseline models and select three hyperparameters considered most important for improving performance. These hyperparameters must be tuned systematically, with visuals prepared to show the experimentation process and resulting performance changes. Appropriate evaluation metrics are then used to compare the trained models. The strongest MLP and deep-learning models must be saved so that they can be loaded and tested on unseen data during the final demonstration without retraining. CMP_6059B_7059B_2025_26_CW1-pre… The second task focuses on topic discovery using NLP and NLU techniques. Students perform syntactic preprocessing such as tokenisation, stop-word removal and lemmatisation or stemming, and experiment with at least two different text-representation approaches. Suggested methods include Bag of Words, TF-IDF, LDA, word vectors and word embeddings. Students must interpret the discovered topics and explain how those topics relate to document content, linked news headlines and class labels. The best topic-discovery model or models must also be saved for live analysis during the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… The assessment is completed through a bench demonstration, supported by a maximum of seven PowerPoint slides. The slides should document the system design, model-improvement process, performance evaluation and discussion of results for both fake-document classification and topic discovery. Students also submit a ZIP file containing only their Python source files. The demonstration lasts up to 15 minutes, consisting of approximately 10 minutes for presentation and technical demonstration followed by 5 minutes for questions and transitions. CMP_6059B_7059B_2025_26_CW1-pre… The marking scheme allocates 45% to fake-document identification, including descriptive analysis, preprocessing, MLP design and deep-learning design; 35% to topic discovery, including preprocessing, model development and interpretation; and 20% to the structure, organisation, professionalism and Q&A quality of the demonstration. CMP_6059B_7059B_2025_26_CW1-pre… Important for the public Reference Library: the brief explicitly states that the use of Large Language Models or generative AI to produce any part of the submission is strictly prohibited, including code, data processing, testing, writing or PowerPoint content. Therefore, this entry should remain only a high-level public description of the assessment and should not be presented as material intended for direct student submission. CMP_6059B_7059B_2025_26_CW1-pre…

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Cyber Security / Internet of Things / Connected Systems 4,500 words

Security Evaluation for Connected Systems: IoT Strategy, Secure Design, Security Auditing and Organisational Change

This Security of Connected Systems assessment requires students to act as an external IoT Security and Scaling Strategist for a rapidly expanding Coventry-based technology startup specialising in connected devices and smart energy-monitoring systems. The client intends to scale its operations and enter new markets and therefore requires both organisational-change guidance and a comprehensive evaluation of its cybersecurity posture. 38f2fba9a933f212c9e46e8cc591cf2… The report combines business strategy with technical cybersecurity analysis. Students must review different secure design and development methodologies, compare their strengths and weaknesses, and recommend an appropriate approach for integrating security into the client's IoT software-development lifecycle. 38f2fba9a933f212c9e46e8cc591cf2… 38f2fba9a933f212c9e46e8cc591cf2… A major component of the assessment focuses on organisational change strategy. Students examine the internal and external factors driving organisational growth, develop a comprehensive strategy for achieving the client's business objectives, and explain how that strategy should be implemented and monitored. The report must also critically evaluate relevant change-management theories and models and address the complexities of leading and managing strategic transformation. This section carries 35% of the marks and is allocated approximately 2,000 words. 38f2fba9a933f212c9e46e8cc591cf2… The Security Audit Strategy section requires students to design a guide for auditing the client's connected system. Different approaches, including methodologies such as PTES and OWASP, should be compared and evaluated. Students must explain each stage of the selected testing methodology and justify why the individual steps are required. 38f2fba9a933f212c9e46e8cc591cf2… The final technical component applies these principles to an IoT vulnerability case study. Students research a real vulnerability in an IoT device, explain where the security flaw was introduced, identify weaknesses in the secure-design or audit process, assess the resulting security impact and propose appropriate corrective actions. 38f2fba9a933f212c9e46e8cc591cf2… Overall, the coursework integrates IoT cybersecurity, secure software development, security auditing, vulnerability analysis, organisational strategy and strategic change management. The marking scheme allocates 35% to organisational-change strategy, 20% each to secure design and development, security auditing and security recommendations, and 5% to report structure. 38f2fba9a933f212c9e46e8cc591cf2… Important: the brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted on a website. 38f2fba9a933f212c9e46e8cc591cf2… So use an original public summary like the one above, but do not upload the original brief itself.

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Data Science / Time Series Analysis / Machine Learning

Time Series Modelling Case Study: Oil Price Forecasting with ARMA and Alternative Models

This Time Series Modelling Case Study requires students to analyse real-world oil-price time-series data and develop forecasting models capable of predicting future values. The assessment combines traditional statistical time-series techniques with an alternative forecasting approach, requiring students to demonstrate practical modelling skills, critical research engagement and evidence-based interpretation of forecasting results. The coursework is completed individually and contributes 40% of the assessment. assing,,, (1) The assessment is divided into three main parts. Part 1 focuses on developing an ARMA-based forecasting model using daily oil-price data covering approximately 2024 to 2026. Students begin with exploratory data analysis and initial visualisation before testing whether the time series is stationary. Where necessary, appropriate transformations or differencing must be applied to obtain stationarity. Students then define an ARMA model and identify suitable p, d and q parameters using an AIC-based model-selection procedure across the parameter ranges specified in the brief. assing,,, (1) Model adequacy must be assessed using diagnostic analysis. Students inspect residuals, generate additional ACF plots, examine residual distributions and evaluate prediction performance using appropriate metrics such as RMSE. The selected model is then used to forecast oil prices 24 months into the future, with appropriate confidence intervals added to communicate forecast uncertainty. assing,,, (1) Part 2 requires students to research and implement an alternative forecasting approach. Suggested examples include LSTM and Prophet, although another appropriate model may be proposed. Students conduct a literature review supporting the alternative method, build and where relevant hyperparameter-tune the model, generate another 24-month forecast, visualise predictions and confidence intervals, and calculate suitable evaluation metrics. This component is intended to demonstrate independent research and the ability to propose an alternative solution rather than relying only on the conventional ARMA approach. assing,,, (1) Part 3 consists of a 6–8 page technical report explaining the modelling process, forecasting results and resulting inferences. The report should provide a critical analysis rather than simply reproducing numerical outputs. Students are expected to explain why results occurred, justify modelling choices, evaluate how those choices influenced performance, compare forecasts with subsequently observed real data where possible, and construct a coherent narrative supported by plots, images, summary statistics and academic literature. Future improvements to the modelling approach should also be critically discussed. assing,,, (1) Submission consists of both the report and working code. The code may be submitted directly or through an accessible Colab or GitHub repository and must reproduce all models, figures and numerical results presented in the report. The assessment allocates 60% of the marks to code and 40% to the report. Within the coding component, modelling and forecasting completion accounts for 40 marks and code quality and annotation for 20 marks. The report is assessed on analysis and inference, methodological justification, comparison of the two modelling approaches, presentation quality, figures and use of appropriate references. assing,,, (1) Key technical expectations include appropriate testing for stationarity, use of methods such as ADF, ACF, PACF and differencing, systematic model selection, forecasting, evaluation and clear comparison between the traditional ARMA model and the chosen alternative approach. Higher-quality work is expected to interpret what the forecasts mean, identify potential improvements and demonstrate sound technical communication rather than merely reporting model outputs. assing,,, (1) Important for the public Reference Library: the brief explicitly states that students must not use generative AI to write the report, and the rubric indicates that AI text-generation use may result in zero marks for the whole assignment. Therefore, the public entry should remain a high-level description of the assessment rather than material intended for direct submission. assing,,, (1)

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Computer Science / Parallel Computing

Parallel Merge Sort with Load Balancing

This technical research article investigates the performance limitations of conventional parallel merge sort and proposes a load-balanced alternative designed to improve processor utilisation in distributed-memory parallel computing systems. Traditional parallel merge sort progressively reduces the number of active processors during successive merge stages, causing many processors to remain idle and reducing the performance benefits of parallelisation. The proposed approach addresses this limitation by ensuring that all processors continue participating throughout the merging process. Parallel_Merge_Sort_with_Load_B… The paper first explains the conventional parallel merge-sort process, in which data are locally sorted before processors are paired for a series of merging operations. At every subsequent stage, the number of participating processors is halved until only one processor remains responsible for the final merged list. This results in poor processor utilisation and increasing workload concentration. Parallel_Merge_Sort_with_Load_B… The proposed load-balanced parallel merge sort distributes each partially sorted list across multiple processors so that every processor maintains approximately the same number of keys throughout execution. Processor groups use histograms and boundary values to determine how data should be redistributed during merging. Histogram-based partitioning reduces unnecessary data movement, while an index-swapping mechanism is introduced to avoid transferring large blocks of keys when logical processor reassignment can achieve the same result more efficiently. Parallel_Merge_Sort_with_Load_B… Parallel_Merge_Sort_with_Load_B… The algorithm was implemented in C using MPI and experimentally evaluated on a Cray T3E parallel computer and an eight-node PC cluster. Testing considered both uniform and Gaussian key distributions. Results show that performance improvements increase as processor count grows, although communication and histogram-management overhead can reduce benefits for small workloads. Parallel_Merge_Sort_with_Load_B… The proposed technique achieved a maximum merge-phase speedup of 9.6 on a 32-processor Cray T3E and 2.3 on an eight-node PC cluster when processing four million keys. The study concludes that distributing approximately equal workloads across processors can substantially improve parallel merge performance and may also be applicable to related parallel sorting algorithms. Parallel_Merge_Sort_with_Load_B… Overview word count: approximately 330 words. For your portal, I would not label this as university coursework unless you also have the actual assessment brief that uses this paper. This PDF itself only establishes a published academic paper and the authors’ Korea University affiliation.

