代写COMM5501 Data Story Project Guide代写Python语言
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COMM5501
Introduction
The major project for COMM5501 is structured to provide students with a step-by-step guide to build their own data story on a topic of their own choosing, related to the UN Sustainable Development Goals (SDGs). A link to the SDGs is included HERE for your convenience.
Students will need to select a contemporary challenge related to the SDGs, find the relevant data, process and present this data in an insightful and coherent manner, and apply their own judgement based on their findings to give an evidence-based recommendation to the identified challenge.
Although there is a “Data Story Content” assessment and a “Data Story Project” assessment as part of this course, we will use the term “Data Story Project” to refer to the overall process of creating your data story.
The first three components of the Data Story Project will focus on building content for your data story. The fourth component will combine the content from the first three into the final version of the data story, and students will present their collated work in an appropriate format (guidance will be provided). The fifth component will require students to showcase their work as part of their profes- sional portfolio.
This Data Story Project has a total weighting of 80% of your final grade for this course. The 5 com- ponents mentioned above will be submitted throughout the term. The key details for each component are provided below.
Please note that this document is only a guide for what to expect, as we may make changes during the term to respond to unforeseen circumstances. This document should not be seen as being set in stone.
0 Engagement and Participation
A large component of success in this course is the ability to interact with your peers for feedback. The labs/tutorials in particular are critical to making sure that you are on the right track. Based on feedback from previous students, we are reinforcing this message by including a lab participation component to this course.
There are 9 labs in total. Students will receive the full 10% weighting if they participate in at least 7 labs. 1.5% is lost for each lab missed below 7 (e.g. attending 6 labs means that you lose 1.5%), down to a minimum of 0%. We, of course, strongly recommend that students attend all labs, but we also understand that sometimes unforeseen circumstances prevent you from attending all classes.
If you cannot attend your regular lab for any week, you can attend a different one for that week, but you will need to notify the new tutor.
The first component (excluding component 0 above) of the Data Story Project will introduce students to the Data Story Project by proposing a topic that is both of interest to them and has a meaningful impact on the broader society. The chosen topic will need to connect to at least one of the UN SDGs.
For this chosen topic students will need to provide:
1. An Impact Statement, containing:
• A brief summary of the problem they would like to address,
• A clear and specific Call to Action, and
• A clear and specific Target Audience to enact your Call to Action.
2. A relevant data set from a reputable source that can support this topic.
This will serve as a starting point for subsequent components of the Data Story Project.
The purpose of this submission is to receive formal feedback from a member of the teaching team (students would already have received peer feedback before submitting) and refine ideas. A good Call to Action and Target Audience are needed to succeed in the rest of the Deliverables.
NOTE 1: Students are NOT locked into this topic for the final version of their data story, and are allowed to adjust their topic statement/question as they progress through the semester.
NOTE 2: Students will have three attempts to meet the requirements of Deliverable 1. See 1.6 for details. If a student has not successfully completed Deliverable 1 within the three attempts, they will score 0 for this component.
This component is has a 5% weighting towards your final grade.
Topic 1 will contain various activities to support students in exploring the SDGs broadly. The lecture for Topic 2 will provide an introduction to writing an effective Impact Statement, and the correspond- ing lab in week 2 will contain a guided activity for students to write their own Impact Statement.
Students will also post a copy of this component for their formative forum post for week 2, where they will receive peer feedback. Students are encouraged to take any additional feedback they receive here into consideration before submitting the deliverable for this task.
1.3 Deliverables (i.e. what you need to submit)
Students will post an updated version of their work, implementing all the feedback they’ve received and make any changes they feel are necessary,to the Deliverables section on Moodle to receive feedback from the teaching team. Your post will need to contain the following:
• A single-sentence topic statement/question,
• A corresponding Impact Statement (max 200 words),
• A link to the chosen data set, a brief description of the data set, and a proposal for how the chosen data set might be used to support the Impact Statement (max 50 words).
