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Data Mining / Data Science
Customer Service Escalation Risk Analytics Using Data Mining and Machine Learning
This advanced Data Mining assessment applies the Knowledge Discovery in Databases (KDD) process to a real-world customer-service analytics scenario. Acting as a Data Scientist, students analyse a historical Customer Service Escalation Risk dataset to help an organisation reduce serious and legal customer escalations by identifying early signs of dissatisfaction, service bottlenecks and operational risk. The findings are intended to support business decisions relating to staffing, employee training, customer-journey improvement and escalation prevention. The dataset contains information covering customer demographics, account characteristics, communication channels, issue categories, operational measures such as wait times, transfers and SLA breaches, behavioural indicators including sentiment and response delays, and commercial variables such as monthly fees and contract value. The target variable contains four escalation outcomes: No escalation, Minor escalation, Serious escalation and Legal escalation. The first stage requires data exploration, visualisation and summary, including examination of variable distributions, dataset structure, descriptive characteristics and potential data-quality issues. Students then perform appropriate data cleaning, transformation, feature engineering and preprocessing. Particular attention must be given to variables that could introduce prediction leakage because of their meaning, timing or reliability. The supervised-learning component requires development and tuning of predictive models using suitable techniques such as k-nearest neighbours, Decision Trees, Support Vector Machines, ensembles or neural networks. Models must be evaluated using appropriate multiclass metrics and compared systematically, with interpretation of influential features and model behaviour. The assessment also requires unsupervised learning. After removing the escalation target, students apply and compare clustering approaches such as K-Means and hierarchical clustering. Appropriate preprocessing, encoding, normalisation or dimensionality reduction may be used, with visualisations such as PCA, t-SNE or scatterplots used to explore cluster structure and its relationship with escalation behaviour. Overall, the project assesses the student's ability to independently design a coherent KDD workflow, justify analytical decisions, compare alternative modelling approaches and communicate actionable findings to both technical and executive audiences. Overview word count: approximately 350 words. AI-use note: the brief permits AI tools only to assist with small, specific code snippets. Any AI-generated code must be clearly acknowledged and cited, while the submitted coding, analysis, interpretation and decision-making must remain the student's own work.
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Cyber Security / Cloud Management
2,500 words
Cyber Security and Cloud Defence Strategy for ShieldSafe Analytics
This Level 7 Cyber Security for Business and Cloud Management portfolio examines the security challenges faced by ShieldSafe Analytics Ltd., a multinational health analytics organisation specialising in AI-enabled diagnostics and telehealth. The organisation processes high volumes of sensitive patient information, including biometric and genomic data, across hybrid-cloud environments and IoT-enabled healthcare infrastructure. Following a suspected data-exfiltration incident involving anomalous traffic from a diagnostic platform connected to third-party cloud APIs, students are required to evaluate the organisation's information environment and develop appropriate cyber-security and cloud-defence strategies. The first task focuses on information environments and the weaponisation of information. Students identify critical elements of ShieldSafe's information environment, evaluate vulnerabilities associated with the data-exfiltration incident and examine how patient data or analytical systems could be manipulated by malicious actors. Relevant real-world healthcare cyber incidents should be used to support the analysis. The second task examines offensive and defensive Information Operations. Students analyse techniques used by nation-state actors and cybercriminal organisations, including healthcare ransomware incidents such as WannaCry, and compare offensive and defensive approaches. The analysis considers how ShieldSafe can balance these approaches while protecting sensitive data and preserving trust in AI-enabled diagnostic systems. The third task applies Information Operations within legal and ethical boundaries and requires development of a secure cloud migration strategy for ShieldSafe's legacy Electronic Health Record system. The supporting student guide specifically permits students to demonstrate an implementation using Amazon AWS, including IAM users and roles, VPC configuration, security groups, web servers, EC2 instances and AWS migration services. The final task requires a comprehensive cyber-defence strategy, including implementation of Zero Trust Architecture across cloud platforms and analysis of vulnerabilities affecting cyber-physical healthcare systems such as wearable medical devices and diagnostic equipment. Students must propose controls against both remote and local attacks. Overall, the portfolio integrates information operations, healthcare cybersecurity, hybrid-cloud protection, secure migration, Zero Trust, cyber-physical security and strategic cyber defence. The work is produced as a portfolio report using PebblePad and must use Harvard referencing throughout, with appropriate citation of academic sources, images, definitions and external arguments.
