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Engineering and Environment Advanced Practice London Campus Consultancy Project
2,000 words
Individual Reflective Report – Group Consultancy Project
This assessment is an individual reflective report based on the student’s learning and experience during the Group Consultancy Project for the Engineering and Environment Advanced Practice London Campus Consultancy Project module at Northumbria University. The assessment requires students to critically reflect on their activities, contributions, skills development and professional learning throughout the group-based consultancy experience. The report has a total word limit of 2,000 words, excluding the table of contents, page numbers and captions for figures and tables. It is structured around two main components: a Progress Report of approximately 1,000 words and a Reflection of Learning and Development of approximately 1,000 words. The Progress Report brings together the main themes of the module from the perspective of additional skills development, engagement in self-development, group-based learning, cultural awareness and ethical awareness. Students are expected to discuss key personal activities undertaken during the group project, skills gained through participation, personal contributions to the project, and the development of interpersonal and intrapersonal skills. Academic literature can be used to support the discussion. The Reflection of Learning and Development section requires students to critically evaluate their personal strengths and weaknesses and demonstrate their ability to engage in continuous self-development within the context of group project work. Students should reflect on a range of project activities and provide examples of their involvement in decision-making, problem-solving, communication and influencing, technical skills, collaboration, innovation and proactivity. The assessment therefore focuses not only on describing what students did, but also on evaluating what they learned and how the experience contributed to their professional and personal development. The assessment is worth 50% of the total marks available for the module, which is assessed on a pass/fail basis. The assessment rubric evaluates the Progress Report at 50%, Reflection of Learning and Development at 40%, and Writing Style at 10%. Strong submissions are expected to provide clear and relevant examples, integrate activities with skills and contributions, demonstrate critical reflection on strengths and weaknesses, and present a well-structured and professional written report. Students are also expected to follow the university’s requirements regarding academic integrity and the responsible use of generative AI. The assessment guidance states that AI may assist with activities such as improving grammar, formatting structure, organising ideas and generating suggestions, but the main content, analysis and conclusions must remain the student’s own work. Students are required to declare their use of AI tools when submitting the assessment.
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Business
850 words
HopeLink Community Support and Food Security Database
This assignment requires students to design, develop and analyse a relational database for HopeLink Community Support and Food Security, a fictitious non-profit organisation working across rural and urban communities to reduce poverty and hunger. HopeLink currently relies on spreadsheets, which have resulted in inefficiencies and reporting errors. The proposed database is intended to improve operational efficiency, transparency and evidence-based reporting while supporting the organisation's work towards United Nations Sustainable Development Goals (SDGs) 1 and 2: No Poverty and Zero Hunger. The database development project involves five main entities: Beneficiary, Donor, Product, Collection and Collection Detail. Students are required to create a data dictionary defining the tables, attributes and appropriate validation rules. They must then use SQL within Microsoft Access to create the required tables through SQL View, rather than using the Access front end. Primary keys and foreign keys must be identified and implemented as part of the database design. Students must also establish relationships between the database entities and enforce referential integrity to maintain accurate information for management reporting and decision-making. Appropriate validation rules, user-friendly error messages, drop-down lists and input masks should be implemented where required. The database must be populated with sufficient realistic data for testing, with minimum requirements of 10 Beneficiary records, 10 Donor records, 20 Product records, 10 Collection records and 20 Collection Detail records. A further component requires students to develop five useful SQL queries in Microsoft Access to support analysis and reporting for HopeLink. The queries must follow specific requirements, including one parameter query, at least three queries containing a WHERE clause, at least one aggregate query and at least two queries involving joins across more than two tables. Students must provide screenshots of the SQL code and resulting outputs, together with an English explanation and a short justification of how each query could support HopeLink's operations and decision-making. The final component is an 850-word maximum business insights report investigating the use of food banks in the UK and considering how initiatives could support SDG 1 and SDG 2. Students are expected to use reliable external data, present relevant graphs and analyse current trends in food-bank usage. This section requires Harvard referencing and should use reliable academic, industry and other appropriate sources. The report is based on SDG 1 and SDG 2 and is not directly linked to the database developed for the assignment. Overall, the assignment assesses students' ability to apply data-modelling techniques, database design principles, SQL and data analytics to support organisational operations and strategic decision-making. It combines practical database development in Microsoft Access with data analysis and a business-focused evaluation of food-bank trends and sustainability goals.
