Data Visualisation: Information Management and Visualisation Assignment 1 Guide

This post covers Data Visualisation, as detailed below.

Information Management and Visualisation Assignment 1

Introduction

This is an individual assignment. Please read this assignment brief carefully in order to do the task. You need to read a case study ‘Happy Cow Ice Cream: Data-Driven Sales Forecasting’ and analyse and visualise two datasets for your report. The assignment is to be carried individually and carries 30% of the unit marks.

Task

You have been given a briefing document and two datasets from an ice cream shop based in Hong Kong.

Clean up the dataset and format it into a time-series structure (see the Tableau website or help pages if you are unsure what this is). Using a tool of your choice – Tableau is preferred – explore and visualise the dataset to answer the following questions:

1. Explore and visualise the sales performance of the three consumer groups (students, staff and tourists). What recommendations would you make to the business based on this analysis?
2. The CEO of the store believes that different groups of flavours sell better at different times of the year. Does the data back this assumption up?
   • Propose your groupings and visualise them to generate insights into the ice cream sales.
   • Regarding flavour groups, does grouping give a better level of analysis than individual flavours?
3. What other factors affect the sale of ice cream in this store? Consider topics such as time of year, weather, those present on campus.

Submit a written report (no more than 1,000 words) providing visuals and suggested answers to each question. Your report should briefly explain the process that you used to clean and visualise the data, and the choices that you made in designing and selecting your visualisations. Your conclusion should be a set of recommendations (based on evidence) to the company on a strategy to increase sales (be creative!).

Please pay attention to the grading criteria. Please also note the University policy on academic misconduct.

The use of ChatGPT, Bard, or any other AI-tools is allowed in this assignment under specific guidelines and provisions as outlined in this section.

Under the clauses of fair use, proper academic attribution, and unfair means, if you use any AI text-generation solution without properly attributing its use, then you are at risk. Each assignment will be checked through algorithmic AI-detection tools thoroughly, and while such detection is never perfect, it only needs to be good enough on a % of the submitted text. Any suspicion of unfair AI use will be sent to the plagiarism committee for further investigation, and if it reaches a panel, you may be called to explain your work and defend it. Based on algorithmically presented evidence from such software, and on the balance of probability, if plagiarism / unfair use is established, it is highly likely that your mark in the module will be zero. The same could apply if you copy-paste text from other resources without proper attribution and proper referencing. Repeated plagiarism offences can mean that your programme of study is terminated. So, please be extremely careful and diligent in how you approach AI use in the context of text generation.

If you use LLMs like ChatGPT or an AI-graphical generator, then you must:

• Maintain a full record of the entire discussion you have with the AI tool and share it within your assignment. Include the entire AI-discussion that you had within your assignment submission as an Appendix and label it “ AI discussion transcript” – this can be as many pages as you want. It will not count towards the word count.
• Do not use multiple discussion threads. Keep everything in one place and present only one AI discussion transcript.
• The AI discussion transcript that is presented in the Appendix should have numbered pages for ease of reference.
• Since LLMs generate ‘original text’ for fair use, we will treat ChatGPT, Bard, or any other AI as an original author. However, you are only allowed to use direct quotations from LLMs at this stage, present them within direct quotation marks “…” and include the corresponding page number from the AI discussion transcript.

Example text: The challenges of conceptualising digital technologies are escalating because the nature of digital assets is “dynamic, multifaceted, and ever-evolving… Digital assets encompass a wide range of intangible elements that pose unique considerations and intricacies, making their conceptualisation and management a formidable task” (ChatGPT, p.4).

• AI systems are prone to hallucinations. Augment the AI output by at least one additional academic resource. This can be a corroborating or a contrasting view, or both. Bring in a real-life business-oriented example, or a case study framed insight that will put things into context.

Example text: The challenges of conceptualising digital technologies are escalating because the nature of digital assets is “dynamic, multifaceted, and ever-evolving… Digital assets encompass a wide range of intangible elements that pose unique considerations and intricacies, making their conceptualisation and management a formidable task” (ChatGPT, p.4). This is supported by the research conducted by Zuboff (2021) where she argues that intangibility has many facets as the virtualisation of assets makes them harder to consider, particularly in cloud-based services and distributed architectures like blockchain. Dhillon (2022) argues that this problem is critical for information security: if we do not get a grasp on the nature of digital assets, then how do we know what we are supposed to protect from cyberattacks, and how can we risk-prioritise such assets?

• Finally, do not overuse AI-generated text and follow the instructions above.

