COMM1190 Industry-based Assessment 1: Individual Report
Week 4: 3:00 pm Friday (AEDT)
20%
A written report
Maximum word count of 750, excluding references, figures, tables, and
appendices.
Via Turnitin on Moodle course site
Objective
The objective of this individual assessment is to evaluate your ability to conceptualize and
solve analytics problems, your proficiency in R programming, and your capacity to provide
business recommendations based on analytics results. In this assignment, you will conduct
an exploratory data analysis as a data analyst. You are expected to analyze data using
statistical and visualization techniques. This learning content has been covered in the
course until the end of Week 3.
Company and Product Background
You have recently been employed as a data analyst for Amazing Sports Australia Ltd
(ASAL), an online e-store. ASAL specializes in selling a wide range of branded and non-
branded sports products, which are broadly categorized as (i) Equipment, (ii) Apparel, and
(iii) Footwear. The company has recently launched a shopping mobile app and is concerned
about its effectiveness in increasing sales and promoting its products. The management
team aims to understand customer spending patterns and behavior with the ultimate goal
of optimizing the app usage and enhancing sales.
About the task:
As a data analyst on this project, your primary task is to utilize R to explore the provided
dataset and generate visualizations that assist ASAL's app managers in comprehending
user behavior and engagement with the app. By using R, you will analyze the dataset
and create exploratory visualizations to identify trends and patterns that can provide
insights for marketing and sales strategies.
The company has provided you with data encompassing user demographic information
(such as age, gender, etc.) and application usage information (such as number of
referrals to friends). A separate document, the Data Dictionary, will be shared with you,
containing a detailed description of each attribute in the dataset.
• Conduct descriptive analytics to identify the factors associated with customers'
spending on sports products. Descriptive analytics encompasses the use of
statistical analysis and visualization techniques. For instance, employing a box
plot and a bar chart are considered as two distinct techniques.
• Offer recommendations to the leadership team on enhancing customers'
spending and user engagement on applications, based on the results obtained
from descriptive analytics.
Guidance on Data Analysis
Note: The dataset and the data dictionary will be provided to you separately.
There is not a single correct answer to the assignment. The dataset includes many
attributes for you to explore, and some attributes are likely to be more useful than
others. Therefore, it is important that you systematically explore different variables in
the dataset to facilitate your analysis.
• Consider potential key factors associated with increasing sales and app
engagement by relating them to real-world scenarios. Justify your selection of
variables by referring to industry examples and supporting arguments.
• While creating multiple graphs for your assignment, ensure that you only
include figures that support your main findings. These graphs should highlight
key features of the associations you are reporting.
• Your recommendations to the leadership team at ASAL should be well
supported by your visualizations and/or statistical summaries.
• Explicitly state any key assumptions that impact your data analysis and any
caveats regarding your recommendations.
Requirements:
1. Problem Exploration (10%)
• Explore and understand the business problem of retail sales within the
Australian context.
• Clearly state the purpose of the analytics tasks.
2. Data Analysis (50%)
• Justify the selection of techniques and variables, with a recommendation of
using more than 3 variables for analysis.
• Apply appropriate descriptive analytics techniques, such as summary
statistics and data visualization. Avoid conducting predictive or prescriptive
analytics.
• Utilize visualization graphs, such as histograms, bar charts, scatter plots, and
box plots, to explore associations between variables.
• Interpret the results obtained from the analytics.
3. Recommendations (20%)
• Provide recommendations based on the analytics results.
• Support the recommendations using state-of-the-art industry practices and
supporting arguments.
• Incorporate supplementary readings related to the assessment and conduct
self-research to develop informed recommendations.
4. Communication (10%)
• Demonstrate proficiency in reading and writing in English.
• Use language, figures, and/or tables to convey qualitative and quantitative
information effectively and accurately.
• Attach the R programming code (not a screenshot) to the Appendix of the
report.
5. Organization and structure of the report (10%)
• Develop a logical structure to organize the sections of the report.
• Use academic referencing in the Harvard style. Refer to the UNSW guideline:
https://www.student.unsw.edu.au/harvard-referencing
• An example of structuring and developing the report is provided in Appendix
A.
Submission Instructions
• A written report with all relevant codes in an appendix.
• A cover sheet with signature.
• Word limit is 750 words with 10% leeway.
Please note:
• the codes do not count toward the word limit. A 10% penalty applies to
missing a signed cover sheet in the submission.
• 5% penalty is applied for exceeding the word limit.
Late Submission Penalties
• Late submission will result in a penalty of 5% per day or part thereof (including
weekends) from the original due date and time. Assignments will not be
accepted after 5 days (120 hours) past the original deadline unless special
consideration has been approved. A submission will be considered late if the
requested format, whether hard copy or electronic copy, has not been submitted
on time, or if the wrong assignment has been submitted.
