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500 words
Reflective Account – Data Collection Practices
This individual assessment requires students to complete practical data collection exercises and write a 400–500 word reflective account based on their experience. The assessment is designed to develop understanding of different data collection methods and encourage students to connect the practical exercises completed in the laboratory with research methods and concepts covered during lectures. Students are required to complete two main types of data collection practice. The first involves questionnaire tests, which students attend during their allocated practical sessions. The second involves a series of recorded interaction exercises involving ChatGPT and human participants. Students must follow the supplied study instructions, use the specified prompts where required, record the requested activities using a camera such as a phone, and upload the relevant files to a designated OneDrive folder. The briefing also requires students to complete and sign the participant consent form and submit it with their collected data. The reflective account should summarise the methods learned during the module lectures and the methods exercised during the laboratory sessions. Students should demonstrate their overall understanding of how data collection works, why data collection is important in research, and how knowledge of data collection methods can be improved through practical experience. The reflection must address several key research and methodological questions. Students should explain how they would design a research question based on the collected data and identify the PICO elements relevant to the research. They should discuss what type of research could be conducted using the data and define an example of a machine learning problem that could be investigated. The reflection should also consider the participant experience by identifying areas of the data collection process that could be improved. Students are additionally asked to consider how they would design a similar data collection project themselves and whether they would make any changes to the methods, procedures or participant experience. The assessment therefore combines practical experience with critical reflection on research design, data collection, research questions, PICO, machine learning applications and methodological improvement. The final reflective account is a Canvas submission and should remain within the specified 400–500 word range. The assessment briefing states that students are marked based on the submitted report, making the reflective account the primary assessed component of the activity.
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International Business
2,500 words
Literature Review – International Business Research
This assessment is a 2,500-word individual Literature Review for the International Business module. The assignment requires students to select an international business-related research topic, develop a suitable research question and critically examine existing academic literature relevant to the proposed area of research. The purpose of the assessment is to demonstrate knowledge of the selected subject area, evaluate existing research, identify important debates and inconsistencies, examine research methodologies and theoretical frameworks, and establish a clear research gap that can provide the basis for further investigation. Students must obtain tutor approval for their chosen research topic before completing the Literature Review. The selected topic should have clear relevance to international business and should address an issue that is academically significant and practically relevant. Students are expected to use high-quality academic sources, with particular emphasis on peer-reviewed journal articles. The assessment guidance expects students to critically discuss a minimum of five and a maximum of eight journal articles, although additional academic sources may be used to support the discussion and critique. The Literature Review consists of three main sections: Introduction, Literature Analysis and Conclusion. The Introduction should be approximately 300 words and should introduce the research topic, explain why the issue is important, establish the main research question and demonstrate its relevance to international business. Students should also briefly discuss the potential implications of the proposed research for the advancement of knowledge, theory and practice. The Literature Analysis should be approximately 1,900 words and forms the main part of the assessment. Students should move beyond simply describing individual articles and instead analyse, evaluate and synthesise the literature. For each selected journal article, students should consider its contribution to existing knowledge, theoretical frameworks, research methodology, suggested research gaps and relationship to the proposed research question. The literature should be compared and contrasted to identify areas of agreement, disagreement, methodological differences, theoretical differences and unresolved issues. The analysis should demonstrate critical engagement with the literature rather than providing separate descriptive summaries of each source. The Conclusion should be approximately 300 words and should bring together the major agreements, disagreements and themes identified in the literature. Students should use the conclusion to justify their proposed research by demonstrating how it can address one or more of the research gaps identified through the literature review. The assessment is worth 100% of the final module grade and requires Harvard-style referencing. Students must provide a complete reference list and ensure that sources are appropriately acknowledged throughout the review. The assessment criteria evaluate knowledge of the subject area, analysis of research and methodologies, critical evaluation of literature, communication of complex ideas and professional referencing. High-quality work is expected to demonstrate a well-defined international business topic, strong critical analysis, comparison and synthesis of academic literature, clear identification of a research gap and a logically justified direction for future research.
