BUSINESS SCHOOL
Page 1 of 5

QBUS6600
Data Analytics for Business Capstone
Semester 2, 2024
Assignment 2 (Group assignment)

1. Key information

Required submissions:

• Team responsibilities outline (due: September 12; required but not marked).
• Progress report (due: September 27; required but not marked).
• Written report (due: October 14).
• Presentation video (due: October 14).
• Python Code (due: October 14; required but not marked).
• Peer review (due: October 18; required but not marked).

Weight: 40% of your final grade.
Length: Your written report should have a maximum of 15 pages (single spaced, 11pt). Cover
page, references, table of contents and appendix (if any) will not count towards the page limit.
Please keep in mind that making good use of your audience’s time is an essential business
skill: every sentence, table or figure should serve a purpose.

2. Problem description

Please start by carefully reading through the Project Outline pdf document posted on the
page containing the full dataset for your industry project. Focus on the Problem Description
section of the Project Outline, especially the corresponding second and third bullet points
(Modelling and Strategy). Both your analysis and your recommendations should be in line with
the requirements/suggestions provided in the Project Outline.

Your task as a group is to build on the insights discovered in the first assignment and to use
any appropriate statistical/machine learning modelling tools to address the questions relevant
to your industry project. Ensure that you justify the selection of your final model(s) and
interpret the model(s) in terms of the key attributes or features. If your best-performing
models are too complex for this interpretation, we suggest that you also consider well-
performing simpler models.

Use the results from your analysis to outline a strategy and provide recommendations to the
management team corresponding to your industry project. Please note that your strategy and
recommendations should significantly expand on anything you proposed in your preliminary
recommendations of the first assignment.

BUSINESS SCHOOL
Page 2 of 5

3. Written report

The purpose of the report is to describe, explain, and justify your findings to the management
team corresponding to your selected industry project. You may assume that team members
have training in business analytics, however, they are not experts in statistics or machine
learning. The team’s time is important: please be concise and objective.

Executive briefing.

You are asked to summarise your findings so that they can be shared with the wider business
and, in particular, senior management. This one-page briefing should concisely describe your
findings to a non-technical audience and primarily address the business problem. In the
briefing you should also outline your recommendations for acting on your findings.

You are limited to a maximum of 1 page (which is included in the overall 15 pages).

Suggested potential outline for the main parts of the report (further details below):

1. Business context and problem formulation.
2. Data processing, EDA, and feature engineering.
3. Model building.
4. Conclusions and recommendations.
5. Executive briefing.

Please note that ‘Model building’ should be the most substantial part of your written report.
You should consider breaking down the longer parts into smaller sections. The second part
(Data processing, EDA, and feature engineering) cannot simply be copied from Assignment 1
– this would be considered recycling of the prior work under the academic honesty policy. The
individual EDA findings from Assignment 1 can be summarised, highlighted, and synthesised,
but the main focus is on feature engineering and other elements that are important for your
model.

4. Presentation video

Create a video recording of an oral presentation to summarise your work and your findings
for an executive or managerial audience. For example, the video can be a Zoom recording, in
which the team members present while the presentation slides are shared. Please aim for
about 10 minutes in length. All group members must speak during the presentation, ideally
for equal amounts of time. Remember, your business audience will not be experts in machine
learning or statistics. Your presentation should translate the analysis, modelling, and findings
to business impact.

BUSINESS SCHOOL
Page 3 of 5

5. Marking Scheme
Business context and problem formulation 5%
Data processing, EDA, and feature engineering 15%
Model building 45%
Conclusions and recommendations 15%
Executive briefing 10%
Presentation video 10%
Total 100%

6. Rubric (basic requirements)

Business context and problem formulation. Your report gives a detailed description of the
problem that is being investigated, providing the context and background for the analysis.

Data processing, EDA, and feature engineering. You describe the data processing steps
clearly and in sufficient detail, justifying and explaining your choices and decisions. You
describe and explain your feature engineering process. Your choices and decisions are justified
by data analysis, domain knowledge, logic, and trial and error (if necessary). You describe your
EDA process, presenting selected results. Your analysis is sufficiently rich, and your
visualizations are insightful. You explain the relevance of the EDA results to the underlying
business problem. You interpret the statistical outputs that you provide.

Model building. You clearly describe and justify the models, methods, and algorithms in your
analysis. The choice of methods is logically related to the substantive problem, underlying
theoretical knowledge, and data analysis. You interpret the estimated models. You note crucial
assumptions and whether they are potentially violated. Your overall analysis is rich,
comprehensive, thorough, and logical. You implement a sound model selection process. You
obtain a high standard of predictive accuracy in line with what is achievable with the methods
at the level of experience expected from students taking this unit. You are not misled by
overfitting and explicitly acknowledge the limitations of the data and/or methods.

Conclusions and recommendations. The reasoning from the analysis and results to your
conclusions and recommendations is logical and convincing. Your conclusions and
recommendations are written in plain language appropriate for non-technical audience. Your
recommendations are well thought out, carefully developed, and well supported.

