MN5817 Cloud Computing for Business Assignment: Report + Artefact

Contents

1The Assignment
1.1Assignment Checklist
1.2Selecting a Real-world scenario
1.3Evidence of Analysis
1.4Cloud Visualisations
1.5Development Diary
1.6Risk of Relying on AI Tools
2Writing The Report
2.1Length, File Name and Text Format
2.2Report Structure
2.3Presentation and Captions
2.4Referencing and Citing
2.4.1Referenceable Material Types
2.4.2Credible Sources
2.5Important Restrictions
2.6Writing Style
2.7Report Presentation
3Assignment Rubric
Declaration of AI Tool Usage
Glossary

1 The Assignment

This assignment is a cloud computing project requiring you to design and build an Extract, Transform and Learn (ETL) process on cloud. In addition, you are required to compose a business report of approximately 2,000 words. This report must communicate and justify the thought processes and assumptions behind the development of your ETL process as well as discuss the insights and recommendations they produce. In this, you will create a dummy database. Use any cloud platform’s data factory to extract data from the SQL Server and load it into storage. Perform minor transformations using Python or, alternatively, utilise Alteryx Designer. Discuss identity and security management throughout the project.

The final submissions must be:

• A Paper Artefact (ETL processing on any cloud platform of your choice) that is, the final versions of the models you created. It is important that your diagrams are well annotated with comments that help the marker understand your logic and process. If you are able, you are free to attempt a full prototype.
• The Business Report, that is, your thought processes and assumptions when developing the ETL, as well as discussing the insights and recommendations they produce.

You are also required to maintain a Project Development Diary (see Section 1.4), which must be included as an appendix of your report.

1.1 Assignment Checklist

The checklist below should be used to ensure that your assignment is within the remit.

1. Set up cloud Services: Verify access to SQL Server, Data Factory, Data Lake, and the analysis portal on your cloud platform.
2. Create Dummy Database: Initialize a dummy database in your cloud’s SQL Server with sample data.
3. Configure ETL Pipeline: Use your cloud’s data factory to establish pipelines for data extraction from SQL Server and loading into a cloud data lake.
4. Data Transformation: Define tasks for data transformation in your cloud platform or Alteryx Designer. Specify the use of Python coding or Alteryx workflows.
5. Security and Identity Management: Implement security measures and identity management within your chosen cloud services. Include Cloud Active Directory configurations and role-based access controls.

2.Initial Data Setup

• Choose a dataset that reflects a realistic business scenario for the dummy database in your cloud’s SQL Server.
• Outline transformation tasks to be performed in on cloud or Alteryx Designer. These tasks should demonstrate meaningful data manipulation, cleaning, and aggregation.

3.Detailed Analysis and Report Drafting.

• Conduct a more comprehensive data analysis.
• Refine your data story and enhance ETL process and identity and secure monitoring, for clarity and impact.
• Further refine your business report based on the more comprehensive process with an analysis you have carried out.
• Maintain your Project Development Diary.

4.Complete Draft and Preliminary Review.

Complete a full draft of the report, including insights.

• Conduct a self-review and refine the draft for coherence and narrative flow.
• Maintain your Project Development Diary.

5.Project Completion and Final Submission.

• Perform final checks on the report, ensuring that all the requirements are met.
• Compile and review the appendices and analytical models.
• Complete and incorporate your Project Development Diary into your final report as an appendix.
• Submit your final project report and final versions of your analytical models via Moodle (TurnitIn)
  by 12pm 9th May 2024

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1.2Selecting a Real-world scenario

Choose any product/service real-world scenario and create dummy or use primary datasets chosen for your project are subject to your tutor’s approval. All data sources used and outputs created must comply with ethical standards. It is expected that all real-world scenario and data source combinations will be unique to each student.

This checklist can be used to methodically evaluate the feasibility and applicability of your chosen real- world scenario . This process will help you critically assess your proposed project, ensuring that it is fully aligned with the goals of the assignment and the broader MSc Digital Business programme.

1.Relevance to Business Analytics

• Confirm the real-world scenario addresses a meaningful problem or opportunity.
• Validate that the real-world scenario allows for cloud computing in a real-world scenario

2.Feasibility

• Evaluate resource availability, including time, data, and tools (Alteryx, Azure equivalent).
• Assess whether the project’s scope is realistic within the given timeframe.