1. Case Study ‘Happy Cow Ice Cream: Data-Driven Sales Forecasting’.
2. Dataset 1 (DailySales.xls): Daily sales of several ice cream flavours over five months across three types of consumers (students, staff and others / tourists).

• Dataset 2 (HourlySales.xls): hourly sales over seven months (April to October).

Submission

For this coursework, you need to submit the following:

• A written report providing answers for each question with accompanying visuals.
• Your report must not exceed 1,000 words (plus or minus 10%).

Please submit this assignment electronically via Canvas in Word document or PDF.

Additional Information

1. You must follow the Harvard referencing style.
2. Your review must be written in Times New Roman, 12 font, 1.5 line spacing.
3. Carefully read the guidance on plagiarism from the university’s website. Submitting work that is copied and / or not referenced properly could result in being referred to the plagiarism committee and receiving a mark of zero (0).

Overlength Assignments

Please note: The following penalties will be applied to work that is over the specified limit.

• Up to 10% over the specified limit, no penalty applied.
• 10-20% over the specified limit, a penalty of 10% of the available marks for the assessment or reduction to pass mark, whichever is higher.
• 20-50% over the specified limit, the maximum mark awarded will be the pass mark.
• More than 50% over the specified limit, the work will not be marked and a mark of zero will be recorded.

Addressing Module Learning Outcomes & Grading Criteria

This assignment tests whether you have achieved the learning outcomes of the module:

Information Management and Visualisation Assignment 2

Introduction

The purpose of this assignment is to demonstrate the information visualisation concepts and techniques that you have learned in this module to (1) transform complex data into a compelling and aesthetically pleasing dashboard, and (2) assist in answering specific business problems or questions.

The goal of this project is to come up with two or three interesting questions (which can be business, social, environmental or political in nature), and then to solve / investigate / elaborate on these questions using the information visualisation concepts and techniques learned in this module.

The assignment requires you to carefully collect, clean, present and analyse publicly-available data using the knowledge, tools and skills you developed in class. You are required to produce visual evidence to support any of your claims.

This assignment consists of two parts:

• Part one is to produce Tableau dashboards to illustrate your data.
• Part two is a short report explaining the visualisation and using it to answer the business questions. This is not a repeat of the theory covered in class, but to show the process and choices you made in turning your data into a visual dashboard that assists the business manager in answering the question that is under investigation.

This assignment carries 70% of the unit marks. This is an individual project.

Scenario

This assignment requires you to visualise a publicly available dataset to answer two or three compelling questions of your choice. The answers to the questions must be based on the visualisation produced.

Task

Find a publicly-available data source, of sufficient complexity, and come up with two or three interesting questions (can be business, social, environmental or political in nature), develop a suitable dashboard to solve / investigate / elaborate the questions raised using the information visualisation concepts and techniques learned in this module.

You are required to produce visual evidence (eg a Tableau dashboard) to support any of your claims and a short report explaining the visualisation, using the following rules:

• Use any publicly-available dataset available at https://data.gov.uk/ or any other source. You must provide a reference for where your data came from.
• Come up with a minimum of two or three business (or social, environmental, political, etc) questions based on the data.
• Build a dashboard to visually answer the questions.
• Publish your dashboard(s) to Tableau Public. For inspiration, review the Tableau Public gallery. [“Tableau dashboard + 123456789”. Please use 202426853]
• Refer to the following for your attention (shared by my lecturer):
  Save Workbooks to Tableau Public

https://www.thetableaustudentguide.com/tableau-public/publishing-to-tableau-public

https://help.tableau.com/current/pro/desktop/en-us/stories.htm

• Write a short report (maximum 3,000 words) to outline the process for developing your visualisation, justify the decisions you took in presenting the data in that way, and answer your business questions, based on the evidence provided by your dashboard. Include screenshots of your visuals in the report at appropriate places.

Questions to Consider:

Consider the questions below as you are working on your report.

1. What is the question(s) or problem?
   • What issue or information set does this visualisation cover?
   • What specific problem or question is it / are you trying to resolve?
2. What visualisation techniques are you using and how do they work?
   • What specific investigational goals do you have?
   • What specific techniques are used to achieve those goals?
   • How appropriate are these strategies to the question and the data?
   • How effective are these strategies at revealing, organising, or comparing and increasing your understanding of the issue(s)?
3. What insight did you gain?
   • Specifically, did you discover something about the question? Did you find new insight?
   • Why? How did your visual / organisational technique help?
4. Compare the techniques critically, and suggest improvements.
   • What could be done better to make your understanding of the issue more complete, or improve a broader understanding of the problem?
   • Critique the tool and the strategy.

NOTE

To do the tasks above, you may need to make some assumptions – please highlight them in your report.