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Machine Learning and Deep Learning
2,000 words
Development and Evaluation of Deep Learning Models for Healthcare Classification
This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.
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Business Intelligence / Data Analytics / Project Management
Business Intelligence and Data Analytics for Project Progress Evaluation
This assessment for the Data Analytics and Project Management for Business Intelligence module requires students to critically evaluate the progress of a live-style project through the application of business intelligence and data analytics techniques. Working as a group of Project Analysts, students select either a Convocation project or Concert project associated with Northumbria University London and prepare a professional presentation assessing its progress, stakeholders, deliverables and performance. The assessment begins with development of a clear project problem or opportunity statement, followed by identification and analysis of the key stakeholders and deliverables associated with the selected project. Students then use Business Intelligence and Data Analytics techniques to examine project progress and communicate findings through dashboards. The assessment permits the use of Microsoft Excel or other suitable software for dashboard development. A central part of the task is the creation and critical evaluation of a project dashboard. Students are expected to use realistic assumed or projected data where necessary, as the assessment is designed to test the ability to design, interpret and critically evaluate dashboards rather than the accuracy of real project data. The data should therefore be internally consistent, relevant to the selected project and capable of generating meaningful management insights. Students must also critically justify their chosen BI and analytics tools, explaining how the dashboard supports project monitoring and informed decision-making. The assessment concludes with recommendations for the successful implementation and use of a Business Intelligence or Data Analytics solution within the selected project. Academic references and relevant examples should be used to support the analysis. The marking criteria place the greatest emphasis on application of BI tools and techniques (30%), followed by stakeholders and deliverables (20%) and justification of BI tools (20%). Project and opportunity analysis, conclusion and recommendations, and presentation and referencing each contribute a further 10%. Higher-performing work is expected to demonstrate critical evaluation, meaningful dashboard insights, strong theoretical or industry justification and professionally presented recommendations. If the file you want to upload is the resubmission report instead Use the same University, Subject and Module Name, but change these fields: Title: Business Intelligence and Project Analytics: Individual Critical Analysis of Project Performance Assignment type: MS Technical and Scientific Writing Word count: 1,500–2,000 words Key topics: Business Intelligence, Data Analytics, Project Management, Stakeholder Analysis, BI Tools, Dashboard Evaluation, Project Opportunity Analysis, Project Deliverables, Critical Analysis, Project Recommendations The resubmission is an individual written report in which the student selects only one area from the original project—Project & Opportunity Analysis, Stakeholders & Deliverables, Application of BI Tools & Techniques, Justification of BI Tools, or Conclusion & Recommendations—and develops it in depth. It should use the same Convocation or Concert project context while demonstrating independent critical analysis, reflection and application of theory.