3.Intellectual Challenge

• Choose a real-world scenario that requires the detailing of how you have understood cloud computing skills.
• Ensure the scenario demands critical thinking and problem-solving.

4.Sustained Engagement

• Select a real-world scenario that you are passionate about, and will maintain your interest.
• Consider the real-world scenario ‘s relevance to personal and professional growth.

5.Balance of Theory and Practice

• Check that the real-world scenario offers opportunities to apply theoretical concepts.
• Plan for practical application and real-world testing of these concepts.

6.Accessibility of Information

• Identify all required data sources and confirm access.
• Obtain necessary permissions for proprietary or sensitive data. (NB. All formal permissions must be included as appendices to your final business report).

7.Skill and Knowledge Requirements

• Map out the skills and knowledge needed for the project.
• Plan for acquiring any skills or knowledge you currently lack.

8.Uniqueness

• Register your real-world scenario and core data sources to confirm their uniqueness.
  Selecting a well-aligned and feasible real-world scenario sets the foundation for a meaningful and impactful learning experience, allowing you to demonstrate the breadth of your analytical and consulting capabilities.

1.3 Evidence of Analysis

Your final report must provide comprehensive evidence of your design and building ETL journey. This evidence demonstrates your proficiency in applying the skills and knowledge acquired during your course to a commissioned or self-selected business or societal issue. Below is a detailed breakdown of the essential activities and tasks that must be completed and documented:

1.Dataset Selection and Preparation

• Choosing Datasets: Select your primary datasets that may hold real-world scenario al business or societal value.
• Document the real-world scenario and datasets you have selected (see Section 1.2).
• Data Preparation: Clean, complete, augment, format, and explore the data to prepare for thorough analysis. Ensure that your datasets are sufficiently robust to support your aims, providing the necessary depth and breadth.

2.Analysis and Interpretation

• Employing Analytical Tools: Use any cloud platform’s analytical tool like Azure’s Databricks or Alteryx with your choice of data to perform your data analyses. Your report must showcase your ability to effectively develop and employ cloud models.
• Narrating Data Stories: Develop and narrate cohesive data stories that clearly articulate the business problem or opportunity addressed. Your narrative should align with the objectives of the MN5816 module and your academic interests.
• Data Visualisation: Create compelling and contextually relevant visualisations your process clearly and engagingly (see Section Error! Reference source not found.).

3.Insights and Recommendations

• Insight Identification: Document how you identified and discussed the insights gained from your analysis. What stories did the data reveal? What the data reveals about the problem or opportunity you are exploring?
• Strategic Recommendations: Offer tactical and strategic recommendations based on narrated data stories and insights. Your conclusions should provide actionable and insightful suggestions stemming from your thorough analysis.

4.Proficiency and Compliance

• Demonstrating Proficiency: Throughout the project, demonstrate proficiency in the software, tools, techniques, and skills taught during the course. If undertaking a commissioned project, adhere to any stipulations set by the commissioning organisation.
• Ethical and Appropriate Use of Artificial Intelligence (AI): If you opt to use AI tools for any part of your project, your report must include an explicit declaration of AI usage as an appendix. You must clearly articulate and reference any AI tool used in your report. Demonstrate that you have not breached the academic standards for Generative AI.

5.Documentation of Process

• Articulating Assumptions and Justifying Choices: Clearly articulate all assumptions made and explain each decision made along with its justification and reasoning.
• Addressing Challenges: Document each challenge experienced and how it was overcome or worked around.
• Other Crucial Information: Include any other information that provides context, explanation, and/or clarification relevant to your project.
  Your final report should not just detail the tasks completed but also reflect your analytical skills, your ability to weave a viable and compelling data story, your communication skills (both verbal and visual), your creativity, and your journey as a business analyst.

1.4 Cloud Visualisations

Visualising your cloud business is an essential aspect of your report, playing a crucial role in clarifying, supporting, and highlighting your analysis of your work. Rather than serving as mere additions, visual elements should be integrated throughout your report as key narrative tools. They turn complex data into clear, engaging insights. Thoughtfully placed visualisations within your report enhance comprehension and effectively engage the reader. Consider the following suggestions for strategically embedding data visualisations in various sections of your report:

1.Introduction:

• Generally, visualisations in the introduction are minimal. However, a simple, high-level visual might be used to set the scene or present the context of your research, but only if relevant.