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Business Research Methods
3,500 words
Business Research Methods – Research Methodologies, Research Instrument and Research Dissemination
This individual assessment for the Business Research Methods module requires students to produce a structured report of no more than 3,500 words. The assessment is designed to develop students’ ability to identify and justify a suitable business research problem, critically evaluate research methodologies, design an appropriate research instrument, and consider how potential research findings could be effectively communicated to relevant audiences. The report consists of three interconnected sections that require students to demonstrate critical research skills and an understanding of how research can contribute to knowledge and practical decision-making. Section 1 focuses on comparing two research methodologies. Students select a topic of interest or a business problem relevant to their degree specialism, such as strategy, supply chain management, international business and economics, marketing, finance, or human resources. They first explain and justify the selected research problem and formulate a research question. Students then review relevant academic literature, justify the selection of key readings, and demonstrate the scope of the literature through a visual presentation such as a mind map, Venn diagram, or literature map. The section concludes with a detailed critical evaluation of at least two established research methodologies, considering their application, contribution to theory and research, advantages, limitations, constraints, and potential research gaps. Section 2 focuses on the development of a research instrument. Students design an appropriate instrument for their proposed research, such as an interview guide, survey questionnaire, or another suitable data-collection tool. The instrument is provided in an appendix, while the main report includes a reflection explaining how and why it was designed. Students are not required to collect data for this assessment. The section concludes by evaluating the expected value of the proposed instrument and its potential contribution to answering the research question. Section 3 addresses research dissemination. Students identify the audiences that could benefit from the potential research findings and explain why those audiences are relevant. They also consider appropriate methods of communicating research outcomes, including suitable summaries, media, report design, and potential partners such as public organisations, NGOs, associations, or industry. The assessment requires Harvard referencing and a bibliography containing at least 12 relevant journal articles, including at least six published within the previous 24 months. The report is assessed on the clarity of the research problem, depth of literature analysis, critical evaluation of research methods, quality of the proposed research instrument, understanding of research dissemination, and overall academic presentation.
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Computational Algorithms and Paradigms
1,000 words
Computational Algorithm Analysis – Research Paper Algorithm
This individual coursework for the Computational Algorithms and Paradigms module requires students to thoroughly analyse a computational algorithm proposed in a research paper selected from the list of research papers provided on Canvas. The purpose of the assessment is to develop students' ability to understand, explain and critically evaluate computational algorithms presented in academic research. Students must first identify and describe the computational problem addressed by the selected research paper and clearly state the research questions investigated by the authors. They must then extract the main computational algorithm proposed in the paper and present it in pseudocode. The assessment also requires students to clearly identify the inputs required by the algorithm and the outputs produced by it. The coursework consists of six main analytical sections. The first section focuses on the computational problem and research questions addressed in the selected paper. Students are expected to provide an accurate description of the problem and explain the research questions that the proposed algorithm attempts to address. The second section requires the proposed computational algorithm to be represented using suitable pseudocode. The third section identifies and explains the algorithm's inputs and outputs. The fourth section requires students to explain the proposed algorithm using simple and understandable language. The explanation should demonstrate a clear understanding of how the algorithm operates rather than simply reproducing the description provided in the research paper. The fifth section focuses on analysing the time complexity of the proposed algorithm. Students should evaluate the computational cost of the algorithm and explain its time complexity appropriately. The final section requires a critical evaluation of the algorithm's strengths and weaknesses. Students should identify the advantages and limitations of the proposed approach and discuss potential improvements where appropriate. This section should demonstrate critical thinking about the effectiveness, efficiency and practical applicability of the algorithm. The coursework has a total word count requirement of 800–1,000 words. The inputs and outputs, pseudocode and time-complexity analysis sections are excluded from this word-count limit. The template requires approximately 250 words for the computational problem and research questions, approximately 250 words for the simple explanation of the algorithm, and approximately 300 words for the strengths and weaknesses evaluation. Students must report the word count for sections 1, 4 and 6 after completing the assignment. The submitted work must be original and is subject to plagiarism and collusion checks through Turnitin. The assessment brief also states that generative AI tools may be used for proofreading but are not permitted for creating the coursework content. No figures or images are permitted, and the coursework must be submitted using the provided Word template in DOC or DOCX format.