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Operations Management / Business Simulation / Process Improvement
3,500 words
Operations and Simulation Analysis: Sam’s Sandwich Shop
This operations-management portfolio uses the Sam’s Sandwich Shop case study to examine service-process performance, simulation modelling, resource planning and operational improvement within a busy takeaway food environment. The case concerns a small sandwich outlet located in the departure lounge of Heathrow Airport Terminal 5, where rising terminal capacity is expected to increase customer demand over a five-month period. Students investigate how the store’s current sandwich assembly and service process performs under this growing demand and identify opportunities for operational improvement. MGT4924 Assignment instructions… The assessment consists of three related tasks. Task 1 – Arena Simulation Model requires students to develop a valid and functioning discrete-event simulation of the current system using Arena Simulation software. The model must accurately represent customer arrivals, ordering and payment, movement between the service counter and sandwich assembly area, preparation of meat, cheese and vegetarian sandwiches, resource constraints, queues and the possibility of sandwiches being rejected and remade. A commentary of up to 500 words must be included within the model to explain its main components and operation. MGT4924 Assignment instructions… MGT4924 Assignment instructions… The case provides defined operational resources, including three servers, two cash registers, one meat counter, one cheese counter and one vegetarian counter. The same server completes the full customer-service cycle, and customers join a single FIFO queue when both a server and cash register are not immediately available. The simulation must run for one week, equivalent to 168 hours, using multiple replications. MGT4924 Assignment instructions… Task 2 – Simulation Report requires a professional 1,000-word report analysing the system’s performance across the five-month period using results generated from the simulation study. Students must conduct experiments to determine suitable resource levels for each month and provide justified recommendations concerning the number of resources required for optimal operational performance. Tables and figures must be created independently in Excel rather than copied directly from Arena outputs. MGT4924 Assignment instructions… Task 3 – Operations Report requires a 2,000-word critical report examining two operations-management concepts in the context of Sam’s Sandwich Shop and the wider takeaway-food sector. Students analyse how the selected concepts are or could be implemented, identify associated operational problems and impacts, evaluate potential solutions, and consider relevant tools, techniques, methods and emerging technologies that could improve performance and competitive advantage. Two detailed and justified recommendations must be provided for each selected concept. MGT4924 Assignment instructions… Overall, the portfolio integrates discrete-event simulation, process analysis, resource optimisation, operational performance measurement and evidence-based operations-management decision making. Strong submissions are expected to move beyond simply reporting simulation outputs by critically analysing performance, evaluating experimental results, presenting high-quality data visualisations and producing logical, well-justified operational recommendations. MGT4924 Assignment instructions…
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Business Ethics
2,500 words
Success Through Business Ethics – Ethical Analysis of an FMCG Brand
This assessment is an individual written report for the Success Through Business Ethics module. The assignment requires students to critically examine ethical issues and challenges within a Fast Moving Consumer Goods (FMCG) brand and evaluate how ethical theories, decision-making approaches and leadership practices can be applied to understand unethical business behaviour. The report has a total word limit of 2,500 words and accounts for 100% of the assessment. Students are required to select an FMCG brand that has been involved in unethical practices between 2000 and 2025. The selected brand may originate or operate anywhere in the world and may still be operating or may have ceased operations. Students must ensure that sufficient information is available about the organisation and its external environment before selecting the company. The report begins with an introduction explaining the nature of business ethics and introducing the selected FMCG brand and the unethical business practices associated with it. The main analysis requires students to select three normative ethical theories, explain their key principles and critically evaluate whether the company's behaviour adhered to or violated those principles. Students must use this analysis to explain why the identified business practices can be considered unethical. A second analytical section requires students to choose either ethical decision-making or social accounting. The selected concept must be explained and then critically applied to the unethical practices of the chosen FMCG brand. A further analysis focuses on the leadership style of the company's leadership team. Students must explain the principles of the leadership style, consider its positive and negative aspects, and analyse how the leaders responded to the unethical business practices. The report concludes by summarising the key findings from the analysis and providing two important recommendations for the selected brand. The recommendations should be directly relevant to the unethical practices identified and should be justified using evidence and findings from the report. Where a selected company has ceased operating, recommendations should still be provided on the assumption that the brand is operating. The assessment develops students' ability to apply ethical frameworks to business decision-making, understand ethical decision-making and corporate social responsibility, align ethics and values with business contexts, analyse ethical challenges in business strategies and operations, and evaluate moral dilemmas using economic, legal and ethical considerations. The report must use Harvard referencing for in-text citations and the reference list, with the reference list organised alphabetically.
