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Business
800 words
Individual Reflection on Group Research Presentation
This assignment is an individual written reflection based on a group research presentation completed for the Principles of International Business module. The assessment requires students to reflect on both the research topic explored by their group and the process of working collaboratively to develop and deliver the group presentation. The reflection has a word count of 800 words and should demonstrate thoughtful consideration of the student's individual experience, contribution and learning. The first part of the reflection introduces the research topic investigated by the group. Students are expected to provide a concise summary of the subject explored and establish the context for their reflection. This provides an opportunity to explain the focus of the group's research and the main area investigated. The second area focuses on the group project experience. Students should reflect on their own contribution to the group presentation and evaluate how effectively the group collaborated. The reflection should consider aspects of teamwork such as communication, presentation, time management and conflict resolution. Students should also discuss what they learned from undertaking the research project, including critical reading and thinking, development of the research topic, data collection and analysis, and the process of making recommendations. Importantly, the reflection should include at least one specific example of how a problem was identified and resolved during the group process. The final area focuses on research skills development. Students should identify the research skills they developed while conducting the chosen research topic and provide examples of how these skills were applied during the group research project. They should also discuss challenges encountered while completing the project and explain how they addressed or overcame those challenges. Overall, the assignment is designed to encourage students to critically reflect on their group research and presentation experience, rather than simply describe what happened. It provides an opportunity to consider individual contributions, teamwork, research capabilities, problem-solving and personal learning gained through the project. The assessment therefore combines reflection on the international business research topic with evaluation of collaborative working and the development of research skills.
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800 words
Individual Reflection on Group Research Presentation and Research Skills Development
This assessment is an individual 800-word reflective piece that requires students to reflect on their experience of completing a group research project and delivering a group presentation. The reflection focuses on the research topic explored by the group, the student's individual contribution to the presentation, the effectiveness of group collaboration, the development of research skills and the challenges encountered throughout the research process. The reflection begins with an introduction to the research topic investigated by the group. Students are expected to provide a concise summary of the topic explored and establish the context for their subsequent reflection. The discussion should demonstrate an understanding of the research undertaken by the group and provide a clear foundation for evaluating the student's experience. The second area focuses on the group project experience. Students should describe their individual contributions to the group presentation and critically reflect on how effectively the group worked together. The reflection should consider aspects of teamwork such as communication, presentation skills, time management and conflict resolution. Students should also identify what they learned from both the teamwork process and the research project, including areas such as critical reading and thinking, research topic development, data collection and analysis, and making recommendations. At least one specific example of how a problem was resolved during the group process must be included to demonstrate practical reflection on the team's experience. The third area focuses on research skills development. Students should identify the research skills they developed while conducting the chosen research topic and provide specific examples of how these skills were applied during the group research project. The reflection should also discuss challenges experienced while completing the research and explain how those challenges were addressed or overcome. Overall, the assessment requires students to move beyond simply describing what happened and provide meaningful reflection on their learning and development. The reflection should demonstrate how participation in the group research project contributed to the development of teamwork, communication, research, analytical and problem-solving skills. It should also consider how these skills can support future academic and professional activities. The assessment is marked using four criteria: presentation and structure of the reflection, breadth and depth of reflection, research skills development, and group presentation experience. The rubric allocates 10 points to presentation and structure, 30 points to breadth and depth of reflection, 40 points to research skills development and 20 points to group presentation experience, for a total of 100 points. The assessment instructions also state that students must not use AI tools for the assignment.