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Probability and Statistics / Data Analysis / Python

Probability and Statistics Jigsaw Puzzle: Probability Calculations and Chi-Square Analysis in Python

This technical Probability and Statistics exercise presents a set of Python code fragments designed to demonstrate basic probability calculations and statistical hypothesis testing. The material is organised as a jigsaw-style collection of code segments, allowing the underlying logical sequence to be reconstructed from variable definitions, probability calculations, observed data, statistical testing and final interpretation. Jigsaw_Puzzle_3_Original_Struct… The first part models a two-bag probability problem. Bag 1 contains four white and two black items, while Bag 2 contains three white and five black items. The code calculates the probabilities of selecting a white or black item from each bag and then uses multiplication rules to determine the probability of drawing two white items, two black items, or one white and one black item across the two bags. Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… A second section demonstrates a chi-square test of independence using an observed frequency table containing grade outcomes for girls and boys across grades A, B, C and D. The data are stored in a NumPy array and analysed using SciPy's chi2_contingency function. The resulting chi-square statistic, p-value, degrees of freedom and expected frequencies are calculated and displayed. Jigsaw_Puzzle_3_Original_Struct… Jigsaw_Puzzle_3_Original_Struct… The final fragment applies a conventional significance threshold of α = 0.05. If the p-value is greater than 0.05, the code reports that the null hypothesis should not be rejected; otherwise, it concludes that a statistically significant difference exists. Jigsaw_Puzzle_3_Original_Struct… Overall, the document demonstrates core statistical-programming concepts including probability rules, contingency tables, chi-square analysis, expected frequencies, p-value interpretation and hypothesis testing using Python, NumPy and SciPy. Important: because this file does not identify a university, module, academic level, academic year, assignment brief, formal word count or referencing system, I would leave those fields as Not specified rather than guessing.

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3,000 words

ParkaLot Enterprise Parking Garage Management System: Software Analysis, Design and Prototype Development

This enterprise software engineering project requires students to analyse, design and prototype a centralised parking garage management system for ParkaLot Group, a UK operator of multi-storey parking facilities. The existing organisation relies heavily on manual processes, local spreadsheets and simple barrier-based vehicle counts, resulting in limited real-time occupancy information, inconsistent reservation arrangements, decentralised billing and reduced ability to optimise parking capacity and revenue. COMP1471 CW 2526 (1) The proposed system supports ParkaLot’s wider digital transformation by integrating customer registration, reservations, parking-space allocation, occupancy monitoring, contracts and billing. Customers can check availability and reserve parking through an online portal, while frequent and corporate users can establish recurring arrangements or block reservations. License-plate recognition and individual parking-space sensors enable automated access control and real-time occupancy tracking. COMP1471 CW 2526 (1) Additional functionality includes centralised electronic billing, dynamic pricing, promotional schemes and predictive decision-making for controlled overbooking. Historical usage data may be analysed to estimate no-shows, early departures and overstays, while operational dashboards support staffing, pricing and capacity-management decisions across the garage network. COMP1471 CW 2526 (1) Development is undertaken in two phases. The first uses structured analysis and design, requiring an Entity Relationship Diagram, Data Flow Diagram including a context diagram, and implementation of a prototype database. The second expands the solution using object-oriented analysis and UML, with emphasis on adaptable and reusable software design. COMP1471 CW 2526 (1) The final report covers the software-engineering **5 Ps—Problem, Process, Project, Product and People—**alongside ERD and DFD models, UML use cases, at least three sequence diagrams, a detailed class diagram and application of design patterns such as GRASP. Students must also submit prototype evidence, source code, personal reflection, peer assessment and work-contribution documentation. COMP1471 CW 2526 (1) Overall, the assessment integrates requirements engineering, structured modelling, object-oriented design, database development, design patterns, software project management, implementation and acceptance testing within a realistic enterprise-system case study. Overview word count: approximately 360 words. AI-use note: the brief permits Levels 1–4 of generative-AI use, including research and exploration at Level 4, but all final submitted text, code, diagrams and designs must be the students’ own work. Level 4 use requires disclosure, an appendix of prompts/outputs and reflective commentary. COMP1471 CW 2526 (1)

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Cyber Security / Penetration Testing

Web Application Penetration Testing and Security Vulnerability Assessment Portfolio

This postgraduate cyber security portfolio requires students to conduct a structured penetration test of a controlled web application and document the technical findings in a professional security-testing format. The assessment develops practical competence in identifying, validating and communicating security vulnerabilities while maintaining appropriate legal, ethical and professional boundaries. Assessment Brief CMP-L021 (PG) … Students begin by performing network and service enumeration, identifying open ports and the services running on the target host. They are expected to interpret the security implications of the findings and provide appropriate recommendations to a hypothetical client. The assessment then progresses into web-application vulnerability testing using tools such as a web browser, Burp Suite Community, Nmap and student-developed scripts. Assessment Brief CMP-L021 (PG) … A major part of the portfolio examines common web-security weaknesses including SQL Injection and Cross-Site Scripting. Students must demonstrate how they tested the application, capture relevant requests and responses, and explain the evidence supporting their conclusions. Additional tasks involve application and server reconnaissance, including identification of technologies, server versions, publicly exposed files and other information that may create security risks. Assessment Brief CMP-L021 (PG) … The higher-level reporting component requires students to document significant vulnerabilities using the conventions of a professional penetration-test report. This includes assigning CVSS scores, relating identified weaknesses to the OWASP Top 10 and NIST classifications, and supporting findings with appropriate technical evidence. Assessment Brief CMP-L021 (PG) … Students must also produce an executive summary for a non-technical audience, considering security, privacy, regulatory exposure and budget implications. A vulnerability table linking technical weaknesses with relevant regulatory concerns is also required. Overall, the assessment integrates technical penetration testing with risk communication, vulnerability classification, evidence collection and professional security reporting. Assessment Brief CMP-L021 (PG) … Overview word count: approximately 320 words. AI-use note: AI can be used in this assessment, but any use must be acknowledged and AI-generated outputs must be appropriately cited. Assessment Brief CMP-L021 (PG) …

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Cyber Security / Ethical Hacking / Penetration Testing 4,000 words

Ethical Hacking and Penetration Testing: Vulnerability Exploitation, Privilege Escalation and Mitigation

This Ethical Hacking and Penetration Testing coursework requires students to conduct a practical CTF-style penetration test against a set of authorised target machines and produce a professional technical report documenting the compromise of one selected target. The assessment evaluates practical exploitation skills alongside the ability to analyse risk, explain attack vectors and recommend effective security controls. 8598ba8d8fff5a38af8d427a4ce8f84… The practical element requires students to identify vulnerabilities in multiple target systems, exploit those weaknesses to gain low-privileged access and then perform privilege escalation to obtain root-level access. Successful completion of each stage produces flags, with separate user and root flags contributing directly to the practical marks. Brief descriptions of the attack vectors and payloads used must also be recorded. 8598ba8d8fff5a38af8d427a4ce8f84… The written report focuses in detail on one compromised machine. Students must explain the reconnaissance and vulnerability-identification process, including the techniques used to discover services, web content and potential attack surfaces. The marking criteria specifically recognise appropriate reconnaissance tools such as Nmap and FFUF and reward clear justification of methods and links between reconnaissance results and identified threats. 8598ba8d8fff5a38af8d427a4ce8f84… 8598ba8d8fff5a38af8d427a4ce8f84… A further component requires a formal risk rating for the discovered vulnerabilities. Students should use a recognised risk-classification approach, such as OWASP or SANS, justify the assigned severity and discuss relevant social, legal and ethical considerations. Higher-performing work is expected to connect those considerations directly to the specific vulnerabilities identified. 8598ba8d8fff5a38af8d427a4ce8f84… The exploit section should explain the technical cause of the vulnerability, describe the exploitation process and present relevant example payloads. Mitigation recommendations must then be linked directly to the vulnerabilities discovered, with clear explanations of where the weakness occurs and how it can be remediated. 8598ba8d8fff5a38af8d427a4ce8f84… Overall, the coursework integrates reconnaissance, vulnerability analysis, exploitation, privilege escalation, risk assessment, ethical and legal considerations, technical reporting and defensive mitigation within an authorised penetration-testing environment. Important: this brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted publicly. 8598ba8d8fff5a38af8d427a4ce8f84… So for your public Reference Library, use an original summary like the one above rather than uploading the assessment brief itself.