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Cyber Security / Penetration Testing
2,400 words
Grey-Box Penetration Testing: Vulnerability Assessment, Exploitation and Mitigation
This technical cyber-security project presents an authorised grey-box penetration test conducted within a controlled virtual laboratory environment. The objective is to assess the security posture of a deliberately vulnerable target system, identify weaknesses in exposed network services, demonstrate how those weaknesses could be exploited, evaluate their security and organisational impact, and recommend appropriate mitigation measures. The assessment follows a practical penetration-testing workflow supported by technical evidence, screenshots, activity records and academic research. The project begins with laboratory configuration, network discovery, service enumeration and vulnerability analysis. Tools including Kali Linux, Metasploitable, VMware, Nmap, Netcat and Metasploit are used across the testing lifecycle. Identified services are mapped to known vulnerabilities before controlled exploitation is undertaken and the resulting access is documented. The activity log records the progression from environment setup and network scanning through vulnerability identification, exploitation, evidence collection and final reporting. Five principal attack vectors are examined. These include the vsftpd 2.3.4 FTP backdoor, Samba username-map-script exploitation, an UnrealIRCd backdoor, insecure Java Remote Method Invocation and a misconfigured DistCC service. The practical demonstrations show how vulnerable or incorrectly configured services can permit unauthorised command execution and, in several cases, privileged shell access. For each vulnerability, the report explains the weakness, exploitation process, observed result, security impact and proposed mitigation. Recommended controls include patching or upgrading obsolete services, disabling unnecessary services, implementing firewall restrictions, strengthening authentication and input validation, restricting access to authorised systems, applying least privilege and monitoring suspicious activity. The project also incorporates group management and reflective practice. Team members perform specialised roles covering laboratory configuration, reconnaissance, vulnerability analysis, exploitation and documentation. Individual reflection considers technical performance, teamwork, evidence management and future skills development, demonstrating how structured collaboration contributes to an effective penetration-testing engagement. Important: unlike the earlier assignment briefs, these uploads appear to be completed student/project materials rather than the official 7COM1068 assessment brief. Therefore I would not invent the university, academic level or academic year. If you upload the actual 7COM1068 assignment guideline, I can fill those fields exactly.
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Machine Learning / Data Science
Linear Regression and Stability of the Moore–Penrose Pseudoinverse Using Python
This machine learning practical and assessment activity develops an understanding of linear regression, Ordinary Least Squares and the Moore–Penrose pseudoinverse using Python. The work progresses from generating synthetic regression datasets to implementing regression algorithms manually, applying established machine learning libraries, analysing real datasets and evaluating the stability of estimated regression coefficients. The laboratory component begins with the generation of synthetic linear regression data using NumPy, including explanatory variables, random noise and an outcome variable. Students then implement simple linear regression without relying on machine learning libraries, using the least-squares solution to estimate the intercept and slope. The resulting observations and fitted regression line are visualised using Matplotlib. The work is subsequently extended to multiple linear regression, where several independent variables are used and coefficients are first calculated manually before the same problem is solved using Scikit-learn. The laboratory also introduces application of regression to the Scikit-learn Diabetes dataset, including feature and target standardisation, model fitting, prediction, correlation analysis and interpretation of regression coefficients. It also highlights the importance of residual analysis when assessing whether a linear model is appropriate. The associated weekly challenge focuses on the stability of linear regression solutions estimated using the Moore–Penrose pseudoinverse. Using a house-price dataset containing variables such as property size, number of bedrooms, distance from the city centre and property age, students construct the design matrix, standardise features and the response variable, and calculate regression coefficients using the pseudoinverse. Students then investigate model robustness by repeatedly fitting the regression model to random subsamples of different sizes and analysing the mean and standard deviation of each coefficient. Tables, boxplots or error-bar visualisations can be used to compare coefficient variability. The final discussion considers which variables are most influential, which coefficients are most stable, how sample size affects stability and whether coefficient interpretation remains reliable across different samples. The final work is submitted as a single PDF exported from Jupyter Notebook or Google Colab, combining documented Python code, experimental results, plots and written interpretation in a professionally organised notebook. Overview word count: approximately 360 wor
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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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Cyber Security / Digital Forensics
3,500 words
Digital Forensics Portfolio: Disk Image, Memory and Windows Registry Investigation
This Level 7 Digital Forensics portfolio requires students to conduct a structured forensic investigation across disk, memory and Windows Registry evidence. The assessment develops practical investigative skills alongside professional forensic reporting and requires students to preserve evidence integrity, document methodology, interpret technical artefacts and communicate findings clearly. The portfolio is equivalent to 3,500 words and forms 60% of the module assessment. The first part involves analysing a seized USB forensic image in the context of a suspected insider involved in video piracy and potentially more serious criminal activity. Students must follow ACPO digital forensic best practice, verify image integrity before and after examination, maintain a clear chain of custody, identify significant device properties and artefacts, and justify conclusions using evidence. Tools such as FTK Imager and Autopsy may be used, alongside other appropriate forensic utilities. The scenario also requires examination of an encrypted VeraCrypt container discovered within the evidence. The second part focuses on memory forensics using a Windows memory dump. Students are expected to reconstruct process execution timelines, examine suspicious processes including PowerShell, Notepad and AtomicService, identify process owners and SIDs, extract relevant memory artefacts and produce an executive summary suitable for a non-technical audience. The third part requires an extensive Windows Registry and system artefact investigation. Students examine operating-system information, users, network configuration, login activity, suspicious files, executable and DLL creation, BAM records, Prefetch artefacts, scheduled tasks, persistence mechanisms and evidence of potentially malicious activity. Findings must be supported with screenshots, extracted artefacts or other appropriate evidence. The assignment must use the university's official portfolio template and be submitted as a PDF. The template organises the work into forensic image analysis, memory investigation and Windows Registry investigation sections. For a public Reference Library entry, this title is better than simply “Digital Forensics Coursework” because it clearly communicates the three major technical components of the work.