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Big Data and Cloud Computing
2,500 words
Big Data and Cloud Computing: FieldVision Cloud-Based Big Data Solution for AgroNova
This assessment is a 2,500-word individual report for the Big Data and Cloud Computing module within the MSc Management with Data Analytics programme at BPP University. The report is based on a fictional agricultural technology company, AgroNova, and its FieldVision project. AgroNova provides climate change and crop monitoring services to farmers in the UK and intends to expand internationally. However, its existing ageing infrastructure and manual processes present challenges to international expansion and the development of data-driven decision-making. The FieldVision project aims to use Internet of Things (IoT) technology and cloud-based big data solutions to collect, store and analyse real-time information from agricultural environments. The report requires students to act as a big data and cloud solution consultant and provide recommendations to senior management at AgroNova. The project involves IoT devices such as CropCam aerial cameras supplied by HydroSense and SmartHarvester solar-powered sensors supplied by SoilTech. These technologies collect information including crop imagery, soil moisture, temperature and humidity. The resulting data can support crop monitoring, risk assessment, early warning alerts, irrigation decisions, crop-failure claims validation and other farming-related insights. The first task focuses on Big Data Requirements and Storage Solutions. Students must identify the requirements arising from the scenario and critically evaluate a range of cloud-based big data storage solutions. The evaluation should consider factors such as capacity, functionality and costs. The second task requires students to propose one appropriate cloud-based solution architecture supported by an architecture diagram showing the essential components from data sources through to reporting. The selected architecture must be analysed in relation to AgroNova's requirements and the storage solutions considered in Task 1. The third task addresses Project Risks and Issues. Students must critically appraise the risks and issues associated with deploying the proposed cloud-based big data solution and identify appropriate mitigation approaches. These issues should be connected directly to the storage solutions and proposed architecture. The scenario highlights concerns from AgroNova's CISO, CFO and Chief Reputation Officer regarding potential data breaches, high costs and poor returns on investment, making security, financial viability and organisational risk important considerations. The report should contain an approximately 200-word introduction, an 800-word analysis of Big Data Requirements and Storage Solutions, a 500-word Proposed System Architecture section supported by relevant diagrams, an 800-word Project Risks and Issues section, and an approximately 200-word conclusion. Harvard referencing, academic research and appropriate supporting appendices are also required. The assessment addresses three learning outcomes: designing an architecture that supports complex data collection, critically evaluating data storage solutions from an enterprise systems perspective, and critically appraising issues involved in enterprise-system deployment.
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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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1,500 words
Network Security Evaluation and Monitoring – Reconnaissance, Incident Response and APTs
This coursework assesses the research and analytical abilities required to design and evaluate an effective network security evaluation and monitoring solution. The scenario places the student in the role of a network security evaluation specialist responsible for helping a client design and build a monitoring solution for a complex client network. The client operates in the defence and security sector, works with government departments, multinational organisations and foreign agencies, and handles sensitive information. The network includes several server farms, gateway nodes, hundreds of client nodes, internal application services, externally accessible services and wireless access points. The organisation is considered vulnerable to threats such as sabotage and intellectual property theft. The coursework requires all questions to be answered in the given order within a single report. An abstract is not required, and students are expected to use technical terminology precisely. Relevant and clearly labelled illustrations are encouraged. Where assumptions are required about security software, hardware or services already deployed on the network, these assumptions must be clearly identified in a dedicated “Assumptions” section at the beginning of the report. Question 1 focuses on detecting network reconnaissance originating from inside the organisation. Students must explain how an insider could collect and use reconnaissance information for malicious purposes, identify the types of data that should be collected and the appropriate network locations for collection, and justify the selection of monitoring data. The question also requires recommendations for suitable tools and configurations to detect reconnaissance activity, together with strategies for dealing with the scale and high traffic volume of the client network. This section carries 30 marks and has a suggested length of 500 words. Question 2 focuses on incident response following a confirmed security incident. The scenario involves suspicious out-of-hours activity and an external flash drive connected to a workstation at gateway 10, a large number of files being opened on a file server at gateway 9, and significant traffic between the workstation and a database server at gateway 5. Students must determine which previously collected data would be relevant, explain the evidence expected from that data, and recommend additional network and endpoint data that should be collected. The proposed approach must be forensically sound so that evidence can potentially be used in court. This section carries 50 marks and has a suggested length of 700 words. Question 3 addresses Advanced Persistent Threats (APTs) and evaluates the effectiveness of the proposed monitoring solution. Students must recommend appropriate testing to determine whether the monitoring system operates according to its specifications and objectives, explain the types, timing and location of testing, and identify suitable qualifications, certifications, knowledge and tool experience for security testers. The section also requires discussion of APT behaviour and how the proposed monitoring mechanisms could detect or prevent such activity. This section carries 20 marks and has a suggested length of 300 words. Overall, the coursework develops skills in network security monitoring, reconnaissance detection, incident response, digital forensics, security testing and APT detection. It requires students to connect technical monitoring strategies with practical security, legal and operational considerations within a complex organisational network environment.