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Engineering and Environment Advanced Practice
3,000 words
AP Research Project – Reflective Individual Report and Poster Presentation
This assessment is the Advanced Practice (AP) Research Project for the Engineering and Environment Advanced Practice London Campus Research Project module. It is designed for postgraduate students undertaking an independent research project with supervisory guidance. The assessment provides an opportunity to reflect critically on the research process, knowledge gained, professional development and challenges encountered during the project. Students are expected to demonstrate independent learning, research skills, critical thinking and the ability to communicate complex ideas in a professional context. The main assessment is a 3,000-word Reflective Individual Report, which contributes 80% of the module assessment. The report is structured around four key areas. The first section, Finding a Research Topic, requires students to explain how and why they selected their research topic, the research tools and search strategies used, the library collections explored, their research aims and objectives, and relevant discussions or recommendations received from their supervisor. The second section, Professional Activities, focuses on reflection on research development activities, scheduling, the independent researcher role, challenges such as time management, and communication with the supervisor. The third section, Literature Review and Research Work, requires students to critically review relevant literature, discuss their research method, demonstrate major research activities and consider the feasibility of the selected method, including challenges and recommendations. The final section, Reflection of Research Project, requires critical reflection on personal strengths and weaknesses, continuous self-development and the relevance of research activities to the student's programme of study and future career. Areas such as decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity should be considered. The second component is a 10-minute Poster Presentation worth 20% of the assessment. The poster should provide a balanced combination of visuals and text and present the research objectives and significance, a summary of the literature review and research method, key research findings, and conclusions and recommendations. The assessment also requires appropriate academic presentation, including a cover page, table of contents, page numbers, figure and table captions, numbered headings and consistent formatting. Harvard or APA referencing may be used. The report is submitted through Turnitin and is subject to anonymous marking. The assessment is a Pass/Fail module, with students required to achieve at least 50% to pass.