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Software Engineering / Enterprise Systems Development 3,000 words

Enterprise Software Engineering Development: ParkaLot Parking Management System Analysis, Design and Prototype

This Enterprise Software Engineering Development assessment is based on the ParkaLot Group, a fictional operator of multi-storey parking garages across major UK cities. The organisation currently relies on fragmented manual processes, basic barrier-based vehicle counting, local spreadsheets, on-site payment and inconsistent customer access arrangements. Students act as software engineering consultants and are required to analyse these operational weaknesses and design a centralised enterprise parking management system capable of supporting reservations, customer accounts, vehicle identification, billing, real-time occupancy monitoring, dynamic pricing and management reporting. COMP1471 CW 2526 The proposed ParkaLot platform is intended to integrate customers, parking spaces, reservations and billing across the garage network. Planned functionality includes online registration and reservation, recurring and corporate parking arrangements, licence-plate recognition, automated space allocation, sensor-based occupancy tracking, centralised monthly billing, electronic payments, promotional pricing and predictive overbooking. The system must also provide dashboards and historical reporting to assist management with capacity planning, staffing, pricing and operational decision-making. COMP1471 CW 2526 The coursework is completed in two main development phases. Phase 1 – Structured Analysis and Design requires an Entity Relationship Diagram representing the logical data model, a Data Flow Diagram including a Level 0 context diagram, and implementation of a prototype database. Phase 2 – Object-Oriented Development extends the system using object-oriented analysis, design principles and UML, with substantial business and user-interface functionality implemented using suitable OO technologies. COMP1471 CW 2526 The final report contains analysis of the 5 Ps of software engineering: Problem, Process, Project, Product and People. Students discuss the business problem and commercial risks, justify the development methodology followed, define resources and budget, document project artefacts and requirements, and identify the main stakeholders involved in the project. The technical design section includes the ERD, DFD, UML use-case model, at least three sequence diagrams, a detailed class diagram and discussion of design patterns. COMP1471 CW 2526 Students must additionally submit a functioning prototype that reflects the design and participate in acceptance testing and a live demonstration. Individual students are questioned on both theoretical and technical aspects of the submitted system. The assessment also evaluates group contribution, peer and self-assessment, personal reflection, research quality, communication and professional teamwork. COMP1471 CW 2526 The weighting places substantial emphasis on technical design and implementation: the UML design is worth 24 marks, the software prototype 15 marks, design patterns 6 marks, and acceptance testing/demonstration 25 marks. This makes the coursework strongly focused on demonstrating the relationship between requirements analysis, software architecture, UML modelling, implementation quality and working enterprise-system functionality. COMP1471 CW 2526

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Software Engineering / Object-Oriented Programming / Java Development

Java Group Exercise Booking and Management System for Furzefield Leisure Centre

This software-development coursework requires students to design and implement a Java-based booking and management system for Furzefield Leisure Centre (FLC). The proposed system manages member bookings for group exercise lessons delivered on Saturdays and Sundays. Exercise types can include Yoga, Zumba, Aquacise, Box Fit and Body Blitz, with each lesson accommodating a maximum of four members and carrying an exercise-specific price. 7COM1025 Coursework Explanation… The application must enable members to search the lesson timetable either by day or exercise type and make bookings subject to capacity constraints. Members may book multiple lessons, but duplicate bookings for the same lesson are prohibited. Existing bookings can be changed when capacity is available in the replacement lesson or cancelled before attendance. Successful changes must retain the existing booking ID while releasing the place held in the original lesson. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… After attending a lesson, members can provide a written review and a numerical rating from 1 to 5. Booking statuses should distinguish booked, changed, attended and cancelled reservations. The system must also generate management reports showing attendance and average lesson ratings and identify the exercise type producing the highest income, while listing income generated by each exercise category. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Students must design at least eight weekends of timetable data, equivalent to 48 lessons, covering at least four exercise types. The program should be self-contained and does not require an external database or security protocol. 7COM1025 Coursework Explanation… The software-engineering component requires a UML class diagram, Java implementation, JUnit testing, version-control evidence and consideration of design principles, design patterns and refactoring. Students submit a PDF report containing the UML diagram and repository evidence, a ZIP containing source code, tests and an executable JAR, and a screen-recording demonstrating the final system. 7COM1025 Coursework Explanation… 7COM1025 Coursework Explanation… Overall, the coursework assesses practical object-oriented software development through system modelling, implementation, validation and reflective documentation. The marking allocates 40 marks to system functionality, 30 to design and implementation, 10 to UML, 10 to JUnit testing, 5 to version control and 5 to report quality. 7COM1025 Coursework Explanation… Note: the uploaded material identifies the code 7COM1025 but does not state the formal module title. I would therefore keep Module name = Not specified rather than inventing one.

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Computer Networks / Network Security / Cloud and Software Defined Networking 3,500 words

Network Systems and Security: Ad Hoc, Cloud and Software Defined Networking Emulation

This Network Systems and Security coursework requires students to design, implement and critically evaluate a series of practical network-emulation environments covering wireless Ad Hoc networking, cloud services and Software Defined Networking (SDN). The project combines Python-based network configuration with practical connectivity testing, cloud deployment, controller-based networking and theoretical evaluation of contemporary network-security technologies. The first task involves designing an Ad Hoc wireless network representing an emergency-response scenario. Students configure at least three wireless stations using Mininet-WiFi, assign appropriate network parameters and demonstrate connectivity through ICMP communication. The task requires discussion of the design and implementation together with the Python script used to configure the emulated environment. 7COM1076+ref+def+CW+2025+26+ The second task focuses on cloud-service emulation. Students develop a simple static website, deploy it through Render.com and GitHub, and access the hosted service from a Mininet-emulated network. Required evidence includes the Python implementation, cloud configuration screenshots, commands used to provide internet connectivity, webpage access through an xterm environment and the associated HTML code. 7COM1076+ref+def+CW+2025+26+ The third practical component examines Software Defined Networking using the ONOS controller. Students construct an emulated topology containing hosts, servers and programmable switches, demonstrate complete ICMP connectivity and perform a TCP transmission lasting 600 seconds. Evidence must include the network-emulation script, ONOS graphical interface and connectivity results. 7COM1076+ref+def+CW+2025+26+ The final analytical section critically evaluates whether Software Defined Networking and Network Functions Virtualisation (NFV) complement one another and examines security algorithms used within cloud computing. Students compare two selected cryptographic approaches, evaluating their respective advantages and disadvantages. 7COM1076+ref+def+CW+2025+26+ Overall, the coursework integrates network modelling, wireless networking, cloud deployment, SDN control, Python scripting, connectivity testing and security analysis. The marking scheme gives substantial weight to system modelling, cloud and SDN implementation, ICMP/TCP functionality, technical analysis and overall report quality. 7COM1076+ref+def+CW+2025+26+

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Artificial Intelligence / Machine Vision / Computer Vision

Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application

This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.