Please pay attention to the grading criteria. Please also note the University policy on academic misconduct.

The use of ChatGPT, Bard, or any other AI-tools is allowed in this assignment under specific guidelines and provisions as outlined in this section.

Under the clauses of fair use, proper academic attribution, and unfair means, if you use any AI text-generation solution without properly attributing its use, then you are at risk. Each assignment will be checked through algorithmic AI-detection tools thoroughly, and while such detection is never perfect, it only needs to be good enough on a % of the submitted text. Any suspicion of unfair AI use will be sent to the plagiarism committee for further investigation, and if it reaches a panel, you may be called to explain your work and defend it. Based on algorithmically presented evidence from such software, and on the balance of probability, if plagiarism / unfair use is established, it is highly likely that your mark in the module will be zero. The same could apply if you copy-paste text from other resources without proper attribution and proper referencing. Repeated plagiarism offences can mean that your programme of study is terminated. So, please be extremely careful and diligent in how you approach AI use in the context of text generation.

If you use LLMs like ChatGPT or an AI-graphical generator, then you must:

• Maintain a full record of the entire discussion you have with the AI tool and share it within your assignment. Include the entire AI-discussion that you had within your assignment submission as an Appendix and label it “ AI discussion transcript” – this can be as many pages as you want. It will not count towards the word count.
• Do not use multiple discussion threads. Keep everything in one place and present only one AI discussion transcript.
• The AI discussion transcript that is presented in the Appendix should have numbered pages for ease of reference.
• Since LLMs generate ‘original text’ for fair use, we will treat ChatGPT, Bard, or any other AI as an original author. However, you are only allowed to use direct quotations from LLMs at this stage, present them within direct quotation marks “…” and include the corresponding page number from the AI discussion transcript.

Example text: The challenges of conceptualising digital technologies are escalating because the nature of digital assets is “dynamic, multifaceted, and ever-evolving… Digital assets encompass a wide range of intangible elements that pose unique considerations and intricacies, making their conceptualisation and management a formidable task” (ChatGPT, p.4).

• AI systems are prone to hallucinations. Augment the AI output by at least one additional academic resource. This can be a corroborating or a contrasting view, or both. Bring in a real-life business-oriented example, or a case study framed insight that will put things into context.

Example text: The challenges of conceptualising digital technologies are escalating because the nature of digital assets is “dynamic, multifaceted, and ever-evolving… Digital assets encompass a wide range of intangible elements that pose unique considerations and intricacies, making their conceptualisation and management a formidable task” (ChatGPT, p.4). This is supported by the research conducted by Zuboff (2021) where she argues that intangibility has many facets as the virtualisation of assets makes them harder to consider, particularly in cloud-based services and distributed architectures like blockchain. Dhillon (2022) argues that this problem is critical for information security: if we do not get a grasp on the nature of digital assets, then how do we know what we are supposed to protect from cyberattacks, and how can we risk-prioritise such assets?

• Finally, do not overuse AI-generated text and follow the instructions above.

Submission

For this coursework, you need to submit the following:

1. Individual dashboard(s) (*.pdf or *.jpg).
2. A report (maximum 3,000 words) explaining the visualisation, the business questions you are trying to solve, why the issue is important, and who the target audience of the visualisation is (e.g. stakeholders, suppliers, employees, customers, potential investors and shareholders). Upload this as a .pdf or .word file.

Please note: The following penalties will be applied to work that is over the specified limit.

• Up to 10% over the specified limit, no penalty applied.
• 10-20% over the specified limit, a penalty of 10% of the available marks for the assessment or reduction to pass mark, whichever is higher.
• 20-50% over the specified limit, the maximum mark awarded will be the pass mark.
• More than 50% over the specified limit, the work will not be marked and a mark of zero will be recorded.

Additional Information

1. You must follow the Harvard referencing style.
2. Your review must be written in Times New Roman, 12 font, 1.5 line spacing.
3. Read carefully the guidance on plagiarism from the university’s website. Submitting work that is copied and / or not referenced properly could result in being referred to the plagiarism committee and receiving a mark of zero (0).

NOTE

Instructions for submitting assignment documents:

• Save your individual Tableau dashboard(s) – indicate document name, and your student number: “Tableau dashboard + 123456789”. [ Please use 202426853]
• Save your report – indicate document name, and your student number: “Report + 123456789”. [ Please use 202426853]
• Both documents need to be submitted electronically via Canvas in *.word, *.pdf or *.jpg formats.

Addressing Module Learning Outcomes & Grading Criteria

This assignment tests whether you have achieved the learning outcomes of the module:

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