Data visualisation for business analytics
INSTRUCTIONS TO STUDENTS:
1. This End-of-Course Assessment paper comprises NINE (9) pages (including the cover page).
2. You are to include the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Name, and Submission Date.
3. Late submission will be subjected to mark-deduction scheme by the University.
Please refer to the Students Handbook for details.
IMPORTANT NOTE
ECA Submission Deadline: 3 March 2019, 12 noon
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ECA – January Semester 2019
ECA Submission Guidelines
Please follow the submission instructions stated below:
A – What Must Be Submitted
You are required to submit the following TWO (2) items for marking and grading:
• A Report (you should submit this item first as it carries the highest weightage).
• The Tableau workbook (.twbx file).
Please verify your submissions after you have submitted the above TWO (2) items.
B – Submission Deadline
• The TWO (2) items of Report and Tableau workbook are to be submitted by 12 noon on the
submission deadline.
• You are allowed multiple submissions till the cut-off date for each of the TWO (2) items.
• Late submission of any of the TWO (2) items will be subjected to mark-deduction scheme
by the University. Please refer to Section 5.2 Para 2.4 of the Student Handbook.
C – How the (2) Items Should Be Submitted
• The Report: submit online to Canvas via TurnItIn (for plagiarism detection)
• The Tableau workbook:
o save as .twbx file
o submit online to Canvas
• Avoid using a public WiFi connection for submitting large files. If you are using public
wireless (WiFi) connection (e.g. SG Wireless at public areas), you might encounter a break
in the connection when sending large files.
D – Additional guidelines on file formatting are given as follows:
1. Report • Please ensure that your Microsoft Word document is generated by
Microsoft Word 2007 or higher.
• The report must be saved in .docx format.
2. Tableau
workbook
• The workbook must be saved in .twbx format.
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ECA – January Semester 2019
E – Please be Aware of the Following:
Submission in hardcopy or any other means not given in the above guidelines will not be
accepted. You do not need to submit any other forms or cover sheets (e.g. form ET3) with your
ECA.
You are reminded that electronic transmission is not immediate. The network traffic may be
particularly heavy on the date of submission deadline and connections to the system cannot be
guaranteed. Hence, you are advised to submit your work early. Canvas will allow you to
submit your work late but your work will be subjected to the mark-deduction scheme. You
should therefore not jeopardise your course result by submitting your ECA at the last minute.
It is your responsibility to check and ensure that your files are successfully submitted to
Canvas.
F – Plagiarism and Collusion
Plagiarism and collusion are forms of cheating and are not acceptable in any form in a
student’s work, including this ECA. Plagiarism and collusion are taking work done by others
or work done together with others respectively and passing it off as your own. You can avoid
plagiarism by giving appropriate references when you use other people’s ideas, words or
pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting
and referencing. You can avoid collusion by ensuring that your submission is based on your
own individual effort.
The electronic submission of your ECA will be screened by plagiarism detection sof
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ECA – January Semester 2019
Tableau Workbook Evaluation Criteria
Effective Communication of Information /6
• Layout, text elements, colour blends, graphical enhancements are effectively used to
communicate information.
Clarity and Coherence /5
• Proper flow – title page, introduction, body, results, recommendations /
discussion points, conclusion/summary; quick to understand, free from
grammatical errors
Integrity /5
• Information presented is not distorted or misleading.
Creativity /4
• Ability to use creative elements to stimulate viewer’s engagement and
enhance understanding and clarity of difficult concepts
TOTAL /20
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ECA – January Semester 2019
SECTION A (Total 80 marks)
Question 1
Choose one (1) company in the cosmetic skincare industry.
(a) State the company’s Vision and Mission statement.
(4 marks)
(b) Construct four (4) Business Performance Measures and explain which Balanced
Scorecard Perspective these Measures belong to.
(8 marks)
(c) How do you think the company can make use of the four (4) Business Performance
Measures mentioned in Q1(b) to achieve its Strategic Objectives?
(8 marks)
Question 2
Realtor’s research team collects and cleans housing and economic data from a variety of
sources. The majority of Realtor’s aggregated housing market and economic data is made
available for free download (source: https://www.realtor.com/research/data/).
Realtor created a list of metrics (see Annex A) to track the real estate property market at various
geographical levels.
Store your Tableau workbook into a single Tableau workbook file in .twbx format.
(a) Select two (2) metrics from Annex A and use Tableau Desktop to create one (1) chart for
EACH of the metrics you selected.
You have to create at least two (2) of the following items in EACH of the chart created:
• Trend Line
• Reference Line
• Parameter
• Forecast
• Drill-down at various geographical levels
Provide screenshots of the charts in your report.
(14 marks)
(b) Illustrate the applications of visual cues, coordinate systems, scales and context in data
visualisation by providing explanation of the four (4) data visualisation components used
in EACH of the charts created in your answer to Question 2(a).
(5 marks)
(c) Create a dashboard which includes charts created in 2(a). You may create more charts if
needed to make your dashboard provide a more holistic view of insights from the data.