Executive briefing. You appropriately summarize your key findings to a non-technical
audience, addressing the underlying business problem. Your clearly outline your suggestions
for acting on your findings. Your executive briefing is well presented and logically structured.

Writing and presentation of the report. Your writing is concise, clear, precise, and free of
grammatical and spelling errors. Your paragraphs and sentences follow a clear logic and are
well connected. If you use an abbreviation or label, you define it first. Your report is well
organised and professionally presented, as if it had been prepared for a client later in your
BUSINESS SCHOOL
Page 4 of 5


career. There are clear divisions between sections and paragraphs. Your tables are
appropriately formatted and clearly presented. They do not contain irrelevant information. The
tables are placed near the relevant discussion in your report. Your figures are easy to
understand and have informative titles, captions, labels, and legends. The figures are well
formatted and laid out. The figures are placed near the relevant discussion in your report. Your
figures have appropriate definition and quality. All numerical results are reported to suitable
precision (typically no more than three decimal places, in some cases fewer). The text of your
report is entirely free of Python code. You follow the University of Sydney referencing rules
and guidelines.

Presentation video. Your presentation is well-structured, clear, and insightful. Your
presentation is well-performed in terms of logic, confidence, passion, and timing.

7. Python code (required but not marked).

Your group is required to submit the Python code used for the analysis, as a Jupyter notebook
or Python script. The code is submitted separately from the report. The code may be examined
to verify that your group has done the work. Your code should have comments that clearly
indicate which parts correspond to which sections of your report. You should explicitly
acknowledge when you borrow pieces of code from external sources.

8. Team responsibilities outline, progress report, peer review.

Your group is required to submit a brief ‘Team responsibilities outline’ (due September 12)
and a brief ‘Progress report’ (due September 27) – we encourage everyone to start thinking
as early as possible about the project tasks and the division of labour. There are no templates
for these documents – you are free to use any format or structure within the guidelines given
below.

Team responsibilities outline. Have a group meeting as soon as possible to agree on the
division of labour. After the meeting, submit one pdf document for your group, providing your
group number and listing the names of the group members. For each name on the list, report
whether or not they attended the group meeting, indicate their group assignment task (e.g.
EDA), and provide the corresponding expected date of completion. Please try to keep the
document under ¾ of a page. This submission will not be marked; however, there is a 5%
group assignment penalty for not submitting the document by the due date.

Progress report. Have another group meeting to discuss the progress. Submit your update
on the team responsibilities outline: for each task, indicate the percentage completed and the
updated expected completion date. Feel free to include any additional comments, such as
changes to the tasks assigned and/or division of labour. In addition, provide attendance for
the group meeting (yes/no for each student), and the date of the meeting. Please keep the
document under one page. This submission will not be marked; however, there is a 5% group
assignment penalty for not submitting the document by the due date.

BUSINESS SCHOOL
Page 5 of 5


Peer review. The peer review task is designed for you to benefit from peer feedback; it will
also be useful when you work on your Assignment 3. Peer review submission will not be
marked; however, there is a 5% assignment penalty if the review is not submitted by the due
date. The penalty will only be applied to the student who is late with their review.

9. Late Submission of the report/code/presentation video.

Late submissions are subject to a deduction of 5% of the maximum mark for each calendar
day after the due date. After ten calendar days late, a mark of zero will be awarded.

10. Disputes

If there is a dispute within a group, please notify the teaching team as soon as possible and
provide evidence, so that the dispute may be resolved quickly and equitably. If the teaching
team is notified well in advance of the due date, uncooperative and/or uncontactable group
members may be removed from their groups and will complete their assignment individually.
The contact person for disputes within groups is Qin Fang (qin.fang@sydney.edu.au), who is
the member of the teaching team focusing on the group assignment. All group members are
expected to make a meaningful and reasonable contribution to the team. Failure to do so will
result in penalties.

11. Academic honesty

Please remember that plagiarism and academic dishonesty are not tolerated by the University.
You can read more about this on the University website:

• Plagiarism and dishonesty
• Academic honesty

As a student of the University, you are responsible for taking part in your education in an honest and
authentic manner. It is, therefore, expected that you take extra care to ensure that there are no breaches
of academic honesty. All assignments will be manually and electronically checked for plagiarism
(copying). Any perceived breaches of academic honesty will be referred to the Office of Educational
Integrity for further investigation and penalised if verified. You can read more information on what
plagiarism is and how to avoid plagiarism from the University link:
https://www.sydney.edu.au/students/academic-integrity/breaches.html

Students are reminded that all sources of support for the group assignment must be acknowledged and
failure to acknowledge such support may potentially breach the University’s academic honesty
requirements.

Each group member is expected to be involved in the preparation, drafting, proofing and checking of all
aspects of this group assignment including ensuring no breaches of academic honesty. Group members
will be held jointly responsible for the entire submission and awarded the same merit mark. In the event
of a breach of academic honesty the penalty could apply to all members irrespective of which member(s)
caused the breach.