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Engineering and Environment Advanced Practice London Campus Research Project
3,000 words
LD7152 – AP Research Project
The LD7152 AP Research Project is a postgraduate research and reflective assessment for students studying within the MSc Computing Framework or MSc International Project Management programme. The module focuses on developing students as independent researchers and requires them to reflect critically on their research strategy, activities, learning and personal development. The assessment is designed to encourage students to recognise their achievements, identify challenges encountered during the research process and evaluate how their research experience has contributed to their academic and professional development. The assessment consists of two components: a 3,000-word Reflective Individual Report worth 80% and a 10-minute Poster Presentation worth 20%. Students are expected to work independently while receiving supervisory guidance. The reflective report requires students to demonstrate critical engagement with knowledge discovery and reflect on their educational development in relation to the challenges experienced during their research project. The report is structured around four main areas. The first section, Finding a Research Topic, is approximately 500 words and requires students to explain their chosen topic, why it was selected, the research tools and search techniques used, the Library collections explored, the research aims and objectives, and relevant discussions or recommendations from their supervisor. The second section, Professional Activities, is approximately 500 words and focuses on research development tasks, scheduling, the independent researcher role, challenges such as time management, and communication with the supervisor. The third section, Literature Review and Research Work, is approximately 1,500 words. It requires a critical review of relevant literature with appropriate citation, discussion of the research method and major research activities, and consideration of the feasibility, challenges and recommendations associated with the selected method. The fourth section, Reflection of Research Project, is approximately 500 words and focuses on achievements, contributions, strengths and weaknesses, continuous self-development and employability. Students are expected to reflect on areas including decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity. The second assessment component is a 10-minute poster presentation. The poster should provide an overview of the research activities and communicate what the student learned during the research process. It should balance visual and textual information and include the research objectives and significance, a summary of the literature review and research method, key research findings, and conclusions and recommendations. The report must include a cover page, table of contents, page numbers and captions for figures and tables. The required formatting includes Times New Roman, 12-point font, numbered headings and approximately 1.2–1.3 line spacing. The assessment brief permits Harvard or APA referencing. The report is submitted electronically through Turnitin on Blackboard. The module is assessed on a Pass/Fail basis, with students required to achieve 50% or above to pass. The assessment also evaluates critical reflection, application of knowledge, independent learning, communication of complex ideas, personal development and reflection on technical leadership.
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Principles of Data Science
3,000 words
Principles of Data Science – Predictive Modelling and Data Analysis
This individual assessment for the Principles of Data Science module requires students to select, apply and critically evaluate data science methods, tools and techniques using one of three provided datasets and its associated scenario. The main assessment takes the form of a 3,000-word report in which students explore their chosen dataset, identify an appropriate predictive modelling approach, build and evaluate models, interpret the findings and critically reflect on the overall process and outcomes. The assessment addresses the principles and foundations of data science, statistical methods, data preparation, visualisation, predictive modelling, decision making and the critical evaluation of data science techniques and tools. Students begin by exploring the selected dataset to understand its structure, characteristics and limitations. Although the supplied datasets have already been cleaned, students may undertake additional data preparation or transformation where necessary. Any preprocessing decisions must be justified in relation to the requirements of the selected analytical methods. Feature selection should also be considered as part of preparing the data for model development. The assessment requires students to identify suitable forms of analysis for the selected scenario and justify their choice of methods. At least two different techniques must be used to develop models with predictive capacity for the response variable in the chosen dataset. The models must be trained and tested consistently, using the same training and test datasets so that their performance can be compared fairly. Where appropriate, students should also provide insight into feature importance and explain the contribution of relevant variables to predictive performance. Model performance must be evaluated using suitable metrics, followed by a clear description of the findings and recommendations appropriate for the intended audience. The report should document the complete analytical workflow, including data exploration, preprocessing, feature selection, model development, testing and evaluation. Students are expected to explain and justify the decisions made throughout the process rather than simply presenting code or model results. The assessment also requires students to demonstrate practical proficiency in data science tools and techniques. The brief expects the use of R for completing the assignment and requires evidence of important elements of the code, although the complete code does not need to be submitted. Data visualisation must be used to support the written discussion and communicate relevant findings effectively. The assessment is evaluated across theoretical knowledge and method selection, data exploration and processing, technical application and model evaluation, communication of findings, and overall presentation and referencing. The assessment therefore combines technical implementation with critical analysis, requiring students to explain why particular methods were selected, evaluate their effectiveness and consider the limitations and implications of the resulting findings. A separate second assessment component accompanies the written report. This component requires a presentation of the key findings from the written work using a maximum of five slides and a presentation duration of no more than seven minutes. It should summarise the dataset, methods, key findings and project outcomes while providing critical reflective commentary on lessons learned, factors affecting success and potential real-world applications.