Use relevant Actions in Tableau to create an advanced Dashboard navigation so that
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ECA – January Semester 2019
Dashboard users will be able to filter, highlight and drill down the data used in the
Dashboard.
Illustrate the Dashboard Design Principles used. Provide screenshots of the dashboard in
your report.
(10 marks)
(d) Develop a storyboard with at least three (3) story points. The storyboard should enable
users to capture the insights from the data. More dashboards will need to be created to
give a more holistic view of the insights from the data set.
Provide screenshots of the storyboard in your report.
(16 marks)
(e) Describe and explain the insight your storyboard captured from the data. The evaluation
criteria include logical flow of your explanation and novelty as well.
(15 marks)
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ECA – January Semester 2019
SECTION B (Total 20 marks)
Save your Tableau workbook in a single Tableau workbook file in .twbx format and submit to
Canvas
(20 marks)
—– END OF ECA PAPER —–
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ECA – January Semester 2019
Annex A Realtor Metrics
(source: https://www.realtor.com/research/data/)
Inventory
1. Median Listing Price – The median listing price within the specified geography during
the specified month.
2. Average Listing Price – The average listing price within the specified geography
during the specified month.
3. Median Listing Square Feet – The median home size in square feet within the
specified geography during the specified month.
4. Median List Price Per Square Feet – The median listing price per square foot within
the specified geography during the specified month.
5. Active Listing Count – The count of active listings within the specified geography
during the specified month. The active listing count tracks the number of for sale
properties on the market, excluding pending listings where a pending status is available.
This is a snapshot measure of how many active listings can be expected on any given
day of the specified month.
6. Median Days on Market – The median number of days property listings spend on the
market within the specified geography during the specified month. Time spent on the
market is defined as the time between the initial listing of a property and either its
closing date or the date it is taken off the market.
7. Days on Market Under 30 Share – The share of properties spending fewer than 30
days on the market.
8. New Listing Count – The count of new listings added to the market within the specified
geography during the month.
9. Price Increase Count – The count of listings which have had their price increased
within the specified geography during the month.
10. Price Decrease Count – The count of listings which have had their price reduced within
the specified geography during the month.
11. Pending Listing Count – The count of pending listings within the specified geography
during the specified month, if a pending definition is available for that geography. This
is a snapshot measure of how many pending listings can be expected on any given day
of the specified month.
12. Total Listing Count – The total of both active listings and pending listings within the
specified geography during the specified month. This is a snapshot measure of how many total listings can be expected on any given day of the specified month.
13. Pending Ratio – The ratio of the pending listing count to the active listing count within
the specified geography during the specified month.
14. Views – The count of views to all Realtor.com listings in the geographic area.
15. Views per Property – The total count of views to active Realtor.com listings divided
by the active listing count for the geographic area.
Market Hotness
1. Nielsen HH Rank – The specified zip code, county, or metro area’s rank by household
count compared to other zip codes, counties and metro areas. A rank value of 1 is the
highest by household count.
2. Hotness Rank Within County – In the case of a zip code, this metric represents the
zip code’s hotness rank, by hotness score, compared to all other zip codes within its
county. A rank value of 1 is considered the hottest (highest hotness score).
3. Hotness Rank Within CBSA – In the case of a zip code or county, this metric
represents the zip code or county’s hotness rank, by hotness score, compared to all other
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ECA – January Semester 2019
zip codes or counties within its metro area. A rank value of 1 is considered the hottest
(highest hotness score).
4. Hotness Rank – The specified zip code, county, or metro area’s hotness rank, by
hotness score, compared to all other zip codes, counties and metro areas. A rank value
of 1 is considered the hottest (highest hotness score).
5. Hotness Rank (Previous) – The specified zip code, county, or metro area’s hotness
rank in the previous month.
6. Hotness Rank (Change) – The change in hotness rank from the previous month. A
positive value indicates that the geography’s hotness has decreased (moved down in
ranking), and a negative value indicates that its hotness has increased (moved up in
ranking).
7. Hotness Score – The hotness score is an equally-weighted composite metric of a
geography’s supply score and demand score.
8. Supply Score – The supply score is an index representing a zip code, county or metro’s
median days on market ranking compared to other zip codes, counties or metros.
9. Demand Score – The supply score is an index representing a zip code, county or
metro’s listing page views per property ranking compared to other zip codes, counties
or metros.
10. Median DOM – The median number of days property listings spend on the market
within the specified geography during the specified month. Time spent on the market
is defined as the time between the initial listing of a property and either its closing date
or the date it is taken off the market.
11. Median DOM (vs US) – The median days on market in the specified geography divided
by the median days on market for the US overall during the same month.
12. Views Per Property (vs US) – The count of views a typical property receives in the
specified geography divided by the count of views a typical property receives in the US
overall during the same month.
13. Median Listing Price – The median listing price within the specified geography during
the specified month.
14. Median Listing Price (vs US) – The median listing price within the specified
geography divided by the median listing price for the US overall during the same month.
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