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Operating Systems and Networks 2,000 words

Security of Operating Systems and Networks – Individual Assignment

This individual assignment focuses on the security of operating systems and computer networks. Students are required to produce a professional technical report of approximately 2,000 words demonstrating a deep and systematic understanding of operating-system security, networking functions, security threats, vulnerabilities and practical security testing. The assessment carries 50% and forms 100% of the module assessment. Students are expected to support their work with appropriate technical evidence, images showing practical steps and relevant sources. The assignment uses a business scenario involving Net-Tech, a small and medium-sized technology-services enterprise. The organisation is concerned about the security of its proposed system, including operating-system attacks such as buffer overflow and network threats such as hacking and phishing. Students are required to investigate, design and experiment with the features and functions of a web server used to serve the company's website, while assessing the security landscape and presenting findings that can support appropriate organisational security decisions. As part of the practical work, students must create a prototype Net-Tech network test rig. This includes creating two users, with one configured as a superuser and another as a standard user, applying appropriate baseline security measures, installing suitable software and identifying vulnerabilities, using appropriate tools to conduct security tests, and writing scripts to automate repetitive tasks. Students must document assumptions and parameters used within the project, including further security implementations and recommendations such as the use of a suitable database for a database-driven website. The report is structured around introduction, background research, pre-engagement, engagement and post-engagement activities. The introduction should explain the business scenario, assumptions, aims, objectives, deliverables, available skills and resources, constraints and project plan. The background research should address common vulnerabilities, threats, risk models, security-testing approaches, relevant attack and testing tools, legal and organisational requirements, and ethical, social, professional and sustainability considerations. The pre-engagement section covers the test-rig setup and testing strategy. The engagement section requires practical comparison and demonstration of operating-system and network security, including user authentication, file and directory permissions, protection against stack-overflow attacks, network weaknesses, operating-system discovery, firewalls, listening ports, network statistics, prevention of denial-of-service attacks and scripting for automation. The post-engagement section requires a summary of the work, deductions and limitations, mitigation measures and recommendations, and personal reflection. The assessment requires students to use relevant sources, provide a bibliography and demonstrate appropriate analysis, evaluation and reflection. The assignment is designed to assess learning outcomes relating to knowledge of network security threats and the development of complex software and scripts relevant to operating systems and computer networks.

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Team Research and Development

Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis

This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.

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Data Warehousing and Big Data

Data Warehousing and Big Data – Inventory Management Data Warehouse

This individual coursework for CS7079 Data Warehousing and Big Data requires students to design, implement and test a data warehouse based on a business case scenario and then export and migrate data to a Big Data platform for further processing. The assessment focuses on the inventory management business process of ABC Consumer Electronics Outlet Ltd, a multi-channel consumer electronics retailer operating from six stores around London and conducting online business across the UK and Europe. The company manages more than 10,000 products across approximately ten categories and more than 200 brands. The company already uses cloud-based and business applications including Vend, Linnworks and Xerox, which generate large volumes of transactional data. However, these datasets are stored separately by the individual applications, making it difficult for managers to produce integrated reports and perform analysis to support business decision-making. A business analyst has therefore recommended the development of a data warehouse capable of meeting the company's reporting and analytical requirements. For this coursework, the solution is focused specifically on the inventory management business process. The inventory management case involves three main business activities: sending purchase orders to suppliers when stock reaches minimum levels, receiving purchase orders and storing stock in appropriate locations, and controlling and maintaining stock levels including adding new products and adjusting existing stock. The required analysis includes daily stock levels for the previous month, weekly identification of products at minimum stock levels, analysis of stock levels by brand, product type and supplier, daily and weekly sent and received stock orders, and analysis of received stock orders by supplier and month. Students must analyse and design a dimensional data model that represents these business activities. This includes defining the grain of three central fact tables, identifying appropriate dimensions, defining dimension attributes and fact measures, and producing simple star schemas showing the relationships between fact tables and dimensions. The design must be justified according to the available data sources and the reporting and analysis requirements. The implementation stage requires students to create the relational database using Microsoft SQL Server Management Studio. Students must create the database, dimension tables and fact tables, establish appropriate primary-key and foreign-key constraints, and demonstrate implementation and testing through SQL commands and their results. Test data must then be populated into the data warehouse. The Big Data component requires migration of test data from the data warehouse to an Apache Hadoop platform using the Hortonworks Data Platform. Students must export the data warehouse data to an external data file, migrate the file into Apache HDFS, create a suitable data structure for loading the data into Hive, and demonstrate Apache Pig for manipulating the loaded data. Implementation and testing of the Big Data storage environment must also be demonstrated through commands and results. The coursework concludes with a personal reflective section in which students discuss what they have learned throughout the overall coursework and the challenges encountered during the process. The final submission is expected to be a well-written, structured and well-presented report combining data warehouse design, database implementation, testing, Big Data migration and reflective learning.

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Networking and Security Practice

Networking and Security Practice – Recorded Demonstration

This assessment for the MSc Cyber Security module Networking and Security Practice is a practical recorded-demonstration coursework designed to assess students' ability to configure, secure, troubleshoot and monitor a virtualised sandbox network. The assessment contributes 60% of the module mark and consists of four recorded demonstration videos, with each video limited to a maximum of five minutes. The completed videos are submitted through a Moodle quiz as video files or accessible links. The practical environment uses virtual machines running Ubuntu Server and Ubuntu Desktop on VirtualBox or UTM. Students deploy a four-machine architecture consisting of a Gateway, Webserver, Workstation and Zabbix-Server. The Gateway acts as the router, firewall and NAT gateway; the Webserver hosts an Nginx web server; the Workstation is used for administration and testing; and the Zabbix-Server provides network monitoring. The architecture requires appropriate network interfaces, IP addresses, routing and communication between the different internal networks. The coursework develops practical networking and system administration skills through several phases. Students install and configure operating systems, allocate virtual machine resources, configure static IP addresses, enable IP forwarding and NAT masquerading, and implement firewall rules using iptables. They also develop command-line proficiency using networking, DNS, security, remote-access and web tools including ping, traceroute, ss, nslookup, dig, nmap, Wireshark, netcat, SSH, SCP, rsync, curl and wget. Students must also configure secure remote access and deploy an Nginx web server. SSH must be hardened by disabling password authentication and root login, while key-based authentication is used for secure access. The Webserver must contain a customised landing page showing the student's name and student ID, which is accessed from the Workstation. The monitoring component requires installation and configuration of Zabbix, deployment of agents across the virtual machines, host monitoring, a customised dashboard containing at least five live-data widgets and configured alerts or triggers. Students must explain what their selected monitoring elements measure and why they are operationally useful. The traffic-analysis component uses Wireshark to capture and examine HTTP, ICMP and DNS traffic. Students apply appropriate filters, identify protocols using the Protocol Hierarchy and discuss the security implications of unencrypted HTTP traffic, including secure alternatives such as HTTPS and DNS over TLS. The security-evaluation component uses Nmap within the isolated virtualised sandbox to identify open ports and services, assess vulnerabilities and recommend mitigations. Students must also demonstrate iptables forwarding and NAT masquerading rules. The four videos cover Network Infrastructure, Network Monitoring, Traffic Analysis and Security Evaluation. The assessment is marked out of 100, with 35 marks allocated to Network Infrastructure, 25 to Network Monitoring, 20 to Traffic Analysis and 20 to Security Evaluation. Students are expected to provide clear voice narration, explain commands and outputs, demonstrate technical understanding and critically relate their work to network security. The assessment develops employability skills in Linux administration, remote system management, network troubleshooting, security hardening, packet analysis, port and service scanning, virtualisation and network monitoring. It also requires students to conduct security testing ethically within their own isolated virtualised environment and prohibits unauthorised scanning of university networks, public websites or other systems.

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Principles of Data Science 3,000 words

Principles of Data Science – Predictive Modelling and Data Analysis

This individual assessment for the Principles of Data Science module requires students to select, apply and critically evaluate data science methods, tools and techniques using one of three provided datasets and its associated scenario. The main assessment takes the form of a 3,000-word report in which students explore their chosen dataset, identify an appropriate predictive modelling approach, build and evaluate models, interpret the findings and critically reflect on the overall process and outcomes. The assessment addresses the principles and foundations of data science, statistical methods, data preparation, visualisation, predictive modelling, decision making and the critical evaluation of data science techniques and tools. Students begin by exploring the selected dataset to understand its structure, characteristics and limitations. Although the supplied datasets have already been cleaned, students may undertake additional data preparation or transformation where necessary. Any preprocessing decisions must be justified in relation to the requirements of the selected analytical methods. Feature selection should also be considered as part of preparing the data for model development. The assessment requires students to identify suitable forms of analysis for the selected scenario and justify their choice of methods. At least two different techniques must be used to develop models with predictive capacity for the response variable in the chosen dataset. The models must be trained and tested consistently, using the same training and test datasets so that their performance can be compared fairly. Where appropriate, students should also provide insight into feature importance and explain the contribution of relevant variables to predictive performance. Model performance must be evaluated using suitable metrics, followed by a clear description of the findings and recommendations appropriate for the intended audience. The report should document the complete analytical workflow, including data exploration, preprocessing, feature selection, model development, testing and evaluation. Students are expected to explain and justify the decisions made throughout the process rather than simply presenting code or model results. The assessment also requires students to demonstrate practical proficiency in data science tools and techniques. The brief expects the use of R for completing the assignment and requires evidence of important elements of the code, although the complete code does not need to be submitted. Data visualisation must be used to support the written discussion and communicate relevant findings effectively. The assessment is evaluated across theoretical knowledge and method selection, data exploration and processing, technical application and model evaluation, communication of findings, and overall presentation and referencing. The assessment therefore combines technical implementation with critical analysis, requiring students to explain why particular methods were selected, evaluate their effectiveness and consider the limitations and implications of the resulting findings. A separate second assessment component accompanies the written report. This component requires a presentation of the key findings from the written work using a maximum of five slides and a presentation duration of no more than seven minutes. It should summarise the dataset, methods, key findings and project outcomes while providing critical reflective commentary on lessons learned, factors affecting success and potential real-world applications.