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Critical Analysis of Computational Algorithms: Research Paper Evaluation and Complexity Analysis
This postgraduate Computer Science coursework requires students to undertake a critical technical analysis of a computational algorithm presented in a prescribed academic research paper. Students select one paper from the available options and demonstrate that they understand both the research problem addressed by the authors and the algorithmic solution proposed. The assessment contributes 30% of the overall module grade and is completed individually. assignment The available research papers cover several algorithmic topics, including an improved Dijkstra shortest-path algorithm for sparse networks, a modified merge-sort approach for large-scale datasets, parallel merge sort with load balancing, and a Prim-based algorithm for hierarchical clustering. Students must extract the principal algorithm from their selected paper and explain its purpose, inputs, outputs and operating procedure. A major component involves identifying the research question and computational problem addressed by the selected study. Students then reproduce or extract the proposed algorithm in pseudocode form and clearly identify the information supplied to the algorithm and the outputs it generates. The algorithm must also be explained step by step using straightforward language so that its operation can be understood without relying exclusively on formal notation. The coursework further requires a detailed time-complexity analysis, demonstrating understanding of how computational requirements grow with input size and how the proposed technique compares with alternative or conventional approaches. Students must critically evaluate the algorithm’s strengths, weaknesses, performance characteristics and limitations, and suggest potential improvements where appropriate. The marking rubric gives substantial emphasis to five areas: identifying the computational problem and research questions, extracting the proposed algorithm, identifying inputs and outputs, explaining the algorithm clearly, analysing its time complexity, and critically evaluating its strengths and weaknesses. assignment The written submission must be 800–1,000 words, although the inputs/outputs, pseudocode and time-complexity sections are excluded from that limit. Figures and images are not permitted, and the work must be submitted using the prescribed coursework template in DOC/DOCX format. Overview word count: approximately 340 words. AI-use note: the guideline permits generative AI only for proofreading. AI tools are explicitly not permitted to create the assessed work itself. assignment
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Strategic Management / Sustainability / Responsible Leadership
2,500 words
Organisational Strategy and Sustainability: Strategic Evaluation of Engineers & Planners Company Ltd
This Consultancy Project Proposal assessment requires students to develop a professional and feasible research proposal addressing a current organisational issue or business challenge affecting an existing organisation. The work combines critical business analysis with research design, secondary-data methodology, ethics and visual communication through an accompanying one-page digital poster. MSc Management Summative Assess… The first task requires students to introduce the selected organisation and critically examine between one and three current challenges. The discussion must be evidence-based and supported by recent sources. Stronger work should connect the identified issues to current affairs, recent news, Sustainable Development Goals and relevant academic or company evidence. MSc Management Summative Assess… The second task requires development of a clear research aim, three objectives and, for stronger submissions, one or two research questions. Students must demonstrate awareness and application of research methodology using secondary research only, while incorporating both qualitative and quantitative approaches. The research design should be directly connected to the selected organisational challenges. MSc Management Summative Assess… The third task focuses on the research plan and ethical considerations. Students must explain ethical issues associated with secondary-data collection and support the discussion using credible literature. The accompanying poster should present a coherent research plan and demonstrate effective data-analysis and data-presentation skills through relevant graphs, charts, tables or descriptive statistics. MSc Management Summative Assess… The fourth task assesses the student's ability to critically organise and synthesise information into a coherent proposal. The written report should use recent, credible sources, while the poster should include a short reflection on challenges encountered when synthesising evidence for the proposed study. MSc Management Summative Assess… The report must be written in the third person and use Harvard referencing. The proposed structure allocates approximately 150 words to the introduction, 400 words to the challenge discussion, 150 words to the research aim and objectives, 500 words to methodology, 200 words to ethical considerations and 100 words to the conclusion. The digital poster has no formal word count but must fit on a single A4 page and use a readable 10–12 point font. MSc Management Summative Assess… MSc Management Summative Assess… Overall, the assessment develops skills in consultancy problem definition, research design, secondary-data analysis, mixed-method thinking, ethical research practice, critical synthesis and professional visual communication.