2.Trends and Theoretical Perspectives:

• Trends: Use charts or graphs to depict any significant trends or patterns you have identified related to your real-world scenario . Visuals here can help illustrate shifts or developments over time.
• Theoretical Perspectives: While this section is generally text-heavy, conceptual diagrams or models related to the theoretical frameworks discussed can be included to aid understanding.

3.Analytical Framework and Method:

• Method Overview: Flowcharts or diagrams can be useful to outline your analytical process or framework, providing a visual summary of the steps taken and the methods used.

4.The Data:

• Dataset Overview: Incorporate visuals such as sample data snapshots, infographics, or simple charts to represent the structure, source, and nature of the data.
• Data Quality and Preparation: Before-and-after visuals can effectively show cleaning, transformation, or imputation changes in your data.

5.The Analysis – This section is the heart of the paper portfolio and diagrams in your report:

• Descriptive Analysis: Utilise Azure Databricks notebooks to perform and visualise the descriptive analysis of the data post-transformation. Present the distribution, trends, and relationships within your dataset using visualisations like bar charts, line graphs, histograms, and scatter plots. For geographical data, consider using choropleth maps to display spatial distributions. Azure Databricks’ built-in visualisation tools or integration with other software (like Power BI) can enrich this section.
• Predictive Analysis: If your project scope includes predictive modeling, use any platform to create models and showcase their summaries. Visualise model performance with ROC curves or precision-recall curves. Predictive outcomes can be juxtaposed against actual values through scatter plots or line charts to assess the model’s accuracy.
• Prescriptive Analysis: When your data analysis suggests potential actions or solutions, illustrate these recommendations with decision trees or flow diagrams created in Azure Databricks or Alteryx Designer. Scenario visualisations can be particularly effective here, helping to conceptualise different outcomes based on the prescribed actions.
• Findings: This part of your report should emphasize the critical insights derived from your ETL process and subsequent analyses. Use compelling visualisations to highlight these insights,ensuring that the significance of your findings is immediately apparent to the reader. For instance, if the data reveals significant trends or patterns, a well-designed infographic can succinctly communicate these discoveries.
• Recommendations: Based on your findings, provide recommendations for actionable strategies or further analyses. Visual tools can be particularly effective in this section to illustrate your proposals. For example, use flowcharts to outline the steps for implementation of a strategy, or employ diagrams to model the potential impacts of your recommendations. If your project involves decision-making under uncertainty, scenario analysis visualisations can offer a comparative view of different strategies’ outcomes.

6.Conclusion:

• While the conclusion is typically a summary text, a powerful final visual (like an infographic summarising the key findings or a diagram of the proposed recommendation framework) can leave a lasting impression.
  Ensure that each visualisation has a clear purpose, is referenced and explained in the text, and is well- labelled and accessible. Include an interpretation linking them to the narrative of your report In your Development Diary; note instances where a visualisation clarified your understanding or when a specific chart or graph effectively presented your data, adding depth to your analytical journey’s narrative.

1.5 Development Diary

The Development Diary is an essential element of this project, functioning as a personal chronicle of your journey. It is designed to capture pivotal moments, critical decisions, fundamental assumptions, and any challenges encountered along the way. This diary is your space to meticulously record every assumption you make, every decision and choice along with their justifications and reasoning, every obstacle faced, and how you navigated or circumvented it.

What to include:

• Document the building and troubleshooting process of the ETL pipeline, highlighting key decisions, challenges, and solutions.
• Reflect on the design choices for the ETL pipeline, data transformation logic, and security configurations.

Below is a table outlining the types of entries you might make, with clear explanations and relevant examples to guide you in chronicling your project experience.

Remember that the more detailed and consistent your entries are, the more valuable the Development Diary will be as a resource for understanding and improving your project approach. Your diary entries are likely to be simple notes written in the first person. These entries are only for your own use, and should not be repeated verbatim when writing your report.

A template for the Development Diary will be available via Moodle.

1.6 Risk of Relying on AI Tools

Artificial Intelligence (AI) tools can aid in productivity and creativity; however, they are not without risks. The most obvious risk is the potential breach of academic integrity standards (see the Generative AI page on the College’s website).