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Programming for Data Science 2,500 words

Individual Coursework – Programming for Data Science

This Individual Coursework assignment for the Programming for Data Science module requires students to demonstrate practical programming skills and the ability to critically select and apply Python data science tools and libraries. The assessment is worth 20 credits and has a word count of 2,500 words plus 10%, excluding the reference list and output. Students are required to submit one clearly organised report covering two tasks and their individual subtasks. Task 1 focuses on designing, building, testing, explaining, adapting and critiquing a Python program. Students are required to implement a dice-based football match simulation for two players using at least two Python functions. The implementation should follow the logic of the physical dice game, demonstrate good Python coding style, and use functions designed with high cohesion and low coupling. The report must include the full Python code and carefully selected output from a sample match that demonstrates the progression of the game and changes in the score. Students must not implement a Python class or graphical visualisation. The second part of Task 1 requires students to explain how they designed their implementation, including assumptions, incremental development and testing. They must then modify their program to estimate at least two performance measures relevant to a football manager. The coursework asks students to investigate how these measures change when a restrictive shot clock is introduced and to model a realistic “Hail Mary” shot when the shot clock is close to expiring. Students must provide modified Python code, clearly identify the changes made, include selected output for checking the logic, and provide a robust conclusion based on comparison of the results. Task 2 focuses on critically assessing, selecting and applying Python data science libraries and algorithms. Students must describe an applied data science problem involving unstructured data such as image, audio, video or text. They must provide a specific example based on the context of a Coventry University fresher and explain how the problem could be solved manually. Students then select two Python libraries, justify their selection, compare their capabilities, apply both libraries to the chosen problem, and provide the relevant Python code and output. The final part of Task 2 requires a critical assessment of the selected Python libraries using the student's experience and additional sources. Factors may include coding difficulty, adaptability, level of control and quality of the resulting solution. Students must make a reasoned assessment of the suitability of the libraries for their chosen application and support their discussion with appropriate references. The assignment assesses three module learning outcomes: understanding essential programming concepts relevant to data science; designing, building, testing, explaining, adapting and critiquing small programs in a high-level programming language; and critically assessing, selecting and applying data science tools, libraries or algorithms for different applications and tasks. The submission must be provided as a single Microsoft Word or PDF report, organised by subtask, with each task starting on a new page. Python code, relevant output and plots must be included directly in the report. The brief requires APA referencing and states that sources should be cited in-text with a reference list for each task where relevant.

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Security of Emerging Connected Systems 2,000 words

Security Evaluation – Secure Design, Security Audit and IoT Security Recommendations

This individual coursework for the Security of Emerging Connected Systems module requires students to prepare a 2,000-word technical report evaluating secure design, security auditing and security recommendations for an organisation developing Internet of Things (IoT) devices and systems for home and workplace environments. The assessment is worth 10 credits and requires students to provide practical and evidence-based guidance to the client’s software development team. The report is assessed across secure design and development methodology, security audit methodology, security recommendations and overall report structure. The first major section addresses Secure Design and Development Methodology. The client wants to incorporate secure design principles into its software development workflow and therefore requires an overview of possible secure design processes relevant to IoT. Students must evaluate the strengths and weaknesses of different approaches and provide a justified recommendation for a process that would be appropriate for the client. The emphasis is on integrating security into the design and development of systems rather than treating security as a separate activity after development. The second section focuses on Security Audit Methodology. The client wants to complement its secure design process with a security audit of the final developed system. Students must provide a practical guide explaining how a security audit could be performed, including the overall testing methodology and the purpose of each stage. The brief identifies methodologies such as PTES and OWASP as examples. Students are also expected to discuss different approaches and evaluate their respective strengths and weaknesses. The third section concerns Security Recommendations and requires students to demonstrate the implications of strong secure design and auditing through an IoT security case study. Students must research a security vulnerability affecting an IoT device, explain the vulnerability and identify where the security flaw was introduced. The report must then examine which parts of the secure design and audit processes were not implemented correctly and evaluate the resulting impact on the security of the product. The report is intended for a technical audience, specifically the client's software development team. Students are expected to use an appropriate technical structure and language, support their arguments and analysis with references, and use APA referencing. The assessment also encourages appropriate diagrams to support the written content. The report structure component accounts for 10% of the assessment, while Secure Design and Development Methodology, Security Audit Methodology and Security Recommendations each account for 30%. The coursework assesses learning outcomes relating to defence-in-depth solutions for technical internet security vulnerabilities, secure private networks for IoT and BYOD, and current research and technological advances in network security. These outcomes connect the assignment to practical IoT security engineering, secure development, security auditing and emerging network-security practices.

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Network Security and Incident Report 1,500 words

Network Security and Incident Report – Megadodo Publications

This 1,500-word individual coursework for the Network Security and Incident Report module examines the network infrastructure and security challenges of Megadodo Publications, a company located near Warwick that has expanded into two buildings, Ursa Major Alpha and Ursa Minor Beta. Despite investment in new networking equipment, the organisation is experiencing poor network performance and repeated security incidents involving the leakage of sensitive information into the public domain. The situation is particularly urgent because the company is negotiating an important government contract. The student is placed in the role of a network security professional asked to investigate the existing environment and provide recommendations for improving its infrastructure and security. The case study provides a network topology, addressing scheme, equipment information and an existing security policy. The network connects departments across the two buildings and includes servers, management systems, sales, marketing, legal, finance, software development, product testing, administration, IT support, Wi-Fi and a rented floor occupied by a separate start-up company. The addressing scheme identifies separate subnets for several organisational functions, while the topology includes multiple switches, routers, servers and wireless access points. The report requires students to make reasonable assumptions where information is incomplete and document those assumptions at the beginning of the report. The first substantive task evaluates how the organisation's network performance could be improved. Recommendations should address the existing infrastructure and, where the design is changed, include an appropriate network diagram using tools such as Packet Tracer or other suitable applications. The second major area addresses amendments to the existing security policy. Students should recommend improvements to the current policy rather than create a completely new policy. The report must also discuss how appropriate security measures could be implemented across the network and its devices. Detailed device configurations are not required, although relevant examples or configuration snippets are encouraged. The final section requires a summary of the key findings and recommendations, together with a proposal for how the organisation could use its remaining IT support budget of approximately £8,000 and identify areas for future investment. The assessment is divided into introduction, assumptions and requirements, improving performance, policy amendments, security and devices, summary and budget, and references. The marking scheme allocates 50% of the module mark to this coursework, with the individual report submitted as a single DOC, DOCX or PDF document.

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1,500 words

Network Security Evaluation and Monitoring – Reconnaissance, Incident Response and APTs

This coursework assesses the research and analytical abilities required to design and evaluate an effective network security evaluation and monitoring solution. The scenario places the student in the role of a network security evaluation specialist responsible for helping a client design and build a monitoring solution for a complex client network. The client operates in the defence and security sector, works with government departments, multinational organisations and foreign agencies, and handles sensitive information. The network includes several server farms, gateway nodes, hundreds of client nodes, internal application services, externally accessible services and wireless access points. The organisation is considered vulnerable to threats such as sabotage and intellectual property theft. The coursework requires all questions to be answered in the given order within a single report. An abstract is not required, and students are expected to use technical terminology precisely. Relevant and clearly labelled illustrations are encouraged. Where assumptions are required about security software, hardware or services already deployed on the network, these assumptions must be clearly identified in a dedicated “Assumptions” section at the beginning of the report. Question 1 focuses on detecting network reconnaissance originating from inside the organisation. Students must explain how an insider could collect and use reconnaissance information for malicious purposes, identify the types of data that should be collected and the appropriate network locations for collection, and justify the selection of monitoring data. The question also requires recommendations for suitable tools and configurations to detect reconnaissance activity, together with strategies for dealing with the scale and high traffic volume of the client network. This section carries 30 marks and has a suggested length of 500 words. Question 2 focuses on incident response following a confirmed security incident. The scenario involves suspicious out-of-hours activity and an external flash drive connected to a workstation at gateway 10, a large number of files being opened on a file server at gateway 9, and significant traffic between the workstation and a database server at gateway 5. Students must determine which previously collected data would be relevant, explain the evidence expected from that data, and recommend additional network and endpoint data that should be collected. The proposed approach must be forensically sound so that evidence can potentially be used in court. This section carries 50 marks and has a suggested length of 700 words. Question 3 addresses Advanced Persistent Threats (APTs) and evaluates the effectiveness of the proposed monitoring solution. Students must recommend appropriate testing to determine whether the monitoring system operates according to its specifications and objectives, explain the types, timing and location of testing, and identify suitable qualifications, certifications, knowledge and tool experience for security testers. The section also requires discussion of APT behaviour and how the proposed monitoring mechanisms could detect or prevent such activity. This section carries 20 marks and has a suggested length of 300 words. Overall, the coursework develops skills in network security monitoring, reconnaissance detection, incident response, digital forensics, security testing and APT detection. It requires students to connect technical monitoring strategies with practical security, legal and operational considerations within a complex organisational network environment.