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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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People Analytics / Human Resource Management
2,003 words
Principles of People Analytics and Evidence-Based Decision Making
This CIPD Level 3 assessment introduces the principles of people analytics and evidence-based decision making within the people profession. It focuses on how data, professional expertise, research evidence and stakeholder information can be used to diagnose organisational issues, support rational decision making and improve people practices. The unit emphasises the practical application of analytics rather than data collection alone. The assessment uses a recruitment scenario in which the learner applies for the position of People Analytics Administrator at Company X, an organisation providing HR and people-management solutions. Learners complete an assessment pack containing eight questions designed to demonstrate understanding of how analytics can support business and people decisions. The written component requires learners to explain evidence-based practice and demonstrate how it could be applied in an organisational context. Other areas include the importance of accurate data in diagnosing problems, different forms of data measurement, the role of organisational policies and procedures in decision making, and how people professionals create value for employees, organisations and wider stakeholders. Learners must also consider how a people analytics professional can remain customer focused and standards driven. The practical analytics element uses employee overtime data from Blue Mountain Patisserie. Learners calculate average overtime for individual employees, express overtime as a percentage of normal working hours, interpret patterns within the data and identify potential organisational problems and possible solutions. Findings must then be communicated using at least two different diagrammatic formats, such as bar graphs, pie charts or line graphs. The required written evidence is approximately 1,500 words for Questions 1–6 and 500 words for Question 7, giving approximately 2,000 words in total, with the visualisations excluded from the word count.
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Computing / Artificial Intelligence / Digital Transformation / Management Consultancy
7,000 words
AI Readiness and Programme Adoption Strategy for the NewFutures: AI Programme at Northumbria University London
This postgraduate consultancy project focuses on developing an AI readiness, skills-development and programme-adoption strategy for students and recent alumni at Northumbria University London. The project supports the university’s participation in NewFutures: AI, a funded AI skills and career-readiness programme offering a four-week online course covering responsible AI foundations and specialist pathways in Marketing and Communications, Finance and Accounting, Business Operations and Logistics, Administration, and ICT and Technical Support. The central consultancy challenge is to understand the AI literacy, confidence, readiness and training needs of Northumbria University London students and alumni and translate that evidence into a practical implementation and outreach strategy. The client aims to reach approximately 12,000 students and recent alumni and support a target of 6,000 LMS registrations during the 2026–2027 programme period. The project requires primary and secondary research into AI readiness, demand for different AI-skilling pathways and barriers to participation such as awareness, time, perceived value and accessibility. The consultancy team is also expected to benchmark comparable initiatives and use research evidence to develop recommendations appropriate to different academic disciplines and student and alumni groups. The implementation component focuses on designing an evidence-based outreach and adoption campaign, including appropriate communication channels, messaging, timing, incentives, faculty engagement and stakeholder participation. Recommended channels may include email campaigns, newsletters, social media, student services, events, learning platforms and alumni communications. The project also requires an implementation timeline and indicative budget for the 2026–2027 programme period. The wider assessment develops professional consultancy capability through business and requirements analysis, research methodology, ethical research practice, practical implementation, testing and strategic recommendations. The project charter additionally establishes milestones for research design, data collection, analysis, report development, review and presentation, together with defined responsibilities for project management, data analysis, AI expertise and stakeholder communication. The individual component complements the consultancy work through critical reflection on personal contribution, skills development, decision-making, problem-solving, communication, collaboration, technical capability, innovation and continuous professional development.
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Data Science
1,000 words
Data Investigation Pipeline — Exploratory Analysis and Statistical Evaluation of a Chosen Dataset
This assessment simulates the opening stages of a real data investigation. Students choose their own research question and dataset, then build the full pipeline from raw data through preparation, exploration and statistical testing to visualisation — and, where the question supports it, simple modelling or forecasting. Either Python or R is acceptable; the statistical route through R typically expects an explicit hypothesis rather than a purely exploratory question. The work is structured around an established process methodology such as CRISP-DM, and the development journey is documented alongside the code rather than reported after the fact. The usual submission format is a single notebook combining markdown and code cells, so the written report and the analysis sit in one artefact, though a word-processed document containing the code is normally also accepted. The written element is short — around a thousand words — which makes selection the hardest part of the task. It must cover the scenario, the data collection, the exploratory analysis, the reasoning behind the choice of statistical tests, their results, and the visualisations. Students routinely spend that budget describing what they did and leave nothing for why. The mark distribution makes the priority explicit. Framing the problem and the data source carries the smallest share. Preparation and exploratory analysis, and evaluation of the results in context, carry the bulk in roughly equal measure. That final component is where most marks are lost: it asks for an honest assessment of accuracy, limitations and usefulness. A notebook that produces clean output and then claims more than the data supports scores below one that reports a modest result and explains precisely why it is modest. Established metrics should be used for the statistical tests, and published research cited where it informs the background or interprets the findings. Note that assessments of this type increasingly include a live demonstration in which the student explains their own project to verify authorship, so every line of the submitted work needs to be something the student can talk through unprompted. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how to scope a research question so the analysis fits the word limit, clarifying which statistical test suits which data type and why, reviewing whether a chosen visualisation communicates what it claims, showing how to write an honest limitations section, checking Harvard referencing, and reviewing a student's own draft notebook against the published marking criteria.
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