• Extensions will not be granted unless there are valid reasons such as serious
illness, misadventure, or bereavement, which must be supported by
documentary evidence. Requests for extensions must be sent via email to the
Course Coordinator and be accompanied by the appropriate documentation no
later than 24 hours before the assignment's due date. In cases where this is not
feasible, students must apply for Special Consideration.
• The Course Coordinator is the only person authorized to approve extension
requests. If a request for an extension is made, the Course Coordinator will
communicate the decision via email to the student. It is important to note that a
request for an extension does not guarantee its approval.
Smarthinking English Support
“… an online writing support platform officially sanctioned by UNSW. Students can
submit drafts of their writing to a Smarthinking tutor or connect to a Smarthinking
tutor in a real-time session and receive comprehensive feedback on a variety of
writing areas”. https://www.student.unsw.edu.au/smarthinking
Smarthinking is available on the COMM1190 Moodle Site.
Using the service, you can:
• Submit your drafts to a Smarthinking tutor for comprehensive feedback on
your writing typically within 24 hours; or
• Connect to a Smarthinking tutor in a live one-on-one session about writing.
• Receive comments on a variety of writing areas including clarity of your
ideas, grammar, organization, etc.
• Use up to 2 hours on Smarthinking reviews
Marking Rubric for Individual Assessment
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 65%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
A
N
A
L
Y
S
IS
(
8
0
%
)
10 Problem
Exploration
■ Does not show any
engagement with
sources external to
the assignment
document to
augment
understanding of
the business
problem and
context.
■ Identifies a few
sources external to
the assignment
document to
augment
understanding of the
business problem
and context;
sources are not
highly relevant
and/or credible;
writing lacks
synthesis
■ Identifies relevant
sources external to
the assignment
document to
augment
understanding of the
business problem
and context; sources
are relevant and
credible; synthesizes
information without
any inferences.
■ Identifies relevant
sources external
to the assignment
document to
augment
understanding of
the business
problem and
context; sources
are relevant and
credible;
synthesizes
information and
draws relevant
inferences.
■ Identifies relevant
sources external to
the assignment
document to
augment
understanding of the
business problem
and context; sources
are highly relevant
and from credible
academic sources;
writing synthesizes
information and
draws highly original
inferences.
50 Data
Analysis
■ No relevant
descriptive
analytical
technique was
identified.
■ No specific
variable was
identified.
■ No logic between
business issues,
analytical
techniques, and
variable selection.
■ No statistics
summary or
■ Identifies 1
descriptive
analytical technique
to be used for
solving the problem.
■ Identifies variables
for each technique
to be deployed.
■ Attempts to present
a logic between
business issues,
analytical
techniques, and
variable selection,
■ Identifies and
explains 2
descriptive
analytical
techniques to be
used for solving the
problem.
■ Identifies and
explains variables
for each technique
to be deployed.
■ Attempts to present
a logic between
business issues,
analytical
■ Identifies,
explains, and
justifies 3
descriptive
analytical
techniques to be
used for solving
the problem.
■ Identifies,
explains, and
justifies variables
for each technique
to be deployed.
■ Presents a
reasonable logic
between business
■ Identifies, explains,
and justifies 3
descriptive
analytical
techniques to be
used for solving the
problem with clarity.
■ Identifies, explains,
and justifies
variables for each
technique to be
deployed. The
justifications are
sound and
convincing.
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 65%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
visualization is
presented.
■ The results are
mostly incorrectly
interpreted.
■ No R codes are
included.
but the logic is not
coherent or clear.
■ Attempts to analyze
data but conduct
inadequate data
analysis in some
aspects.
■ The results are
somewhat correctly
examined and
interpreted.
■ R codes are
included but
extensive errors are
identified.
techniques, and
variable selection.
■ Analyzes data but
explanations of
analysis results are
insufficient.
■ The results are
mostly correctly
examined and
interpreted.
■ R codes are
included but some
errors are identified.
issues, analytical
techniques, and
variable selection.
■ Analyzes data
adequately with
sufficient
explanations of the
issues identified,
but the solutions to
solving the issues
identified are
insufficient.
■ The results of
each model are
mostly correctly
interpreted and
examined
supported by
academic
references.
Results
interpretation is
relevant and
meaningful in the
case context.
■ R codes attached
are mostly correct.
■ Explicitly presents a
coherent and clear
logic between
business issues,
analytical
techniques, and
variable selection.
The logic is
coherent and clear.
■ Analyzes data
adequately with
sufficient
explanations of the
issues identified,
and with adequate
solutions to the
issues identified
using statistics and
visualization.