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Business Intelligence / Data Analytics / Project Management
Business Intelligence and Data Analytics Dashboard for Project Progress Evaluation
This group assessment requires students to act as Project Analysts and critically evaluate the progress of a university project using business intelligence, data analytics and project management techniques. The scenario is based on Northumbria University London planning two major events: a Convocation and an associated Concert. Students must select one of these projects and assess its progress through a professionally structured presentation. The assessment requires students to begin by defining the main problem or opportunity associated with the selected project. They must then identify and analyse the project's key stakeholders and deliverables. A major component of the work involves evaluating project progress using suitable business intelligence and data analytics techniques and developing an appropriate dashboard, which may be created using Microsoft Excel or other suitable software. Students must not only present the dashboard but also critically analyse and justify its design, selected metrics, analytical methods and usefulness for project monitoring and decision-making. Because the projects are treated as already being in progress, students are permitted to create assumed or projected data to demonstrate their dashboards. The assessment does not primarily evaluate the accuracy of real project data; instead, it assesses students' ability to design, present and critically evaluate meaningful dashboards. Any assumed data should therefore remain realistic, internally consistent and relevant to the selected project. The presentation should conclude with evidence-based recommendations for the successful implementation and use of the proposed business intelligence or data analytics solution. Academic references and relevant examples must support the presentation, and a single reference list must be included. Assessment weighting places particular emphasis on application of BI tools and techniques (30%), followed by stakeholders and deliverables (20%) and justification of BI tools (20%). Project and opportunity analysis, conclusions and recommendations, and presentation/referencing are each worth 10%. The final assessment is a 15-minute group presentation followed by a 5-minute question-and-answer session. Every group member must contribute to the presentation. The PowerPoint submission is made electronically through Turnitin. Do not select Harvard automatically for this one. Unlike the previous Roehampton brief, this Northumbria brief requires academic references and a reference list but does not state a specific referencing style in the uploaded document.
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Global Supply Chain Management / Operations Management
2,494 words
Global Supply Chain and Operations Performance: Company Case Analysis
This Level 7 individual assignment requires students to produce a 2,500-word company case analysis examining an important operations performance issue within a real organisation. Students may select an organisation of any size, type, industry or country and must concentrate their analysis on one operations performance objective: cost, dependability, flexibility, speed or quality. The report requires students to identify the current challenges faced by the chosen organisation in relation to the selected performance objective and critically examine those challenges using relevant operations and supply chain management theories, concepts and analytical tools. Rather than providing only a descriptive account of organisational activities, students are expected to apply appropriate theoretical frameworks to the case and evaluate how these concepts could contribute to improved organisational and supply chain performance. A significant component of the assignment involves critically discussing appropriate best practices for performance improvement. These may include digital technologies, sustainability practices or other relevant operational and supply chain approaches. Students should connect these practices directly to findings from their chosen company case and assess their practical relevance, opportunities and limitations. Academic literature and appropriate real-world evidence should be used throughout to substantiate the analysis and recommendations. The assignment evaluates students' ability to analyse supply chain and operations problems, apply academic theory to organisational practice, and develop evidence-based conclusions and practical recommendations. The marking rubric places substantial emphasis on analysis and discussion (30%), application of theory (30%), and conclusions and recommendations (30%), with the remaining 10% allocated to presentation, logical structure, English expression and correct referencing. The report must use Harvard referencing, include a contents page, and follow the specified academic formatting requirements, including Arial size 12, 1.5 line spacing and A4 pages with 2.54 cm margins.