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Machine Learning and Big Data 2,000 words

Machine Learning for Big Data

This assignment for the Machine Learning and Big Data module requires students to produce a 2,000-word individual report demonstrating their understanding and practical application of machine learning techniques to big data. The assessment is worth 15 credits and is structured around five interconnected areas: data in big data, machine learning architecture, model deployment, model evaluation, and the machine learning lifecycle. The first part focuses on identifying and evaluating suitable datasets for a selected big data topic and determining whether the datasets are appropriate for the intended machine learning application. Students are expected to examine the characteristics of their data and apply appropriate pre-processing approaches, including consideration of attribute selection and data preparation. The second part addresses machine learning modelling architecture. Students must develop an appropriate architecture for their big data application and may compare alternative architectural approaches. The report should explain the selected machine learning techniques and demonstrate how they operate as part of the proposed system. Practical considerations such as performance, scalability, fault tolerance, technology usage and reliability should also be considered. The third part requires students to implement and deploy the proposed data and machine learning model. This includes testing, visualising and evaluating the resulting outcomes. The fourth part requires critical evaluation of the dataset selection, modelling design, implementation and application, including assessment of whether the selected machine learning techniques are appropriate for the intended purpose. The final part focuses on the complete project lifecycle. Students are expected to critically reflect on the work undertaken, identify what they have learned, evaluate the development process and explain how the machine learning application could be improved in a future implementation. The assessment develops five learning outcomes covering big data sources and applications, machine learning techniques, practical application of machine learning tools, critical evaluation of techniques and tools, and the ability to follow a complete big data analysis lifecycle. The marking criteria allocate 20% to each of these five areas. The assignment is submitted as an individual written report. The brief states that Microsoft Word should be used rather than PDF and requires students to acknowledge sources and any AI tools used in accordance with the stated AI policy.

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Programming for Data Science 4,000 words

Programming for Data Science – Individual Portfolio

This individual portfolio assessment for the Programming for Data Science module at Coventry University consists of four tasks designed to assess programming, debugging, data science, data visualisation, data protection and data ethics skills. The assessment carries 20 credits and has a total value of 4,000 words equivalent, excluding the reference list and output. Students are required to submit one clearly organised report containing all four tasks, with each task beginning on a new page. Python code, outputs and relevant plots must be included directly within the report. Task 1 focuses on analysing, critiquing and debugging Python code. Students are required to identify syntax errors, logical errors, style and readability issues in a supplied program, determine what the program is intended to calculate, and make appropriate corrections. Students must test the program using varying values, explain the changes made, and improve its overall readability and annotation so that an unfamiliar user can understand it. The task also requires students to investigate computational efficiency by measuring execution time for different input limits and identifying more efficient coding or logical approaches. Task 2 requires students to design, build and test a simple Python implementation of the Blackjack card game. The program should simulate a single-player game against a computer-controlled dealer, allow the player to choose between hitting and standing, automatically simulate the dealer's turn, and offer the option to play additional rounds. Complex rules such as splitting and betting are excluded, and students must not implement a Python class or graphical visualisation. The submission must include the Python code and the output from five games, with sufficient storytelling in the output to allow the code to be tested from the results shown. Task 3 assesses the student's ability to critically assess, select and apply data science tools. Students work with animal lifespan information from the AnAge database and use Python, including pandas and appropriate graphical libraries, to explore and communicate insights. The task involves summarising the number of animal species represented within each animal Class and producing plots of maximum longevity against adult weight for the four Classes with the most represented species. Students must discuss whether smaller or larger animals live longer, identify extreme outliers, compare trends between animal groups and consider implications for ageing research. Task 4 examines data protection and data ethics using the Cancer Genome Atlas (TCGA) as a case study. Students must explain how a potential data breach could occur, identify the personal and sensitive information that could be compromised, and discuss consequences for patient confidentiality, institutional reputation, participation in future research and possible legal or public relations responses. A second part considers a hypothetical UK database and requires discussion of GDPR and the UK Government Data Ethics Framework, including informed consent, anonymisation, transparency, ethical governance, privacy and public trust in biomedical research. The assessment assesses two module learning outcomes. MLO2 focuses on designing, building, testing, adapting and critiquing small programs in a high-level programming language and is assessed through Tasks 1 and 2. MLO3 focuses on critically assessing, selecting and applying data science tools, libraries or algorithms throughout the data science project lifecycle and is assessed through Tasks 3 and 4. The assignment requires APA referencing and asks students to provide in-text citations and reference lists where relevant. The brief also classifies the assessment as “Amber” for Generative AI: AI tools may be used for inspiration but not to generate answers or analyse datasets. Any permitted use must be clearly acknowledged, documented and cited using APA style.

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Ethical Hacking 2,500 words

Ethical Hacking – Professional Penetration Testing Report

This resit coursework for the Ethical Hacking module at Coventry University requires students to conduct a professional penetration testing examination of a small office environment represented by a number of virtual machines. The purpose of the assessment is to evaluate the security of the target environment, identify vulnerabilities, demonstrate appropriate exploitation techniques within the authorised assessment environment, and produce professional recommendations for improving the security of the systems. The assignment carries 15 credits and requires a report of approximately 2,000 words, with a permitted variation of ±10%. The report should follow a structured penetration-testing approach. The first section covers reconnaissance and target analysis, requiring students to investigate the target environment and identify its structure, services and potential attack surfaces. The marking criteria emphasise the use of appropriate tools to identify network structure and services and the identification of vulnerabilities during the scanning process. Students are expected to analyse the results rather than simply reproduce the output of scanning tools. The second section focuses on exploitation. Students must describe in detail the steps taken and the tools used to exploit relevant vulnerabilities identified during the assessment. The marking criteria distinguish between compromising the desktop and gaining access to the server, with higher achievement involving multiple relevant vulnerabilities and successful access through more than one vulnerability. The report should provide appropriate screenshots and sample sessions to support the findings. The third section addresses post-exploitation activities. Students are required to document and analyse activities carried out after gaining access to the target systems. Examples identified in the marking criteria include dumping password hashes and creating a persistent backdoor. For server assessment, the criteria also consider activities such as obtaining root access or establishing a persistent connection. The report should explain the significance of the activities rather than merely listing technical actions. The fourth section provides recommendations for securing the target machines. Recommendations must address all vulnerabilities identified during the assessment, not only vulnerabilities that were successfully exploited. Security issues should be discussed using an established risk-rating approach such as OWASP, and proposed countermeasures should be relevant to the specific vulnerabilities discovered. The report should also analyse how vulnerabilities relate to one another and fit within the wider security context. The final section presents the conclusions, including an evaluation of the penetration-testing work and alternative approaches that could have been taken. The overall learning outcomes require students to critically discuss the legal, technical and ethical scope of ethical hacking, evaluate penetration-testing methodologies and security assessment tools, analyse vulnerabilities, and professionally report penetration-test outcomes with suitable countermeasures.