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Computer Science / Algorithms / Computational Complexity
Modified Merge Sort for Large-Scale Data: Algorithm Analysis, Complexity and Evaluation
This Computational Algorithms and Paradigms assignment critically examines a modified merge sort algorithm designed for large-scale datasets. The work focuses on the computational problem of sorting very large collections of data efficiently while preserving the stability and predictable complexity associated with classical merge sort. The analysed approach replaces recursive processing with an iterative successive-merging strategy intended to reduce stack overhead and improve practical performance on large datasets. Dubba ramesh(up) The first section identifies the underlying computational problem and frames the main research questions. These include how standard merge sort can be modified to improve large-scale performance, whether recursion can be replaced with a non-recursive iterative process, whether the proposed double-merge technique reduces resource consumption, and how its computational performance compares with classical merge sort. Dubba ramesh(up) A technical section then reconstructs the algorithm in pseudocode. The modified process begins with subsequences of size one and repeatedly merges adjacent sorted subsequences, doubling the merge size after each iteration until the entire dataset is sorted. This bottom-up approach removes the recursive decomposition used in conventional merge sort. Dubba ramesh(up) The assignment also identifies the algorithm's principal inputs and outputs. Inputs include the dataset, number of elements, subsequence boundaries and temporary storage required during merging. The resulting output is a fully sorted and stable sequence. Dubba ramesh(up) Complexity analysis shows that the modified algorithm processes approximately n elements across log₂(n) merging levels, resulting in O(n log n) time complexity in both best and worst cases. Because an auxiliary array is used during merging, the reported space complexity is O(n). Dubba ramesh(up) The final critical evaluation highlights the main benefits of the modified approach, including removal of recursive-call overhead, greater stability when processing very large datasets, predictable performance and preservation of merge-sort stability. Its main limitation is the continued requirement for auxiliary memory during the merge operation. The work also notes that the performance advantages are most relevant for large-scale datasets and may be less significant for smaller inputs. Dubba ramesh(up) Overall, the assignment integrates algorithm interpretation, pseudocode extraction, input-output analysis, complexity analysis and critical evaluation within the context of large-scale sorting. Note: this upload appears to be the completed student response rather than the original assessment brief, so the referencing style and exact formal overall word limit are not stated. I would leave the reference-style field as Not specified unless you also upload the official 7COM1078 guideline.
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Computer Science / Research Methods / Specialist Research
2,800 words
Research Specialism Report: Critical Review of Open Research Questions in Computer Science
This Advanced Research Topics in Computer Science assessment requires students to critically examine a research paper associated with their chosen MSc specialism and demonstrate an understanding of how established research techniques are used to create and extend knowledge in computer science. Eligible specialisms include Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. The assessment is intended to prepare students for deeper independent research as part of their Master's project. 7COM1084+Research+specialism+re… Students begin by providing a clear introduction to their selected research specialism and explaining the broader research area in a way that is accessible to readers with general computer-science knowledge. The report then identifies the open research question presented in the relevant 7COM1084 specialist lecture paper, explains the problem in detail and evaluates why it is scientifically significant or relevant to a real-world application. 7COM1084+Research+specialism+re… A substantial literature-review section requires students to examine existing and related research beyond the specialist lecture paper. The aim is to identify what previous work has achieved, explain why existing approaches do not fully solve the research problem and identify further unresolved questions. 7COM1084+Research+specialism+re… The research-methods section focuses on the approaches used in the selected specialist paper. Students are expected to describe and critically evaluate those methods, considering both their strengths and limitations. They must then propose an alternative or extended research approach that could build on the published work and investigate related open problems, drawing on principles of experimental design and theoretical or practical research. 7COM1084+Research+specialism+re… The final reflective component asks students to explain their personal investment in the research area, including why the selected question interests them and how their own strengths and prior experience would support future research in that domain. 7COM1084+Research+specialism+re… The report must not exceed 2,800 words ±10%, must use the Harvard referencing system, and must include at least 20 references, one of which must be the relevant 7COM1084 specialist paper. 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… 7COM1084+Research+specialism+re… Overall, the assessment integrates research specialism knowledge, literature review, open-problem identification, methodological critique, research design, future-work development and scholarly communication within a Level 7 computer-science research context.