In addition to these concerns, using AI to generate academic or business content comes with significant risks, notably the phenomenon known as “hallucination” where the AI fabricates information or presents false data. This can be particularly problematic in academic and business contexts where accuracy and credibility are paramount. AI systems, although sophisticated, do not discern truth from fiction; they generate responses based on patterns in the data on which they have been trained. This means that AI can confidently present incorrect or misleading information as factual. The likelihood of this occurrence is not trivial, particularly when the real-world scenario s are obscure, complex, or outside the AI training range.

In academic settings, reliance on AI-generated content can lead to the submission of work that contains inaccuracies, potentially undermining the integrity and credibility of your work. In the business world, decisions based on incorrect information can have severe financial and reputational consequences.

Furthermore, the uncritical use of AI content can lead to the homogenisation of thought, as diverse perspectives and critical thinking are sidelined in favour of AI’s often predictable outputs.

Therefore, while AI can be a powerful tool to generate ideas and help with narrative flow, it is crucial for users to critically evaluate and verify information, understand inherent risks, and take responsibility for the final content. This approach ensures that the final output is not only original, but also accurate and reliable, maintaining the integrity of academic or business endeavours.

If you have ethically employed any AI tool for your analysis, you must declare which tool you used (name and version), why and how it was used, and the specific content in your report that was developed with the aid of AI tools. You must provide this information by completing the declaration of AI usage form in this document and including this it as an appendix in your final submission.

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2 Writing The Report

2.1 Length, File Name and Text Format

• The word limit for this report is 2,000 words. References, tables, and figures are not included in the word count.
• Please include your student ID and the total word count on your submission’s cover page.
• Please name your assignment’s electronic file as follows: MN5817_candidate id
• All texts should be Times New Roman, with sizing and style as follows:
  ⁻    Main text  12pt
  ⁻    Heading level 1  14pt bold
  ⁻    Heading level 2  13pt bold
  ⁻    Heading level 3  12pt bold
• The text, not in captions or tables, must be left-justified with 1.5 line spacing.

2.2Report Structure

Your final report should be structured to facilitate a comprehensive and coherent presentation of your work, ensuring that every relevant aspect is articulated effectively. Your Development Diary, a meticulous record of the evolution of your project, will be instrumental in providing the depth and detail required for each section. Below is a detailed breakdown1 of the sections2 expected in the report, with pointers on how your diary entries can enrich your narrative.

1.Introduction, this section covers the following aspects:

• Context and Significance: Describe the background/environment and importance of your chosen real-world scenario. Here, you can use your diary entries, where you explored and justified your choice of real-world scenario.
• Scope and Purpose: Define the boundaries and aims of your project. Here, you can utilise your diary notes on the purpose of your analysis and the questions you sought to answer.
• Assumptions: Make explicit the initial assumptions you made about the real-world scenario or project, as recorded in your Development Diary, and explain how these assumptions shaped the direction and scope of your research.
• Report Structure: Provide a roadmap for the layout of your report.

2.Trends and Theoretical Perspectives, this section should cover:

• Current Trends: Analyse recent developments related to your real-world scenario . Here, you can use your diary entries, where you have noted emerging patterns or shifts in the field.
• Theoretical Foundations: Discuss relevant theories and frameworks. Here, you should consider your diary reflections on key readings and how they shaped your understanding.

1 The bullets under each sections provide a checklist of what should be covered, and do NOT suggest subsections that should be included.

2 A report of this length will have sections, NOT chapters.

• Critical Understanding: Critically discuss the various theoretical perspectives you have considered. Here, your diary insights, where you have critiqued or supported various theoretical perspectives, will help you shape a deeper analysis.
• Assumption: Examine how your theoretical perspective may have been influenced by the assumptions noted in your diary. Consider whether and how these assumptions were challenged or changed based on the research findings.

3.Analytical Framework and Method, this section should cover:

• Analytical Approach: Detail the methodologies you have employed. Here, you can use diary entries about your decision-making process and any methodological adjustments.
• Scope and Focus: Clarify the extent and concentration of your analysis. Your diary extracts highlighting how and why you narrowed your focus should be of help.
• Ethical Considerations: Discuss the ethical aspects of your work. Your diary records of ethical dilemmas and how you addressed them will be helpful.
• Assumptions: Discuss any assumptions that underpin your choice of methodology. Use your diary to reflect on how these have been validated, challenged, or modified throughout your analytical journey.

