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Assessment Descriptor

Module: ITAO7109 – Analytics with Artificial Intelligence

Semester: Resit

Assignment: Individual Report on Country-Level Performance

Submission: before  1^st August 2025 23:59 hrs

Word count: 3,000 (excluding references and appendices)

P.S. This module is assessed 100% by an individual written project. Please also note that the dataset that has been given to be used for this assessment is based on an available dataset downloaded for Educational purposes from a well-known source, Kaggle. The module teaching team does not own the dataset and hence, cannot comment on the accuracy of the data or be held liable for any misinformation that might be presented in the dataset. This is only to be used for educational purposes.

You have been hired as a business consultant by a retail giant that wants to expand its business not just across Europe, but also in other continents. However, any such global expansion has a wide variety of challenges/implications that need to be addressed by proper risk-benefit planning. This requires a thorough assessment of country-level performance in terms of global economic factors. The dataset provided has many such variables with a vast pool of data. Based on this dataset, Students are asked to produce a report of 3000 words (+/- 10%, excluding appendices and reference list) based on the following information.

Assessment Task

The technical and written tasks that you should carry out are detailed below:

Part 1: Analysis (50%)

Part 1a (20% of the overall Marks):

Using the relevant software, carry out an exploratory analysis of the data. You should produce at least five visualisations and a country-level performance for senior supply chain managers, and potentially for other business users. Include any relevant visualisations in your report. You can use any of the tools that have been introduced during class, e.g., Tableau, Power BI, and/or any AI-based tools.

Part 1b (30% of the overall Marks):

After the initial stage, the company would like to explore the use of predictive analytics to help the organisation to examine how the country’s economy is performing based on various indicators and where the country will be in the future in terms of its economic performance so that they can plan on business expansion/development decisions. To do this, they would like you to build a model to predict country performance. They would also like to gain insights into the factors that may be contributing to the county’s economic performance and what they should do about it. You can use any of the tools that have been introduced during class, e.g., KNIME, and/or any AI-based tools.

Part 2: Discussion  (50% of the overall marks)

The top management is more interested in the story/narrative around the results rather than the models/numbers. Furthermore, they would like to develop some in-house capacity for the future. Hence, they are also expecting to get insight into your reflection on the choice of models, the prompt used, if any generative AI-based tools are used, etc. Thus, in this part of this report, you need to create a professional narrative discussing your reflection on the tools selected, the prompts used, the logical deduction of a particular model choice, and the interpretation of the results. Support your arguments with relevant academic literature where applicable.

After conducting these analyses, you will submit a written report.

The maximum word count for the assignment is 3000 words (excluding tables, figures, references, and appendices). Students will be penalized for exceeding the word count by more than 10%. Harvard referencing style should be used. Students are required to submit the assignment via CANVAS by 11:59 pm on 1^st August 2025. Students must submit the written report along with screenshots/exported images of the dashboard, visualisations, and outputs from various tools.

The written report should cover the following areas (but is not limited to these):

1. Background Introduction: Provide an overview of the business problem. Where appropriate, include references to the wider literature.

2. Analysis and Results: Present the details of Part 1A and 1B

3. Discussion and Concluding Remarks:

a) Discuss the implications, reflections, and relevant issues pertinent to the model selections in this section.

b) Recommendations:

· Drawing on the analysis and wider literature, provide recommendations that the company could take to expand into the global market.

· Drawing on the wider literature, summarise the benefits and limitations of advanced analytics, and recommend a project for follow-up work.

4. List of References: This should be listed following the Harvard style. Please refer to the University’s library website for more information on Harvard Referencing.

5. Appendices: In the appendices, you may choose to include some of the software outputs/screenshots. If you choose to furnish your screenshots here, you need to signpost in your main report where to find those.

In addition, the individual assignment will be assessed using the postgraduate conceptual marking scale as recommended by the University (as outlined in Appendix 1 and the Queen’s Management School Postgraduate Student Handbook for further information).

The following criteria are also considered when assessing the assignment:

· Demonstrate a wide reading and understanding of the assignment task

· Ability to synthesise and critically evaluate relevant material

· Quality and relevance of evidence/example presented to support position/claims

· Structure including planning, organising, flow, and coherence

· References – quality of citations and correct style used

· Overall presentation

Please note that the School has a number of policies governing the submission of student work. For all elements of assessment associated with this course, you must be familiar with the School’s policies on:

· ‘Participation, Preparation for Classes and Private Study’;

· ‘Preparation and Submission of Assessed Work’; and

· ‘Plagiarism, Collusion and Fabrication’.

These policies are detailed in the Queen’s Business School Postgraduate Student Handbook.

Appendix 1: Conceptual Equivalents Scale Postgraduate