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Computer Science
2,800 words
Specialism Research Report
The Specialism Research Report is an individual assessment for the Advanced Research Topics in Computer Science module. The assignment provides students with an opportunity to develop a deeper understanding of a selected computer science research specialism and to investigate an open research question within that area. The available research specialisms are Artificial Intelligence, Networking, Cyber Security, Software Engineering and Data Science. Students are expected to examine the research specialism associated with their degree route and develop their understanding of current research challenges, established techniques and potential future research directions. The report requires students to provide an overview of the research paper presented in the relevant 7COM1084 Research Specialism Lecture. The report must identify and explain the research question addressed by the specialist paper, consider why the research problem is scientifically interesting or relevant to a real-world application, and investigate possible approaches for addressing the question. Students must also identify further research that could build upon the existing work and consider their own personal strengths, interests and experience in relation to the proposed research. The report is structured into five main sections. The first section, Introduction of Research Specialism, provides a general overview of the selected research area and is intended to be accessible to readers with broad computer science knowledge. The second section, Open Research Question, identifies and explains the research problem discussed in the specialist lecture paper and considers its scientific or practical significance. The third section, Existing and Related Work, provides a literature review of research beyond the specialist paper, identifies limitations in existing approaches and discusses related open research problems. The fourth section, Research Approach, examines the methods used in the specialist paper, evaluates their strengths and weaknesses, and proposes an approach for extending the research. The fifth section, Personal Investment, explains the student's interest in the research question and evaluates their personal strengths and experience relevant to conducting the proposed research. Students are expected to use module reading materials and secondary research to support their discussion. The report must use Harvard referencing and include at least 20 references, including the 7COM1084 specialist paper. The references are excluded from the word count. The submission must not exceed 2,800 words, with a permitted range of ±10%. The assessment develops students' ability to understand established research techniques, identify research problems from relevant literature, propose alternative solutions, critically evaluate research literature, develop approaches for future research and communicate research knowledge effectively in a scholarly manner.
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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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Consultancy Project
1,464 words
Enhancing Sustainable Supply Chain Practices through Digital Transformation: A Case Study of Unilever Plc
This consultancy project proposal examines how digital transformation can enhance sustainable supply chain practices at Unilever Plc. Unilever is presented as a multinational fast-moving consumer goods organisation operating across more than 190 markets, with a complex global supply chain and sustainability commitments linked to the United Nations Sustainable Development Goals. The proposal focuses on the challenges associated with achieving supply chain transparency, managing fragmented sustainability data, and balancing sustainable sourcing and operational costs. The project investigates how emerging digital technologies, particularly blockchain, the Internet of Things (IoT) and Artificial Intelligence (AI), could support greater transparency, traceability, operational efficiency and sustainability within Unilever's supply chain. The organisational analysis identifies issues including the complexity of tracing raw materials across multi-tier supply chains, fragmented procurement and sustainability information systems, and the tension between sustainability investments and short-term operational costs. The proposal also considers infrastructure readiness, data security and employee upskilling as factors affecting digital transformation. The research aims to examine how digital transformation technologies could improve sustainable supply chain planning at Unilever while supporting long-term business and environmental goals. Three objectives are established: examining sustainability challenges affecting Unilever's global supply chain, assessing how blockchain, IoT and AI could improve transparency, traceability and efficiency, and proposing strategic recommendations for integrating digital innovation into Unilever's sustainability framework. The central research question examines how digital transformation can contribute to an ethical, transparent and sustainable supply chain while maintaining operational efficiency. A qualitative exploratory case-study research design is proposed. The main emphasis is on secondary data, including Unilever's annual reports, sustainability information, digital transformation publications, company news, industry articles and analyst reports. Where feasible, primary data may be collected through semi-structured expert interviews and a short qualitative questionnaire. The proposed analysis includes thematic analysis, document review and descriptive statistics using relevant secondary numerical data. Validity is supported through triangulation, while reliability is addressed through consistent coding and an audit trail. The proposal also addresses ethical considerations, including research integrity, transparency, confidentiality, informed consent, anonymity, GDPR requirements, plagiarism avoidance, Harvard referencing and potential corporate bias or greenwashing. Overall, the project explores the potential contribution of digital innovation to Unilever's sustainability objectives and the wider responsible digitalisation of the FMCG supply chain.