4.The Data, this section should cover:

• Dataset Description: Describe the dummy dataset/real used. Your diary entries on how you selected and sourced your datasets will help you.
• Relevance and Preparation: Explain the significance and steps taken to clean and prepare the data. Your diary notes on any challenges faced and how they were resolved should be of help.
• Rationale: Justify your choices. Here, you can use diary records of where you deliberate on various options and their implications.
• Assumptions: Detail any assumptions made about the data’s validity, relevance, or limitations as documented in your diary. Reflect on how these assumptions were confirmed or adjusted after data collection and analysis.

5.The Analysis, this section should cover:

• Analytical Journey: Narrate the progression of your analysis. Here, diary records of key milestones, breakthroughs, or changes in direction should be useful.
• Descriptive (Diagnostic Implied), Predictive, and Prescriptive Analysis: Elaborate on each analytical phase. Here, your diary entries that detail your thought process, the tools used, and the rationale behind each step should be helpful.
• Rationale and Adaptations: Discuss any adjustments made during your analysis. Here, you can use the diary records of your decision making and problem solving to inform your writing.
• Assumptions: Discuss the assumptions inherent in your descriptive (diagnostic implied), predictive, and prescriptive analyses as noted in your diary. Reflect on the impact these assumptions had on your analytical processes and findings.

6.Findings and Recommendations, this section should cover:

• Interpreted Data Story: Present the narrative derived from your data. Your diary moments where certain insights came to light or your understanding should help you here.
• Summarised Insights: Collate key findings. Here, you could draw on the diary entries that captured your moments of realisation or synthesis.
• Actionable Recommendations: Propose recommendations. This is where diary reflections on potential solutions or strategies would be useful.
• Assumptions: Explore how the initial assumptions were challenged or upheld based on your findings. Use diary entries to discuss how the evolution of your assumptions influenced your recommendations.

7.Conclusion, this section should cover:

• Summary of Key Findings: Concisely recap your main discoveries and arguments. This is where reflection in your diary encapsulating the essence of your project can be useful.
• Analytical Journey: Reflect on the overall process. Here, you can use diary moments that highlight your growth, your analytical processes, the challenges you faced, and how they were overcome.
• Final Takeaways: Emphasise the central messages you wish the reader to remember, inspired by diary entries where you identify your project’s most impactful aspects.
• Assumptions: Summarise how your assumptions were tested and evolved throughout the project, as recorded in your diary. Discuss the role these assumptions played in shaping the final conclusions and takeaways of your report.
• DO NOT INCLUDE NEW INFORMATION HERE: This section should NOT include anything that was not covered in the report. This is the conclusion (i.e. the final section) of your report, NOT your ‘conclusions’.

2.4.1 Referenceable Material Types

This table outlines the types of content that typically require citations and references along with definitions and general examples.

Referenceable Material Types	Definition	Example
Data and Statistics	Quantitative information used to support arguments.	In an essay on climate change, citing the percentage of sea level rise over the past decade.
Direct Quotes	Verbatim excerpts from another person’s work.	In a study on leadership, quoting a famous leader’s statement on effective leadership.
Paraphrased Ideas	Ideas from another source, rewritten in the writer’s own words.	Discussing a psychologist’s theory on human motivation, rephrased in the essay.
Theories and Models	Established theories or models developed by others.	Referencing Maslow’s Hierarchy of Needs in an essay about employee motivation.
Historical Facts	Specific events, dates, or figures from history.	Citing the date of a significant historical event, like the signing of a peace treaty.
Legal or Official Documents	Content from laws, regulations, or official reports.	Referring to a clause in a human rights legislation in a law essay.
Research Findings	Results or conclusions from scientific or academic studies.	Mentioning the findings of a recent study on diet and health in a nutritional science essay.
Artistic and Literary Works	References to books, artworks, music, and other creative works.	Discussing themes from a classic novel in a literature essay.
Factual Assertions	Claims or statements that present specific, verifiable information.	Stating the boiling point of water in a scientific essay.

2.4.2 Credible Sources

In academic writing, particularly in UK universities, credible sources offer reliability, accuracy, and authority in their information. These sources typically include the following.