■ The results of each
model performance
and findings are
correctly interpreted
and critically
examined supported
by academic
references. Results
interpretation is
relevant and
meaningful in the
case context.
■ R codes attached
are thoroughly
correct.
Weight
%
Criteria Fail
(0% -
49%)
Pass
(50% - 65%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
20 Recommendation ■ Inadequate or
no
recommendation
s of the
analysis/evidenc
e are provided.
■ Recommendations
are somewhat
inconsistently tied
to some of the
issues discussed
and inconsistently
linked back to
variables analyzed.
■ Recommendations
are consistently tied
to each issue
discussed and
linked back to
variables analyzed.
■ Recommendations
are logically and
consistently tied to
each issue
discussed and
linked back to
variables analyzed.
■ Recommendations
are logically and
consistently tied to
each issue
discussed, linked
back to variables
analyzed, and
developed with
critical thinking.
C
O
M
M
U
N
IC
A
T
IO
N
(
2
0
%
)
10 Communication ■ Your writing lacks
a professional
tone and
contains
numerous
spelling and
grammatical
errors.
■ The structure of
your essay does
not demonstrate
a logical flow
between each
section, affecting
the overall
coherence of
your written
expression.
■ Although some
attempts have
been made to use
a professional tone
and presentation
in your writing,
there are still some
lingering spelling
and grammatical
errors that need
attention.
■ It appears that you
have made an
effort to establish
logic and flow
between each
section of the
essay. However,
there is room for
improvement in
terms of ensuring a
seamless transition
and coherence
throughout.
■ Your writing
demonstrates a
mostly professional
tone and
presentation,
although there are
occasional spelling
and/or grammatical
errors that should be
addressed.
■ Overall, your written
expression
adequately indicates
the logic and flow
between each section
of the essay, but
there is room for
further improvement
to enhance the
coherence of your
ideas.
■ Your writing is
professional in
tone and
presentation with a
few very minor
spellings and/or
grammatical
errors.
■ Your written
expression
provides a strong
indication of the
logic/flow between
each section of the
essay.
■ Your writing
maintains a
professional tone
and is presented
exceptionally well,
without any
noticeable spelling
or grammatical
errors.
■ Your written
expression skillfully
demonstrates a
strong and coherent
indication of the logic
and flow between
each section of the
essay. This has
enabled your key
arguments to develop
fully and effectively.
10 Organisation
and structure
of the report
■ Poor or
unclear
■ Attempt to a
good structure
■ Good structure with
organized headings.
■ Good structure
with organized
■ Good structure with
organized headings
structure.
■ Your sources
have not been
referenced
and/or there are
excessive errors
in referencing in
the essay.
but lack coherent
flow between
sections.
■ Some sources are
referenced
throughout the
essay, but there
are errors in your
■ Most sources are
referenced
throughout the
essay, with only
minor errors in
referencing.
headings and
coherent follow
between sections.
■ All sources are
referenced
throughout the
essay with only
and coherent follow
between sections.
■ All sources are
referenced
throughout the
essay and the
sources are used
very well, with no
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 65%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
■ The word limit has
not been adhered
to.
referencing of
sources.
minor errors in
referencing.
significant errors in
referencing.
Appendix A. An Example of Report Template
Content page
Include:
• Page numbers from this page onwards (Insert ➔ Page Number)
• A header from this page onwards, including your ZID and course code (Insert
➔ Header)
• All key sections and sub-sections of your report listed in the contents page
If you are unsure how to format a report contents page, select “References” in the
menu above, then “Table of Contents”. Examples:
Key sections to include in your report
1. Introduction
✓ Have you provided the purpose of your report?
✓ Have you discussed the business context adequately in your report?
✓ Have you given a brief outline of the contents of your report?
2. Summary Statistics
✓ Have you included relevant data in the form that best communicates it,
e.g. tables, figures, etc?
✓ Have you divided this data into clear sections or themes for readability?
✓ Have you clearly linked this data to the subject matter and how it is
relevant to the question and problem at hand?
✓ Have you referenced any literature or recent events that you
researched, if it provides useful insights into or justification for the
problem analysis?
3. Visualization Analysis
✓ Have you identified and explained the variables based on the data?
✓ Have you included relevant graphs which are appropriate for the type
of data you’re presenting?
✓ Have you used the data to clearly justify why the variables and
visualization techniques are related to the problem, concluding the
variables that are the most relevant?
4. Recommendations
✓ Have you made at least one clear, actionable recommendation?
✓ Is/are your recommendation/s based on the conclusions/data-
supported variables above?
5. Reference List
✓ Are your references in alphabetical order?
✓ Do your referencing follow Harvard style as required?
6. Appendix
✓ Have you included your R code in the Appendix?
✓ Have you included any other supporting tables or figures in the
Appendix, as relevant?