Faculty of Information Technology
Semester 1, 2024
FIT5145: Foundations of Data Science
Assignments 1 & 3: Business and Data Case Study
1. Assignment 1 (Proposal): Write a proposal document to introduce a data science project that you
are studying. The due date of Assignment 1 is: Sunday, 31 March 2024, 11:55 PM (Week 5).
2. Assignment 3 (Report+Presentation): Write a report on your case study of the data science
project and prepare a 4-minute presentation on your project. The due date of Assignment 3 (the
final project report and presentation slides) is: Sunday, 19 May 2024, 11:55 PM (Week 11) and
the presentation will be held in the Week 12 applied class.
- Both Assignment 1 and 3 are individual assignments.
- Please do NOT zip your submission files. Zip file submission will have a penalty of 20%
of the total mark of the assignment.
Focus of the study
This case study needs to analyse (“study”) a data science project relevant to a business scenario (“the
case”). There are a couple of ways you can choose to do the case study. For instance, you may choose to
study how an existing data science project has been implemented in a particular sector such as
transportation or healthcare. The project chosen is NOT limited to those already established or
completed. You can propose an entirely new and novel project of your own, e.g., how can the Australian
government tackle bushfire or flood by making use of Twitter data? That being said, you can either study
an existing data science project or propose a completely new data science project. Talk to your tutors
about any proposed project you are interested in.
Assignment 1: Proposal (15%)
Weight: 15% of the unit mark
Submission format: one PDF file
Size: up to 700 words.
What you need to do:
● Choose a data science project.
● Write the initial two sections, Project Description and Business Model (References as well to
support your project) of the report, as detailed in the specification of Assignment 3: Report +
Presentation below. This assignment is worth 15% of your unit mark.
We have developed a system named FLoRA to support you to accomplish Assignment 1, which you may
access via: https://www.floraengine.org/moodle/my/courses.php. You are expected to work with the
chatbot embedded in FLoRA, powered by cutting-edge Generative Artificial Intelligence (GAI)
technology GPT-4, to accomplish the assignment. You can discuss the assignment requirements with the
GAI-powered chatbot, seek suggestions for potential project topics in a specific domain (e.g., education,
agriculture, sports, and finance), and gather information relevant to a specific project topic by conversing
with the chatbot. Even more, you may send the proposal draft to the chatbot and seek feedback for further
improvement.
Please notice:
● You should log in to the FLoRA platform by using your Monash email account and we will send
you login credentials via emails.
● You are only expected to use FLoRA to accomplish Assignment 1, though you may use it for
Assignment 3 as well, it is not mandatory.
● The conversational data you generate when interacting with the GAI-powered chatbot will be
used for textual analysis in Assignment 4 in this unit. That is, the conversational data will be
shared with the whole class and thus please only discuss your data science project with the
GAI-powered chatbot and do not disclose any personal or sensitive information during this
process.
After logging to the platform, you will see there are four modules required to accomplish Assignment 1,
as shown below:
Module 1: Pre-task activity
● Please provide information about yourself as well as your prior knowledge and experience in
data science and GAI.
Module 2: Training Module
● We provide a set of tutorial documents to help you familiarise with FLoRA, including:
○ The system interface;
○ The annotation tool, which you can use to make annotation to the reading materials
provide to help you get some inspirations about potential project ideas before discussing
with the GAI-empowered chatbot;
○ The essay writing tool, which you can use to draft the assignment;
○ The GAI-empowered chatbot, which you can consult for help when solving the
assignment.
● Please notice that all these tools have been enabled in Module 2 (available on the top right
corner) and you may familiarise yourself with these tools first before moving to Module 3 to
start working on the assignment.
