BUSINFO 703 Data Visualisation for Business (Q4-2024)
Assignment Two (Group) - 30 points
Presentation: Thursday (28-Nov) – 11:00 AM to 8:30 PM
Report submission: by Friday (29-Nov) – by 11:59 PM
Assignment Overview
For this group assignment, you will work as a team to choose a dataset from a list; explore
and include additional dataset(s) from independent sources; apply unsupervised machine
learning techniques using R to generate relevant insights; craft a suitable narrative pertinent
to a business audience; develop a report in Power BI Desktop with relevant visuals; present
your findings supported by your visuals; and submit your work for review.
Purpose
This group assignment assesses your collective skills in data visualisation, unsupervised
machine learning, and data storytelling – utilising Power BI Desktop and R as your primary
tools. Effective teamwork is essential for success, where each member complements and
supplements one another’s abilities.
Target Audience
You will present the key findings of your research to a business audience. It would be best to
assume they are neither business analytics professionals nor programmers. You aim to
persuade them to take relevant action(s) based on your research. This presentation should
last 8-10 minutes, followed by a 2-minute question-and-answer session.
Data Source
You must use one of these datasets as the primary source for this assignment:
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BUSINFO_703_Dataset_ShipmentPricing_(1001x33).csv (link for download or info)
BUSINFO_703_Dataset_ShoppingTrends_(2501x19).csv (link for download or info)
BUSINFO_703_Dataset_Unicorns_$2B_(508x7).csv (link for download or info)
Note: When selecting a suitable option, consider how it aligns with your interests, your overall
team capabilities, your understanding of the data, and potential storytelling avenues.
In addition to one of these files, you must explore and include dataset(s) from other
independent sources that complement this.
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Here are some examples of external datasets and potential research avenues:
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The “ShipmentPricing” dataset could be integrated with the Natural Disasters dataset
(link) to explore the possible impact of such disasters on prices or supplies.
The “ShoppingTrends” dataset could be integrated with the U.S. Annual/Seasonal
Climate dataset (link) to determine which customers prefer colours based on weather.
The “Unicorns_$2B” dataset could be integrated with the S&P 500 index history (link)
to see how changes in the overall stock market relate to the popularity of Unicorns.
Note: These are mere suggestions to help you deliberate further. Don’t use these options
Key Steps
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Dataset Selection and Data Integration: Choose an appropriate dataset from the list above
and integrate data from external sources (s) to support your narrative.
Data Loading and Transformation: Load the selected data into Power BI Desktop, apply
relevant transformations, and prepare the data for analysis and visualisation.
Visualisation Development: Create meaningful visualisations with suitable interactions
that align with your presentation and effectively convey your narrative.
Unsupervised Machine Learning: Apply an unsupervised machine learning technique
relevant to your dataset and narrative. Use this technique to extract critical insights and
generate relevant visuals.
Storytelling and Presentation: Craft and present your narrative from a business
perspective, supported by your visualisations and insights.
Submission Requirements:
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Original data files – one of the mandatory datasets and any external dataset(s)
One ‘.pbix’ file containing your report in Power BI Desktop
Any '.R' file(s) that showcase your unsupervised ML work
Create a .zip file that includes all your files and submit it on Canvas
Please remember that each group should make only one submission. Only the most recent
submissions will be assessed if multiple submissions are made.
Software
It would be best to use Power BI Desktop and R-Studio for this assignment. We further
recommend using a shared OneDrive folder for collaboration. If you have software-related
issues or need other guidance, please use Piazza, Labs or Office Hours to sort this out.
Data Ingestion
Please load the selected dataset (one of the ‘.csv’ files) and additional data file(s) into Power
BI Desktop. Please apply all data cleaning and transformation steps in Power Query. Make
sure to rename the steps in Power Query to signify their business relevance.
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You may choose to merge the datasets in Power Query or link them in the Data Model in
Power BI Desktop. Please ensure your transformations and modelling actions align with your
visualisation objectives.
Data Visualisation
Build relevant visuals to present a story about your chosen data. Please select the number of
reports and visuals on each report to support your presentation. You may add suitable
interactive features that align with your story and aid the flow of your presentation.
Choose the text content on your pages carefully – please note that your visuals should be the
primary mode for communicating your message.
Unsupervised Machine Learning
Apply machine-learning techniques relevant to your datasets and your overall narrative.
Please include the relevant statements in the code blocks if you need special libraries installed
in the R environment. Add suitable context to your work using comments within your code
when the steps are unclear.
Save your final ‘. R’ file(s) to be included in your submission.
You may embed some of your R code for data extraction (if dealing with unconventional data
sources for complementary datasets) in Power BI Desktop.
You may embed some of your R code for machine learning as part of the transformation steps
in Power Query before generating visuals in Power BI Desktop.
You may embed some of your visuals generated using R code in Power BI Desktop.
Presentation
You will present your story driven by data and visuals to a business-oriented audience. Your
presentation should last around 8-10 minutes and showcase your Power BI reports and
Machine Learning work (please focus on insights based on data and visuals instead of code).
Include a ‘call to action’ at an appropriate point in your presentation.
Ensure that each team member actively participates in the presentation. Storytelling using
data and visuals will be assessed at the group level, while the Business Communication Team
will evaluate individual presentation skills separately.
Presentations will occur in Week 10 on Thursday (from 11:00 AM to 8:30 PM), with specific
time slots announced by the end of Week 9.
Feedback
You need to provide feedback on presentations made by other groups. The form for providing
feedback will be available during the session. Please ensure that your feedback covers both
positive features and areas of improvement and aligns with your overall rating.
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Submission
Upload all your files – Data files, Power BI Desktop file (.pbix), any custom visuals (.pbiviz),
and R code file(s) – as a single .zip file on Canvas.
The submission deadline is Friday, 29-Nov-2024, 11:59 PM. Please allocate sufficient time to
complete this step. Avoid last-minute panic, which may lead to incorrect uploads or missing
the deadline.
The assignment will remain open until Sunday, 29-Nov-2024, 11:59 PM, to accept late
submissions (which incur a 5% penalty for each day of delay beyond Friday, 29-Nov-2024,
11:59 PM). For instance, if you submit on Sunday, 29-Nov-2024, at 5:00 PM, and your score
for the Group Report is 8/10, your final score will be 7.2/10.
Please make only one submission per group. While you may resubmit multiple times, only the
most recent submission will be marked.
Rubric
# Criteria
1 Overall storytelling using visuals and data
2 Presentation Skills (Individual)
3 Feedback on Others’ Presentations (Individual)
4 Peer Review of Group Presentation
5 Data Transformation, Modelling and Visuals (Power BI Desktop)
6 R Code for Unsupervised Machine Learning and Visualisation
Note: Please refer to Rubric on Canvas for specific details
Range
0-5 points
0-5 points
0-5 points
0-5 points
0-5 points
0-5 points
Alignment
This assignment’s expected learning outcomes (as per DCO) are as follows.
Learning Outcomes
LO3: Recommend data modelling and
transformation techniques to generate
suitable visualisations.
LO4: Generate a compelling narrative using
data.
LO5: Apply unsupervised machine learning
techniques on small and large datasets.
LO6: Develop and deliver effective,
engaging presentations to a specified
audience.
Capabilities
C1: Disciplinary Knowledge and Practice
C2: Critical Thinking
C1: Disciplinary Knowledge and Practice
C3: Solution Seeking
C5.1: Independence
C1: Disciplinary Knowledge and Practice
C2: Critical Thinking
C1: Disciplinary Knowledge and Practice
C4.1: Oral Communication
C4.3: Engagement
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