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Data Management / Business Analytics
2,500 words
Data and Decision Making (BS776) — Business Report: Two-Source Data Analysis in Python for Evidence-Based Decision-Making
This Level 7 report applies data management theory to a self-selected industry problem and carries it through to a working Python analysis and a defensible business recommendation. The brief is deliberately open on sector — finance, healthcare, transport, cyber security, business intelligence and others are all permitted — but firm on one point: the chosen topic must carry a genuine business implication rather than being a purely technical or clinical analysis. The work therefore begins by framing a specific data-driven decision the organisation needs to make, and returns to that decision at every stage. Two distinct data sources are then identified from approved open repositories and critically evaluated side by side. The evaluation covers the data types each holds, how the data was collected and what bias that introduces, how each is stored and managed, and where the weaknesses lie — proposing concrete data management solutions for the problems identified rather than simply cataloguing them. The analytical core examines, transforms and explores both datasets using univariate and multivariate techniques. All work is carried out in Python within Google Colab, with full screenshots of the code and outputs placed in the appendices and the live Colab link shared for verification. Charts and tables sit in the main body where they support interpretation, each labelled and referenced back to its data source, and each appendix is cited from the narrative so the reader can move between argument and evidence. Data cleaning and transformation steps are shown and justified, not glossed. Findings are reported at length and converted into a clear recommendation covering both the immediate decision and the current and future direction of data management for the business. The limitations section is written honestly — sample coverage, data recency, the assumptions the transformation forced, and what the proposed solution cannot address. Running alongside this, the module's weekly consolidation discussions are evidenced. Five or more critical responses across units two to nine are screenshotted, dated, individually labelled as appendices, and each supported by academic and practice references. Crucially, these are not left sitting in the appendix: they are cited and used within the main body to support the critical discussion, which is where the marks for that component sit. The report follows the prescribed structure — title page, executive summary, contents, introduction, main section with subsections per task, findings, recommendations, limitations, conclusion, Harvard reference list and full appendices — submitted as a single file.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau
This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making
This Level 7 portfolio accumulates work across a full semester of data visualisation study and is submitted as one continuous document combining reflective writing, tool evaluation and an applied dashboard project. The emphasis throughout falls on interpretative rigour — showing not just that a visualisation was built, but why each design decision serves the decision-maker who has to act on it. The first activity documents sustained critical participation across the module's weekly discussion topics. Five or more substantive responses are evidenced with dated screenshots, each paired with the corresponding workshop date, and each grounded in both academic literature and practitioner sources rather than opinion alone. Live URLs to the Power BI and Tableau dashboards built during the module are included, and the activity closes with a consolidated review of how understanding developed across the units. The second activity covers formal training completion in both platforms, with certificates and badges reproduced, and then turns to accessibility. It examines the visualisations produced during that training against accessibility principles — colour contrast and colour-blind safe palettes, text alternatives, chart type legibility, cognitive load — and argues why accessibility is not a compliance afterthought but a determinant of whether a dashboard actually informs decisions inside a global organisation with a diverse user base. The third activity is a structured comparison of Tableau and Power BI, conducted through the evaluative frameworks introduced in workshops rather than a feature checklist. Benefits and limitations are weighed across data connectivity, calculation capability, visual flexibility, licensing and cost, collaboration and governance, and organisational fit. The section ends with a reasoned recommendation of one platform for the applied work that follows, with the trade-offs of that choice acknowledged openly. The fourth and largest activity is the applied project. A dataset is selected from an approved open source and its provenance, quality and limitations discussed. The target business, its stakeholder groups and the varying analytical literacy of the intended audience are analysed, because these determine what the dashboard must show and how plainly. The report then walks through the full development sequence — data preparation and cleaning, chart selection and justification, layout and interactivity, iteration in response to identified weaknesses — with screenshots evidencing each stage. Findings are reported and translated into specific, evidence-based decisions the business could take on the strength of them. The submission follows the prescribed structure with title page, contents, introduction, business problem, main analysis, findings, Harvard reference list and appendices, presented as a single file.
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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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