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Consultancy Project
1,500 words
Consultancy Project Proposal
This assessment requires students to develop a professional Consultancy Project Proposal addressing a current organisational issue or business challenge. The assessment consists of a 1,500-word individual project proposal worth 80% of the module grade and an A4 Consultancy Project Proposal Digital Poster worth 20%. The proposal should provide an in-depth analysis of a selected business or management issue and establish a comprehensive research methodology for investigating the identified challenge. The assignment requires students to select an existing organisation and critically examine between one and three current and ongoing organisational challenges. The discussion should be evidence-based and supported by relevant, recent and credible sources, such as academic journals, company reports and other appropriate research. Students are encouraged to establish links between the selected challenges, current affairs, news events and the United Nations Sustainable Development Goals where relevant. The proposal must establish a clear research aim and three research objectives connected to the selected organisational challenges. Students must demonstrate awareness and application of appropriate research methodologies, including consideration of qualitative and quantitative approaches. The assessment also encourages the development of one or two concise research questions and requires students to demonstrate how the selected research methods can be applied to investigate the identified challenges. Students must also develop a feasible research plan and discuss ethical considerations, particularly those relating to secondary data collection. The digital poster should communicate the research plan clearly and demonstrate data analysis and data presentation skills through appropriate graphs, charts and tables. The final proposal should critically organise and synthesise information, provide a clear conclusion, and maintain professional academic standards. The recommended structure consists of a cover page, table of contents, list of abbreviations where appropriate, introduction, critical discussion of challenges, research aim and objectives, application of research methodologies, ethical considerations, research plan, critical organisation and synthesis of information, conclusion, references, the Consultancy Project Proposal Digital Poster and appendices where required. The main proposal has a 1,500-word limit, while the digital poster should not exceed one A4 page. The assignment requires Harvard referencing and the report should be written in the third person.
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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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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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Operations and Supply Chain Management
1,987 words
Supply Chain Analytics and Quantitative Data Analysis for Organisational Decision-Making
This postgraduate individual report focuses on the application of quantitative data analysis to Operations, Logistics and Supply Chain Management decision-making. Students are required to select an organisation from the private, public or third sector and investigate a relevant operational or supply-chain issue using quantitative data. The purpose is to demonstrate how data can be collected, prepared, analysed and interpreted to generate evidence-based insights that may support managerial decision-making. The selected dataset must relate to the organisation's operations or supply chain and may include variables such as revenues, product orders, sales, transportation costs, procurement expenditure, inventory levels or other appropriate quantitative measures. The dataset must contain at least 60 observations, and the analysis must involve at least two variables. Data may be obtained directly from organisations or from recognised secondary-data platforms and databases. The assessment consists of two equally weighted components. Part A – Motivation and Justification for the Analysis requires students to formulate relevant analytical questions and explain their practical importance by linking them to Operations and Supply Chain Management theory and business practice. Appropriate academic, industry and practitioner evidence should be used to justify the selected issue. Research questions may also be translated into testable hypotheses where appropriate. Part B – Execution of the Analysis requires students to answer the identified questions through appropriate statistical techniques. Potential methods include tables, charts, summary statistics, t-tests and regression analysis. Data may first need to be cleaned, transformed and structured before analysis. The results must then be interpreted clearly for a managerial audience such as the organisation's board, owner or CEO. The statistical analysis is expected to be conducted using Stata, with all data-cleaning, manipulation and analytical commands recorded in a reproducible do-file. The report must also demonstrate explicit links between theory and practice and contain a suitable mixture of academic and professional evidence, including at least five academic journal articles. Harvard referencing is required throughout. The resulting work demonstrates practical competence in business analytics, statistical interpretation, supply-chain decision support, reproducible analysis and evidence-based managerial communication. Overview word count: approximately 370 words.