1.Peer-Reviewed Journal Articles: Articles published in peer-reviewed journals have been evaluated and critiqued by experts in the field. This process ensures the research is of high quality and the findings are credible.
2.Books Published by Academic Presses: Books published by university presses or recognised academic publishers are generally considered credible, as they undergo rigorous editorial processes.
3.Official Publications and Reports: Documents released by government agencies, international organisations (such as the UN or WHO), or major research institutions are usually reliable. These include policy documents, white papers, and statistical reports.
4.Academic Conference Papers: Papers presented at academic conferences are often peer-reviewed and are considered a good source of up-to-date research.
5.Theses and Dissertations: These are detailed studies conducted by students at the master’s or doctoral level and can be good sources of specialised information.
6.Authoritative Databases and Online Journals: Databases, such as JSTOR, PubMed, and Google Scholar, provide access to a wide range of peer-reviewed journal articles and academic publications.
7.Educational and Government Websites (.edu, .gov, .ac.uk): Websites ending in .edu, .gov, or .ac.uk often contain reliable information, especially if they are linked to known educational institutions or government bodies.
8.Reputable News Outlets: While not as authoritative as academic journals or books, reputable news sources like the BBC, The Guardian, or The Times can provide current information and are often used to support arguments or provide context.
9.Research Institute Publications: Research institutes often publish reports and papers that are credible and detailed, contributing significantly to their field of study.

It is important to critically evaluate each source by considering the author’s credentials, publication date, publisher reputation, and objectivity of the content. The source should be relevant to the real-world scenario and offer depth to the academic work.

2.7 Report Presentation

For postgraduate students, the presentation of a report is not just about providing information; it is a critical aspect of their academic and professional development. An effectively presented report can significantly enhance the impact and credibility of your research, demonstrating your ability to synthesise complex information and communicate it clearly and persuasively.

The following are some reasons why the presentation is so important.

1.Clarity of Communication: Postgraduate studies often deal with complex and nuanced real-world scenario s. Thus, the ability to present these intricacies in an accessible manner is crucial. A well- presented report helps ensure that your audience, whether academic peers, supervisors, or industry professionals, can easily understand and engage with your findings.
2.Professionalism: The standard of your report presentation reflects your professional image. Attention to detail, coherent structures, and clear visuals indicate a high level of professionalism and dedication to your work. This is particularly important for postgraduate students preparing to enter or advance their professional career.
3.Persuasive Argumentation: A key goal of any report is to persuade the reader of the validity of your argument. The presentation of your report plays a vital role in this process. A logically structured report with well-presented evidence and clear narrative is much more likely to convince the reader of your conclusions.
4.Impact and Engagement: The way you present your report can significantly affect how engaging it is. Reports that are visually appealing and easy to navigate are more likely to hold the reader’s attention, making them more impactful.
5.Demonstrating Skills: As a postgraduate student, you are expected to develop certain skills, including critical thinking, research, and communication. How you present your report provides an opportunity to demonstrate these skills. A well-crafted presentation shows that you can not only conduct high- quality research, but also effectively communicate your findings.
6.Academic Rigour: Good presentation goes hand in hand with academic rigour. Properly citing sources, presenting data accurately, and following academic conventions contribute to the credibility and reliability of your work.

In essence, the presentation is integral to the success of the report. It enhances readability, strengthens arguments, and demonstrates your capabilities as a postgraduate student. Therefore, investing time and effort in how you present your work is just as important as the research itself.