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Digital Marketing and Analytics
2,974 words
Digital Marketing Analytics Consultancy Report — Accessibility, Social Listening and Web Analytics for an E-Commerce Storefron
This assessment places the student in a consultancy role advising a global brand's e-commerce storefront on its digital marketing strategy. It is written as a business report rather than an academic essay, in the third person, with appendices used only for supporting material that the main text explicitly refers the reader to. The work is built from four analytical layers, and the mark weighting tells students where the effort belongs. The first is an accessibility and user experience evaluation of the client site benchmarked against two self-selected industry competitors — the selection itself must be justified, and the analysis must cover both technical and content dimensions using recognised evaluation tools rather than impressionistic browsing. The second layer, carrying the smallest weight, is social listening: which networks the brand and its competitors are active on, what engagement looks like, which content types perform, and who the influential voices are. This is paired with qualitative sentiment or content analysis of actual social comments, on the premise that quantitative engagement metrics describe reach but not attitude. The third and heaviest layer is quantitative analysis using a web analytics platform, typically via a demonstration account that provides real traffic data. Students query the data themselves and extract performance insights. Comparison across time periods is what separates competent work from strong work here: a single-period snapshot describes, while period-over-period comparison explains. The fourth layer, weighted equally with the analytics, asks the student to synthesise everything into strategic recommendations for the coming year, covering areas such as customer segments, user behaviour, landing and exit page performance, search ranking positions, advertising budget allocation, marketing channels and e-commerce performance. This section is where most marks are lost. Recommendations that do not trace back to a specific finding from the preceding analysis read as generic digital marketing advice, and rubrics at this level penalise exactly that. Presentation requirements are prescriptive — specified font, size, line spacing and justified margins — and the report is expected to be concise despite the breadth of analysis, which makes ruthless selection of evidence part of the task. A draft submission point for similarity checking is usually provided separately from the marked final submission. Assessments of this type commonly require a signed declaration itemising any AI tool use, with an explicit confirmation that AI was not used to generate sentences, paragraphs or sections. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how accessibility evaluation tools are used and what their output actually shows, clarifying the difference between reporting analytics figures and interpreting them, showing how a recommendation should be traced to a specific finding, advising on report structure and appendix discipline, checking APA consistency, and reviewing a student's own draft against the published rubric.
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Marketing
4,000 words
Strategic Recovery Marketing Report — Diagnosing Underperformance and Building a Sustainable Turnaround Plan
This is a case-based marketing assessment in which the student selects a real business that is currently underperforming or failing, diagnoses the causes, and builds a full marketing recovery strategy for it. The company choice is the student's own but requires module team approval, so the selection itself carries risk: a firm with thin public disclosure will starve the analysis, while an over-documented household name invites description rather than diagnosis. The deliverable is a formal report of around four thousand words, excluding references and appendices, referenced in APA 7. Appendices can carry supporting data, models and supplementary analysis, but not core content — a common way marks are lost is pushing substantive argument into an appendix to stay within the word limit. The report has two linked halves that students often treat as separate. The first is diagnostic: why is this business underperforming? This demands explicit application of marketing theory rather than a narrative of the company's troubles assembled from press coverage. The second is prescriptive: a recovery strategy that follows from the diagnosis. Recommendations that could have been written without the analysis — refresh the brand, invest in digital, improve customer experience — score poorly regardless of how well they are expressed. Where this module differs from a generic strategic marketing assessment is the sustainability and ethics dimension. The learning outcomes centre on sustainable marketing, social responsibility, and the intersection of marketing technology with sustainable practice. The marking criteria reward conclusions that show awareness of ethical and sustainability dimensions at every band above a pass. A recovery plan built purely on cost and revenue logic will therefore underperform against the rubric even if it is commercially sensible. The strongest submissions treat sustainability as part of the recovery mechanism rather than a section appended at the end. Assessments of this type increasingly sit within a tiered AI policy. Where a permissive tier applies, students may use AI tools for idea generation, structuring, source discovery, summarising notes, and proofreading or feedback — but not for producing the analysis itself — and must declare which tools were used and how, usually in a table placed before the reference list. Students are also expected to retain evidence of how their thinking developed, such as version histories or drafts, which can be requested if misconduct is suspected. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on company selection and data availability, explaining how a diagnostic framework should structure an argument, showing the difference between descriptive and evaluative use of theory, clarifying how sustainability criteria are actually assessed in a marking rubric, checking APA 7 consistency, and reviewing a completed draft against the published criteria.