Module 3: Task - Assignment
● This is the main module in which you are expected to accomplish Assignment 1. Before
discussing with the GAI-powered chatbot, please first have a look at the “inspiring” materials
that may give you some initial ideas of what data science can achieve in the following domains:
○ Data Science in Agriculture
○ Data Science in Education
○ Data Science in Finance
○ Data Science in Gaming Industry
○ Data Science in Healthcare
○ Data Science in Social Media
○ Data Science in Sports
You may use the provided annotation tool to annotate the useful information in these materials.
You may select a project topic from these domains. You can also select project topics that are
outside of these domains.
● Use the GAI-powered chatbot to get necessary help for accomplishing the assignment.
● After you finish the draft, please (i) click the “Save Essay” button to send your submission to
FLoRA; and (ii) copy your project text and paste it into a word processing tool (e.g., Microsoft
Word), format it if necessary, and then save it as a PDF file and submit it on Moodle as well.
● Please notice that the conversational data you generate with the GAI-powered chatbot will be
used and shared for Assignment 4 and thus do not disclose any personal or sensitive
information to the chatbot. Your project proposals or any other personal information will NOT
be used or shared for Assignment 4.
● As we will use the conversational data for Assignment 4, ideally you have one “meaningful”
discussion session with the GAI-powered chatbot (instead of having multiple at different times) in
Module 3 to get the help you need to accomplish Assignment 1. Prior to this, you may familiarise
yourself with the chatbot (and other tools as well) in Module 2.
Module 4: Post-Task Activity
● Please share your experience in using FloRA as well as the GAI-powered chatbot to tackle
Assignment 1. Your responses to these survey questions will be necessary for preparing an
anonymised conversational dataset for Assignment 4.
For any technical issues in using FLoRA, please contact guanliang.chen@monash.edu and
xinyu.li1@monash.edu
Assignment 3: Report (20%) + Presentation (10%)
1. Assignment 3: Report
Weight: 20% of the unit mark
Submission format: one PDF file and one RMD file (for demonstration in the Characterising and
Analysing Data section)
Size: up to 2000 words
This report is your analysis of how data science can be used to help solve a particular problem. In your
report you need to identify the size and scope of both the problem and the data science project, as well as
the requirements of enabling the project.
Please answer the following question in the FIRST page of your Assignment 3 submission:
● Have you selected a topic for Assignment 3 that is different from the one that you used for
Assignment 1 (i.e., have you rewrote the first two sections of the report)?
Your report should have at least (but not limited to) the following sections:
● Project Description: provide a description about the data science project that you study/propose,
what the objective of the project is, and what data science roles (e.g., data scientist, data engineer,
system architect) are involved in this project and what are their responsibilities.
● Business Model: provide analysis about the business/application areas the project sits in, what
kind of benefits or values the project can create for the specific business area and who can benefit
from, and what the challenges of the project are.
● Characterising and Analysing Data:
○ Discuss potential sources to collect the data, provide analysis about the characteristics of the
data (e.g., the 4 V's), provide analysis on the required platforms, software, and tools for data
processing and storage, according to the specific data characteristics.
○ Specify/propose the data analysis and the statistical methods (e.g., decision tree and
regression tree) used in the project, provide analysis on why you choose those methods and
discuss the high-level output.
○ Demonstration: identify a usable dataset for the proposed project and perform some basic
analysis on the identified dataset to demonstrate the feasibility of the project, using R (e.g.,
detailing the information/features contained in the dataset, analyse the basic characteristics of
the dataset, etc.), and report the analysis process and result in the demonstration section of a
final report.
Note: Students will get more marks if they use realistic data. If the realistic data is not
available, please create a mockup/example dataset to clearly explain the proposition,
modelling approach, and visualisations that can be derived from it. (please include a link to
download the data in the final report, and upload the R markdown file created for data
analysis on Moodle).
● Standard for Data Science Process, Data Governance and Management: describe any
standard used in your data science process, and describe appropriate practices for data
governance and management in the project, e.g., issues related to the accessibility, security, and
confidentiality of the data as well as potential ethical concerns with the use of the data.