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Software Development Project

Software Development Project – Group Presentation, Demo Video and Peer Assessment

This group-based assessment for the Software Development Project module at Coventry University requires a team of five students to design, develop and demonstrate a full-stack software product of their choice. The project must use technologies or platforms agreed by the team and requires students to contribute to different aspects of the software development process, including design, implementation and testing. The assessment is worth 20 credits and is submitted as a 15-minute MP4 presentation and demonstration video, with a permitted variation of plus or minus 10%. The main component is a group presentation and demo video. Students must evaluate the key Agile techniques applied during development and project management, supported by evidence of using appropriate software tools. The presentation should demonstrate how Agile practices were applied to the group project and should address techniques such as user stories with story-point estimation, story mapping, sprint planning, task boards, work breakdown and burndown charts. The assessment also requires evaluation of project risks and the social, professional, legal and ethical issues associated with the software project. A second major component is the live demonstration of the software features developed by the group. Students must demonstrate the most significant or innovative features implemented using their selected technologies and platforms. The demonstration should show how the software meets business requirements and provide a clear rationale for the development decisions. The marking criteria assess the quality, complexity and creativity of the software, including front-end, computation and back-end development, live data inputs and, where applicable, Create, Read, Update and Delete (CRUD) operations. The assessment also includes an individual peer-assessment component. Each student must rate every other team member's contribution using a whole-number score from 0 to 10, with 10 representing the highest contribution. The peer assessment accounts for 10% of the overall assessment, while the group presentation accounts for 60% and the software features demonstration accounts for 30%. The module learning outcomes focus on evaluating and applying appropriate software development approaches such as Agile, applying current technologies and platforms to meet business requirements, evaluating software solutions against quality metrics, and evaluating commercial risks alongside professional, social, legal and ethical considerations. The brief also places the assessment in the Amber AI category, meaning AI may be used to assist, but any AI tools used during the research process must be acknowledged and relevant AI-generated information must be cited and referenced using Coventry University APA style.

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Secure Design and Development 2,000 words

Secure Design and Development – PixelForge Nexus (UITS)

This assessment for Coventry University’s Secure Design and Development module requires students to design and develop a functional secure online system with the aid of a Large Language Model (LLM). The assignment is based on a practical scenario involving Creative SkillZ LLC and its proposed “PixelForge Nexus” system. The submission combines a functional prototype, an individual 2,000-word report, source code hosted in the Coventry University GitHub environment, and a video report demonstrating the completed prototype. The PixelForge Nexus prototype is intended to provide secure project management and basic asset and resource management for a game-development environment. Core functionality includes adding and removing projects, viewing active projects, assigning developers to projects, allowing developers to view their assigned projects, uploading project documents, and allowing authorised users to access documents associated with their projects. The system must implement privilege separation between Admin, Project Lead and Developer roles. Security is a central requirement of the assignment. Administrators are responsible for managing projects and user accounts, Project Leads can assign developers and upload project documents, while Developers can view assigned projects and associated documents. The system must include a robust login mechanism with secure password hashing and storage, with Multi-Factor Authentication recommended as an additional security measure. Proposed pages include Sign In/Register, a role-based User Dashboard, Account Settings and a Project Details page. The practical assessment evaluates four major areas: System Design, Security Testing and Analysis, System Development, and Formal Methods. System design requires consideration of secure design principles and their application to the development lifecycle. Security testing requires critical evaluation of security techniques, identification of issues and proposed mitigation measures. System development requires a functional prototype that follows the proposed design and considers legal and ethical requirements. Formal methods require a behavioural model and appropriate verification techniques to establish whether the system meets its specification. The individual report must document the methods and techniques used to develop the prototype and discuss the stages of the development lifecycle, including specification, design and development. It must also explain the deployment and testing approach, limitations of the prototype, possible improvements, security techniques and the formal model used. The submission must include links to the Coventry University GitHub repository and Microsoft OneDrive video, while the required appendix contains the LLM prompt history and other resources used with APA-style referencing.

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Network Systems and Administration 3,500 words

Network Systems and Administration – Linux System and Network Administration Portfolio

This assessment is an individual portfolio for the Network Systems and Administration unit. It consists of two quizzes and a report based on a case study involving the development, implementation, configuration, testing, maintenance, and evaluation of a network solution using an industry-standard network operating system. The report requires students to justify their design and implementation decisions, provide a detailed testing strategy, develop a maintenance and disaster recovery plan, and critically evaluate the completed solution. The case study concerns Piranha, a small organisation in which the business owner has been maintaining a server containing information relating to finance, management, production, and sales. The student is required to undertake the role of Network Systems Administrator and configure an appropriate Linux-based network environment. The practical work includes creating a new user account with root access, confirming root privileges, verifying access to company files, and configuring group-based access controls for finance, management, production, and sales directories. Students must then establish a client-server network by installing a Linux distribution on a separate virtual machine in VirtualBox. SSH connections are required to verify user access and the configured file permissions. The assessment also requires students to use Wireshark to observe network traffic during SSH sessions, report on packet types and encryption, identify potential security vulnerabilities, and recommend improvements such as SSH keys and unique passwords. A maintenance schedule and a brief disaster recovery or backup strategy must also be proposed. The final report must document the practical work with clear explanations and screenshots showing commands, user accounts, and results. The report should be between 1,500 and 3,500 words. The assessment also includes Linux Essentials and Networking Essentials final grades. The marking criteria cover system and network administration, the maintenance plan, reflection and report writing, screenshots, references, and Harvard referencing.

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Business Strategy / Digital Business and Innovation 3,125 words

Safaricom Business Hub: Strategic B2B Digital Payment Platform for East African SMEs

This strategic business case proposes the Safaricom Business Hub, an integrated B2B digital-payment platform designed to support small and medium-sized enterprises across East Africa. The proposed solution responds to Safaricom’s need for digital diversification as traditional voice revenues mature, while leveraging the growth of M-Pesa and the wider transition toward digital financial services. The platform combines invoicing, bulk payments, cash-flow analytics and integrations with accounting systems such as Xero, QuickBooks and SAP. The report begins by analysing Safaricom’s current strategic position, including subscriber growth, revenue performance, declining voice services, M-Pesa transaction growth and market saturation. It then assesses the wider East African B2B digital-payment opportunity, considering market size, digital adoption, the SME population, regulatory developments, smartphone penetration and operational inefficiencies associated with manual invoicing and reconciliation. Competitive analysis evaluates Safaricom against established banking solutions, international fintech providers and specialist payment gateways. The strategic case identifies several potential advantages associated with Safaricom’s existing M-Pesa infrastructure, agent network, brand position and payment-services capabilities. The proposed Business Hub addresses fragmented SME financial processes through automated invoicing, bulk payment processing, real-time cash-flow analytics and integration with existing accounting platforms. The business plan also provides a four-phase implementation roadmap, covering MVP development, pilot testing, Kenyan market launch, regional expansion and ecosystem development. Financial projections consider customer growth, revenue, EBITDA, customer acquisition cost, lifetime value, payback and return on investment, alongside sensitivity scenarios for different levels of market adoption. Finally, the report evaluates competitive, regulatory and market-adoption risks and links the proposed investment to Safaricom’s broader strategic diversification objectives. Overall, the work integrates strategic analysis, fintech innovation, market opportunity assessment, implementation planning, financial modelling and risk evaluation to present a commercial case for expanding Safaricom’s digital financial-services ecosystem. Overview word count: approximately 340 words. One useful note for the public Reference Library: I would upload the clean original assignment/business plan rather than the AI-detection or similarity-report versions if you have it, because these two PDFs contain checker-report pages in addition to the coursework itself.

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Machine Learning / Artificial Intelligence and Data Science 1,000 words

End-to-End Machine Learning Model Development, Tuning and Evaluation

This Level 7 Machine Learning and Intelligent Agents assessment requires students to develop and document an end-to-end machine-learning solution, covering the complete workflow from data preparation through model training, tuning, testing and evaluation. Students select an appropriate dataset or scenario, formulate a research question and determine whether the problem is most appropriately addressed through supervised learning, unsupervised learning or reinforcement learning. Suitable machine-learning techniques must then be implemented to create a model that can be systematically trained and tested. The assignment requires students to follow a structured machine-learning development process and document the complete development journey. The report should explain the selected scenario, data collection or dataset, Exploratory Data Analysis (EDA), rationale for selecting particular machine-learning methods, model training, fine-tuning and evaluation. Model performance must be assessed using appropriate established metrics, with relevant published research used to justify methodological decisions and support the interpretation of results. The technical implementation should demonstrate the ability to identify the performance of machine-learning algorithms, implement machine-learning approaches using one or more object-oriented programming languages, and determine which algorithms are most appropriate for a particular analytical brief. These requirements directly correspond to the module learning outcomes relating to machine-learning performance, implementation and algorithm selection. Students are advised to document their work within a Jupyter Notebook, combining Markdown explanations with executable code. The notebook may be submitted directly or converted to PDF. Alternatively, students may prepare the 1,000-word report in Microsoft Word, provided that the Python code is included within the submitted document. Assessment is divided into three principal areas: Introduction (20 marks), Machine Learning Process (40 marks), and Evaluation of Model Performance (40 marks). Higher-level work is expected to demonstrate strong understanding of machine-learning concepts, a functioning and thoroughly tested implementation, appropriate selection of algorithms and critical evaluation of the developed solution. Overall, the assessment integrates research-question formulation, data exploration, algorithm selection, programming, model optimisation and evidence-based evaluation within a reproducible machine-learning workflow. All academic sources must be presented using Harvard referencing.