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Entrepreneurship and Business Start-up
5,000 words
Enterprise Start-up Portfolio — Business Plan, Fundraising Strategy and Entrepreneurial Self-Assessment
An enterprise start-up portfolio assessment asks students to originate a business idea and build the full supporting case for it, then present that case both in writing and as a recorded investor pitch. It is a pass/fail module structure in which every component must be passed individually — a strong business plan cannot compensate for a weak pitch, or vice versa. The written portfolio divides into two unequal halves. The larger part is the business plan itself, built to a fixed section structure with prescribed word allocations: an executive summary; the business idea set against an identified market gap, with market research and competitor analysis; customer profiles and segmentation; product development; marketing and communications; financials and business models covering costing, pricing, sales and revenue; and the founding team with its core competencies. The word allocations are not decorative — financials carry the single largest weighting in both the word budget and the marking rubric, which tells students where analytical depth is expected and where concision is. The smaller part is reflective. It covers the fundraising strategy — which funding sources are realistic for this venture, and how each would be targeted, approached and secured — and the student's own entrepreneurial tendency, informed by a standardised self-assessment instrument taken online. The rubric here rewards critical self-reflection over description: reporting a test result scores poorly; interpreting what the result means for how this founder should build a team and where they need support scores well. The pitch component is assessed on visual and audio quality, content and message, comprehension, delivery, and evident preparation. Technical execution carries real marks, which students routinely underestimate. Two things separate strong submissions. The first is internal consistency: the revenue model must follow from the pricing, the pricing from the customer segment, the segment from the identified gap. Plans that read as seven separate essays under seven headings lose marks even when each section is individually competent. The second is specificity in the financials — costing assumptions stated and justified, rather than round numbers presented without derivation. Note that assessments of this type commonly require the student to retain all drafts and earlier versions of their work, and to sign a detailed declaration itemising exactly how any AI tools were used. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining what a market gap argument needs to be credible, showing how competitor analysis should be structured rather than listed, clarifying how costing and pricing assumptions should be built and presented, reviewing whether a fundraising strategy matches the venture's actual stage, explaining how reflective writing is assessed at postgraduate level, and checking a completed draft against the published rubric.
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mgt
This module requires you to complete two assessments: a group presentation (25%) and an individual report (75%). In the group presentation, your team must analyse a chosen industry using strategic tools such as PESTEL analysis, Porter’s Five Forces, and competitor/strategic group analysis. The presentation should critically evaluate the external and industry environment, include graphs, charts, and academic references, and demonstrate strong teamwork and confident delivery during the 15-minute presentation and 5-minute Q&A session. The individual report requires you to select a company from the recommended list and critically evaluate its strategic challenges, business ethics, sustainability practices, and/or organisational culture issues. You must analyse the impact of these issues on the company, provide well-justified recommendations, suggest suitable leadership and management approaches, and support all arguments using relevant strategic and leadership theories. The work should be analytical rather than descriptive, properly referenced with in-text citations, professionally formatted, and based on extensive research.
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