The sections would present aspects of Weeks 1-10 of the unit for your chosen case study.
The maximum word limit for the report (Assignment 3) is 2000 words. It may include some/all of your
Assignment 1, modified if needed (counted in the 2000 word total). References at the end of the report
(i.e., URLs and academic publications) are not included in the word count. Note that staying within the
word limit demonstrates your ability to write concisely.
2. Assignment 3: Presentation (Slides + Verbal Presentation)
Weight: 10% (Slides and Verbal Presentation) of the unit mark
Submission format: one PDF file (Slides)
Size: a maximum of 10 slides (Slides)
You need to submit your presentation slides along with your final report. The 4 minute presentation
is given in Week 12 during your assigned applied class and after your presentation, the tutor will ask
at least one question to the presenter (1 minute). You will also be required to review and provide
feedback on presentations of other students (peer-review) during the applied class in Week 12, using
the Google Form provided.
How you will be assessed
Assignment 1 proposal: The 15% awarded for your proposal is broken down into the following
categories:
● clear description of the goals of the project;
● appropriateness of topic;
● clear description of the business benefits and challenges ;
● novelty/creativity (this one is of extreme importance!);
● overall clarity of the initial report.
Assignment 3 report: See the marking rubric to understand how we will grade your report. You will
be assessed on your ability to:
● provide a description about the data science project that you study/propose, what the objective of
the project is, and what data science roles (e.g., data scientist, data engineer, system architect) are
involved in this project and what their responsibilities are;
● provide analysis about the business/application areas the project sits in, what kind of benefits or
values the project can create for the specific business area and who can benefit from, and what the
challenges of the project are;
● discuss potential sources to collect the data, provide analysis about the characteristics of the data
(e.g., the 4 V's), provide analysis on the required platforms, software, and tools for data processing
and storage, according to the specific data characteristics;
● specify/propose the data analysis and statistical methods used in the project, provide analysis on
why you choose those methods and discuss the high-level output, etc;
● identify a usable dataset for the proposed project and perform some basic analysis on the identified
dataset to demonstrate the feasibility of the project, using R (e.g., detailing the information/features
contained in the dataset, analyse the basic characteristics of the dataset, etc.), and report the
analysis process and result in the demonstration section of a final report;
Note: Students will get more marks if they use the realistic data. If the realistic data is not
available, please create a mockup/example dataset to clearly explain the proposition, modelling
approach, and visualisations that can be derived from it. (please include a link to download the
data in the final report, and upload the R markdown file created for data analysis on Moodle).
● describe any standard used in your data science process, and describe appropriate practices for data
governance and management in the project, e.g., issues related to the accessibility, security, and
confidentiality of the data as well as potential ethical concerns with the use of the data.
● think critically and creatively, providing justification and analysis;
● provide a good quality of report in terms of structure, expression, grammar and spelling.
For both assignments, make sure that any resources you use are acknowledged in your report. You may
need to review the FIT citation style to make yourself familiar with appropriate citing and referencing for
this assessment. Also, review the demystifying citing and referencing guide for help.
Please also make sure that the Turnitin scores will be generated properly for your submissions. If a
submission receives a high Turnitin score (e.g., more than 15%), the student will likely need to provide
further explanation on the project idea and a penalty might be imposed on the submission in case no
proper justification is provided.
Assignment 3 Presentation (Slides + Verbal Presentation): The 10% awarded is broken down into
the following categories:
● Presentation slides (4%)
● Verbal presentation (4%)
● Peer-review evaluation (2%)
What you need to do
Before you begin, make sure you:
● Download the Business & data case study - Example topics (available on Moodle) and review
them as examples.
o You may select one of those cases as the basis of your report, but please avoid using those
that have been well-investigated, e.g., how Amazon enables personalised product
recommendations to its customers as this will likely result in a high Turnitin score to your
report and a low score in novelty/creativity.
o You are highly recommended to propose your own interesting and novel topic and please
feel free to discuss it with your tutors to ensure the topic is suitable.