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Machine Learning / Data Mining / Text Mining

Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data

This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.

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Big Data Analytics / Data Analytics 4,000 words

Big Data Analytics Using Python and Business Intelligence with Tableau

This individual Big Data Analytics assessment requires students to critically analyse data using programming languages, statistical techniques, data visualisation methods and business intelligence software. The assessment combines practical data analytics using Python with interactive business intelligence and dashboard development using Tableau, requiring evidence of technical implementation, research, critical appraisal and justification of the selected analytical approaches. The first section, worth 70%, is based on a dataset containing accidental drug-related deaths recorded in Connecticut between 2012 and 2024. The dataset contains 12,964 observations and 49 variables. Students are required to conduct exploratory data analysis using Python, develop three to four research questions, formulate null and alternative hypotheses, and apply appropriate statistical methods. The analytical process also requires an evaluation of alternative technologies and methodological approaches, supported by relevant research. Students must justify their selected methodology and present a clear workflow diagram. The solution-development stage involves data preprocessing, descriptive statistical analysis, visualisation, answering the research questions and conducting statistical significance testing to determine whether the null hypothesis should be accepted or rejected. Evidence of coding and a link to working code are also required. The final Python component requires evaluation of the findings, consideration of limitations and recommendations for future development using emerging technologies. The second section, worth 30%, focuses on Business Intelligence using Tableau and uses a historical Olympic Games dataset containing 271,116 rows and 15 columns. Students analyse relationships between medals and host cities, athlete age and medal type, season and medal counts, and sex and medal type. The section culminates in an interactive Tableau dashboard containing at least four interconnected sheets, where changes to relevant parameters are reflected across the dashboard. Overall, the assessment develops practical competence in Python-based analytics, statistical reasoning, research-question development, hypothesis testing, data visualisation and interactive business intelligence dashboard design. Overview word count: approximately 350 words. AI note: the brief permits AI for limited support such as grammar, structure, organisation of ideas and suggestions, but states that the main content, analysis and conclusions must remain the student's own work. AI use must also be declared.

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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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Computer Networks and Network Engineering 3,500 words

Wireless, Cloud and Software Defined Networking: Network Modelling, Emulation and Evaluation

This technical networking assignment focuses on the design, implementation, emulation and evaluation of wireless, cloud and Software Defined Networking environments. It combines practical network modelling with analytical discussion and requires students to demonstrate their understanding of modern networking architectures through Mininet-WiFi, Mininet, cloud deployment technologies and an ONOS Software Defined Networking controller. The first task involves creating an ad-hoc wireless network for an emergency-response scenario. A minimum of three wireless stations must be configured using specified parameters such as transmission range, antenna height, antenna gain, SSID and wireless capabilities. Students are required to explain the network design and implementation, provide the Python configuration script used with Mininet-WiFi, and demonstrate connectivity using ICMP communication between appropriate stations. The second task examines cloud-service emulation. Students must construct a Mininet topology containing a switch and two hosts and deploy a simple static website using Render.com. The task requires evidence of cloud configuration, GitHub repository integration, commands used to provide internet access to the emulated host, webpage access through Xterm, screenshots of the resulting webpage and the associated HTML code. The third task addresses Software Defined Networking (SDN). Students create an emulated environment involving three hosts and three servers, implement the required network topology and use the ONOS controller to provide control-plane programmability. Evidence must include the Python emulation script, ONOS GUI output, host-to-server connectivity testing and TCP transmission testing. The final analytical component requires students to critically evaluate the relationship between Software Defined Networking and Network Functions Virtualisation (NFV) and assess security algorithms used in cloud computing, including a comparison of the advantages and disadvantages of two selected algorithms. The coursework therefore integrates practical network configuration, connectivity testing, cloud deployment, programmable networking and academically referenced technical evaluation. Overview word count: approximately 335 words. Important: the brief explicitly states that AI-generated report or code content is prohibited, so this Reference Library description should be treated only as catalogue/metadata content, not as coursework material for submission.

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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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Computing / Artificial Intelligence / Digital Transformation / Management Consultancy 7,000 words

AI Readiness and Programme Adoption Strategy for the NewFutures: AI Programme at Northumbria University London

This postgraduate consultancy project focuses on developing an AI readiness, skills-development and programme-adoption strategy for students and recent alumni at Northumbria University London. The project supports the university’s participation in NewFutures: AI, a funded AI skills and career-readiness programme offering a four-week online course covering responsible AI foundations and specialist pathways in Marketing and Communications, Finance and Accounting, Business Operations and Logistics, Administration, and ICT and Technical Support. The central consultancy challenge is to understand the AI literacy, confidence, readiness and training needs of Northumbria University London students and alumni and translate that evidence into a practical implementation and outreach strategy. The client aims to reach approximately 12,000 students and recent alumni and support a target of 6,000 LMS registrations during the 2026–2027 programme period. The project requires primary and secondary research into AI readiness, demand for different AI-skilling pathways and barriers to participation such as awareness, time, perceived value and accessibility. The consultancy team is also expected to benchmark comparable initiatives and use research evidence to develop recommendations appropriate to different academic disciplines and student and alumni groups. The implementation component focuses on designing an evidence-based outreach and adoption campaign, including appropriate communication channels, messaging, timing, incentives, faculty engagement and stakeholder participation. Recommended channels may include email campaigns, newsletters, social media, student services, events, learning platforms and alumni communications. The project also requires an implementation timeline and indicative budget for the 2026–2027 programme period. The wider assessment develops professional consultancy capability through business and requirements analysis, research methodology, ethical research practice, practical implementation, testing and strategic recommendations. The project charter additionally establishes milestones for research design, data collection, analysis, report development, review and presentation, together with defined responsibilities for project management, data analysis, AI expertise and stakeholder communication. The individual component complements the consultancy work through critical reflection on personal contribution, skills development, decision-making, problem-solving, communication, collaboration, technical capability, innovation and continuous professional development.

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Data Science 1,000 words

Data Investigation Pipeline — Exploratory Analysis and Statistical Evaluation of a Chosen Dataset

This assessment simulates the opening stages of a real data investigation. Students choose their own research question and dataset, then build the full pipeline from raw data through preparation, exploration and statistical testing to visualisation — and, where the question supports it, simple modelling or forecasting. Either Python or R is acceptable; the statistical route through R typically expects an explicit hypothesis rather than a purely exploratory question. The work is structured around an established process methodology such as CRISP-DM, and the development journey is documented alongside the code rather than reported after the fact. The usual submission format is a single notebook combining markdown and code cells, so the written report and the analysis sit in one artefact, though a word-processed document containing the code is normally also accepted. The written element is short — around a thousand words — which makes selection the hardest part of the task. It must cover the scenario, the data collection, the exploratory analysis, the reasoning behind the choice of statistical tests, their results, and the visualisations. Students routinely spend that budget describing what they did and leave nothing for why. The mark distribution makes the priority explicit. Framing the problem and the data source carries the smallest share. Preparation and exploratory analysis, and evaluation of the results in context, carry the bulk in roughly equal measure. That final component is where most marks are lost: it asks for an honest assessment of accuracy, limitations and usefulness. A notebook that produces clean output and then claims more than the data supports scores below one that reports a modest result and explains precisely why it is modest. Established metrics should be used for the statistical tests, and published research cited where it informs the background or interprets the findings. Note that assessments of this type increasingly include a live demonstration in which the student explains their own project to verify authorship, so every line of the submitted work needs to be something the student can talk through unprompted. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how to scope a research question so the analysis fits the word limit, clarifying which statistical test suits which data type and why, reviewing whether a chosen visualisation communicates what it claims, showing how to write an honest limitations section, checking Harvard referencing, and reviewing a student's own draft notebook against the published marking criteria.

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Database Design and Implementation for KAP Speciality Chocolates Ltd

Design and implement a relational database system for KAP Speciality Chocolates Ltd based on the given case study. The assignment requires creating an Extended ER Diagram, developing SQL database structures with appropriate constraints, writing SQL queries for business requirements, populating the database with sample data, and demonstrating normalisation from Unnormalised Form (UNF) to Third Normal Form (3NF). Expected Deliverables: One PDF report containing: Extended ER Diagram with entities, attributes, relationships, keys and constraints SQL DDL statements for database implementation SQL DML queries with testing evidence/screenshots Normalisation process from UNF to 3NF

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Database Design and Implementation for KAP Speciality Chocolates Ltd