3 Assignment Rubric

MN5817 100% Assignment SPRING 2023-2024		Distinction	First	Second	Pass	FAIL	FAIL	FAIL	FAIL	FAIL
Criteria	Marks	≥ 82	72, 75, 78	62, 65, 68	52, 55, 58	42, 45, 48	35	25	15	0
Critical Discussion of Data  Interpretation (Learning Outcome 1)	15	Exceptional critical discussion with profound insights and application of published sources. Demonstrates deep understanding and analysis.	Strong critical discussion, thoroughly understanding the challenges of data interpretation.	Good level of critical discussion, adequately addressing the challenges in data interpretation.	Satisfactory discussion with basic  understanding and analysis of data interpretation challenges.	Limited discussion, with a superficial understanding of data interpretation challenges. Below pass level.	Minimal engagement with critical  aspects of data interpretation, lacking depth. Well below pass level.	Poor understanding and discussion of data interpretation challenges. Significantly below pass level.	Very poor understanding with almost no relevant discussion. Far below pass level.	No engagement with the critical aspects of data interpretation. Complete lack of understanding.
Data Interpretation  Skills in Business/Societal Context (Learning Outcome 2)	15	Exemplary demonstration of data interpretation skills, applying them innovatively to a realistic business/societal problem.	Strong and effective application of data interpretation skills to a business/societal problem.	Good demonstration of skills with relevant application to a realistic problem.	Adequate application of skills, meeting basic requirements for a business/societal context.	Basic application of skills, with limited relevance to a business/societal problem. Below pass level.	Minimal demonstration of data interpretation skills in context. Well below pass level.	Inadequate application of skills to a business/societal problem. Significantly below pass level.	Very limited skill demonstration, lacking relevance. Far below pass level.	No evidence of data interpretation skills applied in context. Complete lack of application.
Critique and Evaluation of Solutions  to Business Problems (Learning Outcome 3)	15	Exceptional critique and evaluation of solutions, showing deep understanding and analysis of data interpretation impacts.	Strong critique and evaluation skills, demonstrating good understanding of data interpretation effects on solutions.	Good level of critique and evaluation, with a fair understanding of data interpretations’ impact.	Adequate critique and evaluation, meeting  basic requirements of understanding data interpretation impacts.	Basic critique and evaluation with limited insight into data interpretation effects. Below pass level.	Minimal engagement in critiquing  and evaluating solutions. Well below pass level..	Inadequate critique and evaluation, showing significant gaps in understanding. Significantly below pass level.	Poor understanding of how data interpretations affect solutions. Far below pass level.	No evidence of critiquing and evaluating solutions in relation to data interpretations. Complete lack of understanding.
Communication of Data Analysis  Outcomes and developing a cloud project (Learning Outcome 4)	15	Exceptional design and communication skills, making data analysis outcomes highly accessible to a lay audience.	Strong communication, effectively presenting outcomes in an accessible manner.	Good communication, with outcomes mostly accessible to a lay audience.	Adequate communication, meeting the  basic requirements for accessibility.	Basic communication skills, with limited accessibility of outcomes. Below pass level.	Minimal effort in communicating outcomes in an accessible manner. Well below pass level.	Poor communication, with outcomes largely inaccessible. Significantly below pass level.	Very limited communication, with little to no accessibility of outcomes. Far below pass level.	No evidence of communication of data analysis outcomes. Complete lack of communication.
Structure and Style	15	Exceptionally well- structured and lucid report tailored to a business analytics context. Outstanding grammatical precision and readability, reflecting a high academic style.	Very well-organised report with clear, logical flow, suitable for business analytics. Very good grammatical precision enhancing readability.	Good structure and clear presentation appropriate for a business analytics report. Good grammatical accuracy, ensuring readability.	Adequate structure and clarity, appropriate for an academic report in business analytics. Satisfactory grammatical accuracy and readability.	Basic structure and some clarity, but with issues in style appropriate for a business analytics context. Some grammatical errors, slightly affecting readability	Poorly structured and unclear, with  inappropriate style for business analytics. Numerous grammatical errors,  significantly hindering readability.	Very poor structure and clarity, largely inappropriate for a business analytics academic style. Frequent grammatical errors, making the text hard to understand.	Almost no coherent structure or clarity, highly inappropriate for a business analytics context. Extensive grammatical errors, rendering the text almost unreadable.	No coherent structure or academic style, pervasive grammatical errors.
Reading	15	Exceptional breadth and appropriateness of literature, excellently integrated and highly relevant to business analytics.	Very good range of appropriate literature, strongly integrated and relevant to business analytics.	Good breadth of relevant literature, adequately integrated into the business analytics context.	Satisfactory range of literature, with basic integration and relevance to business analytics.	Limited breadth of appropriate literature,  minimal integration and relevance to business analytics.	Poor selection of literature with little relevance to business analytics. Inadequate integration into analysis.	Very poor selection of largely inappropriate literature for business analytics. Little integration and relevance.	Almost no appropriate literature used for business analytics. Negligible integration and relevance.	No appropriate literature used, no integration or relevance to business analytics.
Referencing	10	Exceptional accuracy and consistency in Harvard referencing, with sources demonstrating high academic integrity, crucial for business analytics