● You are also highly recommended to review the “inspiring” materials provided in FLoRA to select
a topic that you would like to work on.
● Download the marking rubric (available on Moodle) as guidance on how you will be assessed.
Choose a data science project as a case, and then:
● Do preliminary research about your case and the relevant technologies
● Write and submit your proposal with cited references (Assignment 1)
● Research and prepare your final report with cited references.
● Submit your report and do a presentation (Assignment 3).
You are free to modify the initial proposal sections submitted for Assignment 1 (especially in response to
feedback from your marker), or even change topics, when you are working on Assignment 3.
How to Submit
Once you have completed your work, take the following steps to submit your work. Penalties may be
applied to your marks if the following instructions are not followed.
1. For Assignment 1, please finish the project proposal first in FLoRA, copy & paste it into a word
processing tool (e.g., Microsoft Word) for the purposes of structuring/formatting or including
visuals if necessary, then save the project proposal in the PDF format and submit it on Moodle.
2. Please ensure you name the file containing your proposal/report/slides correctly using the
following format:
LastName_StudentNumber_AssignmentNumber(_report or Rmd or slides).pdf
e.g., Guanliang_12345678_Assignment1.pdf or Guanliang_12345678_Assignment3_report.pdf or
Guanliang_12345678_Assignment3_Rmd.Rmd or Guanliang_12345678_Assignment3_slides.pdf
3. Upload your assignment file in the corresponding assignment link provided on Moodle.
Those unable to attend week 12 applied class for presentation:
Those who cannot do the presentation in person due to valid mitigating circumstances, can arrange with
their lecturer ahead of time, to record and submit a video 4 minutes duration, through Youtube along with
slides. Please notice that, if you choose to do a video presentation, that means you will lose the 4%
verbal presentation as well as the 2% peer-review evaluation in Assignment 3. We recommend you
produce a video by doing a 4 minute screen capture of your slides with voice over entered concurrently via
microphone. You must upload your video through Youtube (make it unlisted, not private), and provide a
LINK to your Youtube video in your submission (on the first page of the presentation slides) on Moodle.
However, DO NOT include your video on Moodle as part of the submission. Please check the details of
this, and confirm with a lecturer and your tutor a week in advance of presentation week.
Further advice on the assignment:
Here is some further advice from the teaching team regarding the assignment:
1. Make sure to carefully read the assignment specification above.
2. The project should be data-centred -- ideally combining multiple sources of studies to develop
your own project that can solve a real-world problem.
3. The project should contain a clear statement of the problem being tackled. What is the
objective/purpose of the project? Have a look at the structure of the example case studies,
each one starts with a clear definition of the problem.
4. Make sure that the benefit of the project is clear. What is it? Will the project have a financial
benefit, or result in a social good?
5. The report needs to be "telling a story", and to be convincing somebody to "invest in your
project" so that it can be built.
6. Try not to make the project too broad. It should be an achievable data science project.
7. For both Assignment 1 and Assignment 3, you are highly recommended to include visuals to
explain your idea more clearly and support your claims and in lesser words. For example,
depending on your project it may (or may not) make sense to include:
o an influence diagram showing what data is available and how it relates to the decisions
and objectives of the project,
o graphs showing some data analysis (if applicable).
For Assignment 1, you may add the visuals after you copy and paste the project proposal into
a word processing tool (e.g., Microsoft Word).
8. Read up as much as you can on the particular topic you've chosen in order to be able to
describe the data (and software) requirements of the project.
9. Make it clear where the data would come from for the project:
o Is the data proprietary? How would it be collected?
o If the data is public, you should do some exploratory data analysis on it.
10. What preprocessing would be needed? How would the data need to be preprocessed before it
can be used? What software might be needed? Can the preprocessing be distributed?
11. Finally, make sure you've seen the set of possible section headings suggested above and
structure your report accordingly.