2023S1 QBUS3600 Individual Assignment 2 1
QBUS3600 Individual Assignment 2
Due Date: Monday 6 November 2023
Value: 30%
Length: 2000 +/- 10%
Notes to Students
1. The assignment is due at 5:00pm on Monday 6 November 2023. The late penalty for
the assignment is 5% of the assigned mark per day, starting after 5pm on the due date.
The closing date Friday 17 November 2023 is the last date on which an assessment
will be accepted for marking.
2. The assignment MUST be submitted electronically to Turnitin through QBUS3600
Canvas site. Please do NOT submit a zipped file.
3. Your report shall be provided as a word-processed document (Microsoft Word, LaTeX
or equivalent) giving full explanation and interpretation of any aspect you are
discussing.
4. Be warned that plagiarism between individuals is always obvious to the markers of the
assignment and can be easily detected by Turnitin.
5. This report should be considered to be highly confidential as you may discuss the
BigW or Mad Paws project, and you have responsibility to keep it secure and for it to
be used only for your QBUS3600 coursework.
6. Presentation of the assignment is part of the assignment. Marks are assigned for
clarity of writing and presentation.
7. Think about the best and most structured way to present your work, summarise the
procedures implemented, support your results/findings and prove the originality of
your work.
8. Please note that the final mark of this assignment is deemed as the final exam mark,
hence the results will not be released until after grade approval according to the
University policy.
2023S1 QBUS3600 Individual Assignment 2 2
2023S1
Background
Throughout this semester, you have studied and practiced your data analysis and machine
learning skills using a large-scale, real-world dataset. It is now a good time to reflect on what
you have done and learnt from this semester-long project.
It is highly recommended that you prepare for this report early and write it during the
semester, e.g., you may document your critical thinking and reflection on a machine learning
topic you might like, or any technical difficulties you may face when doing your group project.
If this is the first time you write a reflection statement, please look at a couple of online
resources:
1. https://www.sydney.edu.au/content/dam/students/documents/learning-
resources/learning-centre/writing/reflective-writing.pdf
2. https://student.unsw.edu.au/examples-reflective-writing
Tasks:
For tasks 1-3, draw on and link insights and real-world learnings from guest lectures
throughout the semester
1. (40 marks) Choose one or two Machine Learning topics that you have studied in
this semester, and critically discuss what aspects should be considered when it is used in a
real-world problem. You can use a publicly available dataset or data from your group
project to support your analysis and writing. Do not simply list the pros and cons of a
model but illustrate them using the chosen dataset. You can describe the steps you use to
understand the data and to train/select/calibrate/evaluate a model.
2. (50 marks) In this course, you have worked on a real-world dataset individually as
well as collaboratively with your peers. Now you are given an opportunity to reflect on
what you have done for the project by answering the following questions:
(I) Short Answers:
a) Describe the project background, problem and aims in your own words. Don’t just
copy from either your individual report or group report.
b) How did you apply the results or findings of your individual assignment 1 in the group
project? If your results or findings were not adopted in the group project, explain how
the group achieved the consensus.
(II) Longer Answers:
2023S1 QBUS3600 Individual Assignment 2 3
a) Critically reflect upon your role in the group project and summarize your technical
contribution. What models/tasks did you try and how important are these towards
the final group report/recommendations?
b) What were the major difficulties you faced related to the project? If none, how would
you describe the difference between the theory/models you have studied so far in the
class settings and the real-world practical problem(s) seen in this semester?
c) If you had the opportunity to re-consider this project as a data scientist at BigW or
Mad Paws, what would you do differently and/or like to investigate further?
Your essay should be structured in a coherent way to smoothly and logically answer the above
five questions (a)-(e).
3. (10 marks) Presentation
20 marks are allocated for overall presentation of the report.
Marking and Key Rules:
Your report will be marked against the following principles:
• Demonstrate a clear understanding of the topics you are discussing
• Draw on insights and learnings from guest lectures throughout the semester
• The report is well structured and coherent
• The reflection is critical, sound, and logical
• Explain things clearly with specific examples, e.g., how lessons will be taken forward
in this and future projects
• Clearly draw conclusions based on analyses and well-grounded arguments
• Clear and commented Python code for Task 1, if analysis conducted
• Statements are clear, concise and accurate, with correct spelling, free of grammar
errors and correct use of punctuation
• Use of visual presentation is appropriate if any
• Closely follow a referencing style specified in Business School Referencing Guide (e.g.
APA) with consistency
A formal marking rubric will be uploaded to canvas.