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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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Computer Science / Research Methods
500 words
Interim Report: Systematic Literature Review Research Protocol
This postgraduate computer science assignment requires students to prepare an Interim Report establishing the research protocol for a systematic literature review. The assessment focuses on whether the proposed research question is suitable for computer science research, clearly formulated, appropriately motivated by existing literature, and capable of being investigated through a systematic review. The main body of the report must not exceed 500 words, while supporting evidence may be provided separately where appropriate. The report is structured around three main chapters. Chapter 1 introduces the selected research area and summarises the purpose and structure of the report. Chapter 2 provides relevant background and a brief history of the chosen research domain, identifies the problem being addressed, and supports the discussion with at least two relevant academic citations. Chapter 3 presents the formal Literature Review Protocol, including the research question, its context, and the associated PICO elements: Population, Intervention, Comparison and Outcome. Students must also develop and test a Boolean search string using IEEE Xplore and report the number of papers returned by that search. The search strategy is accompanied by explicit inclusion and exclusion criteria governing which studies will be considered for the review. The supplied protocol template requires students to document these elements through two structured tables: one covering the research question and PICO framework, and another recording the search string, paper count, and study-selection criteria. The assessment places significant emphasis on methodological consistency. The research question, PICO elements, search strategy, paper count and inclusion/exclusion criteria must align logically with one another. Students are also assessed on the justification and motivation of the research question, document structure, presentation quality, spelling, grammar and academic referencing. The report must include a title page, table of contents, bibliography and the required research-protocol tables. Harvard referencing is required for both in-text citations and the final reference list. Overall, the assignment develops the foundational skills required for conducting a rigorous systematic literature review, including research-question formulation, structured evidence searching, transparent study-selection procedures, academic justification and professional research reporting.
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Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
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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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Computer Science
1,700 words
Systematic Literature Review — Final Report with Data Extraction and Synthesis
A Research Methods final report at postgraduate computer science level is a systematic literature review written to a defined protocol. Unlike an essay, the method itself is assessed: markers re-run the student's search string and check that the number of papers returned matches what the report claims, so the process must be reproducible rather than merely described. The report is typically built in five chapters. Chapter 1 introduces the research area and states that a literature review is the chosen method. Chapter 2 gives the background, with citations behind every claim or assertion. Chapter 3 sets out the research protocol — the research question decomposed through PICO (population, intervention, comparison, outcome), the search string, and explicit inclusion and exclusion criteria. Chapter 4 presents the results: a data extraction table holding direct quotations and references from each included paper, followed by a data synthesis table that groups those extractions into cross-cutting themes. Chapter 5 concludes by answering the research question from the Chapter 4 evidence alone. Several requirements catch students out. The word limit applies to the main body only — tables, figures, references and appendices sit outside it, which changes how the argument should be distributed. Screenshots and supplementary search strings belong in appendices, not the body. Where an interim report has already been submitted and marked, the final report must visibly incorporate that formative feedback rather than reproduce the earlier chapters unchanged. File naming and file completeness are often mark-bearing in their own right, with missing files scored at zero. Presentation carries weight too: consistent heading and font usage, labelled tables and figures, and error-free spelling and punctuation. The most common conceptual error is treating Chapter 4 as a narrative summary of each paper in turn. A synthesis groups evidence by theme across papers and answers the question; a summary walks through the reading list. A related error is a research question that the background has not motivated — the introduction and background should make the question feel necessary before the protocol formalises it. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how to decompose a research question using PICO, reviewing whether a search string is syntactically valid and whether inclusion criteria genuinely follow from the question, showing how extraction tables feed into synthesis tables, clarifying Harvard referencing conventions, checking report structure and formatting against the specification, and reviewing a completed draft against the published marking criteria before submission.
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Research Methods (Computer Science / Systematic Literature Review)
500 words
Interim Report – Research Methods (7COM1085)
This assignment is an Interim Report for the Research Methods module (7COM1085). The purpose of the report is to demonstrate that the research team has identified a suitable Computer Science research topic and developed a systematic literature review protocol that can be used for the final dissertation or research project. Students are required to formulate a clear and well-justified research question, define the associated PICO (Population, Intervention, Comparison, Outcome) elements, create an IEEE Xplore Boolean search strategy, and establish appropriate inclusion and exclusion criteria for selecting academic literature. The report must also provide background information on the chosen research area and justify the importance of the research question using relevant scholarly sources. The interim report should follow a structured academic format consisting of an Introduction, Background, Literature Review Protocol, and Bibliography. Students must use Harvard referencing throughout the report and ensure that all citations are properly integrated within the text. The report should demonstrate consistency between the research question, search strategy, and selection criteria while maintaining a professional academic presentation. The main body of the report must not exceed 500 words, although supporting materials such as search results, screenshots, and other evidence may be included in appendices. The assignment is designed to assess students' understanding of research methodologies, systematic literature reviews, critical analysis of academic sources, and their ability to design a rigorous research protocol for a Computer Science investigation.
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