The first attempt is due at 11:59pm Friday of Week 3. See Section 1.6 for the full details of the three attempts.
1.5 What makes a good submission?
Tutors will assess submissions based on the first two categories (and the 6th “Other” category) of the Deliverable 4 rubric (see Table 3). Explicitly, these are the same criteria that will be used to assess your submission at the end of the term. You will receive the full 5 marks if you have met ALL of these requirements, and 0 otherwise.
See Section 4.5 for the full process of how the teaching team will assess Deliverable 4.
1.6 How do I use my three attempts
Each student will have three attempts to pass Deliverable 1. Once you have passed, you will not need to make additional attempts.
NOTE 1: Specific instructions will be provided on Moodle around the start of week 5 for what is required for those that are required to make a second and third attempt.
NOTE 2: The three attempts must be completed within the time frame described above. Missed or skipped attempts (without special considerations) will be considered forfeited and cannot be carried over or used at a later time. It is your responsibility to ensure you make use of each attempt within the given time frame.
The second component of the Data Story Project will take the ideas of “good” and “bad” data visu- alisations and apply them to their chosen topic.
Students will take the data set they chose from the “Choosing your Challenge” component (or another data set if necessary) and document their process of improving their first chart, the types of feedback they received, and how they implemented this feedback.
This task serves multiple purposes:
• Documenting the process with clear notes creates a reusable resource for referring back to the process you used to create your graph.
• It reduces the chance of repeating the same mistakes and speeds up the process for creating your subsequent graphs.
• More broadly, it reinforces the learning process. You are very likely learning a relatively new skill, and it’s very easy to forget a detail if you don’t write it down (this is still true if you’re refining an existing skill).
You will receive peer feedback throughout this process. This component will have very limited tutor feedback.
This component is has a 5% weighting towards your final grade.
NOTE: Students will have three attempts to meet the requirements of Deliverable 2, and this must be completed prior to the start of week 7. If a student has not successfully completed Deliverable 2 within the three attempts, they will score 0 for this component.
The lab in week 3 will contain guided activities to help students build effective data visualisations, and receive peer feedback on their work before submission. The StoryWall post in week 3 will give students another opportunity to receive additional feedback from peers. The lab in week 4 will have an activity to support your first attempt at this Deliverable.
Some of the elements from Topic 4 on stakeholders may also be relevant for this component, as a large portion of understanding the purpose of a graph comes from understanding the target audience as a stakeholder.
Students will need to create and maintain a document containing the main iterations of their graph supporting the Impact Statement. This document does not need to be formally submitted, but it will be needed for the three attempts (see Section 2.6 for details). You do not need to include every single version of your graph, just key checkpoints and major changes.
This document also needs to have brief notes on the changes made between each iteration. These notes should contain not only the change being made, but should also mention the rationale behind the change (i.e. Why did you make that change?). The notes can be dot points, but you can also have more text if you feel this is necessary.
These notes should be detailed enough to be a convenient reference material for yourself later in the term. A sample has been provided on Moodle for what this may look like.
The first attempt is due in your week 4 tutorial. See Section 2.6 for the full details of the three attempts.
2.5 What makes a good submission?
Tutors will assess submissions based on the Data Analysis and Visualisation category of the Deliverable 4 rubric (see Table 3). Explicitly, these are the same criteria that will be used to assess your submission at the end of the term.
Tutors will assess the first attempt as part of the Week 4 tutorial activity. If a student is unable to attend their tutorial in week 4, they can attend an online Teams session for Deliverable 4 (these will be announced closer to the date). However, there will be limited availability in these online sessions, so completing this activity in tutorials is highly recommended.
2.6 How do I use my three attempts?
Each student will have three attempts to pass Deliverable 2. Once you have passed, you will not need to make additional attempts.
Note: The three attempts must be completed within the time frame described above. Missed or skipped attempts (without special considerations) will be considered forfeited and cannot be carried over or used at a later time. It is your responsibility to ensure you make use of each attempt within the given time frame.
The following flowchart illustrates this process.