0. 0. 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:

0. • ○  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)