代写FIT5147、代做Python编程设计

日期：2025-03-12 10:14

Monash University

FIT5147 Data Exploration and Visualisation

Semester 1, 2025

Data Exploration Project

Part 1: Data Exploration Project Proposal

Part 2: Data Exploration Project Report

You are asked to explore and analyse data about a topic of your choice. It is an individual assignment and

worth 35% of your total mark for FIT5147. Part 1 Project Proposal contributes 2% and Part 2 Project Report

contributes 33%.

Relevant Learning Outcome

● Perform exploratory data analysis using a range of visualisation tools.

Overview of the Assessment Tasks

1. Identify the project topic, some related questions that you want to address, and the data source(s)

that you will be using to answer those questions.

2. Submit your Project Proposal (Part 1) in the Assessments section of Moodle in Week 3.

3. Discuss with your tutor in your Week 3 Applied Session (after the submission in Moodle) and wait

for approval from your tutor before proceeding further. Do not seek approval from the lecturer.

4. Collect data and wrangle it into a suitable form for analysis using whatever tools you like (e.g., Excel,

R, Python).

5. Explore the data visually to answer your original questions and/or to find other interesting insights

using Tableau or R. The exploration must rely on visualisations and visual analysis, but can analytical

methods or statistical analysis where appropriate.

6. Write a report detailing your findings and the methods that you used. This must include properly

captioned figures demonstrating your visual analysis (i.e. your visualisations must be referred to

correctly in your report).

7. The Project Report (Part 2) is due in Week 7.

Read the rest of this document before deciding on your project topic, as the proposal is for the entire Data

Exploration Project and Data Visualisation Project, which is the second major assignment of this unit. See

the end of this document for an example proposal and potential data sources to get started. Be careful not

to copy this proposal; it is an example proposal, not template text.

Choosing a Topic and Data

The choice of topic, data, and the questions you seek to answer should allow for interesting and detailed

analysis in the Data Exploration Project (DEP) and the subsequent Data Visualisation Project (DVP, due at the

end of semester), which involves presenting the findings from your DEP in a specifically designed narrative

interactive visualisation format.

Good questions are general and not linked to specific parts of the data, allowing for more open-ended and

exploratory analysis. For instance, asking “Where is the safest part of the network?”is a good question that

lets you explore various interpretations of how to link terms like “where” and “safest” to the data about a

network, whereas “Which region has the lowest value of number-of-deaths?” is not a very good question as

it is very specific to the data, is easy to answer with one visualisation and therefore limits the exploration

and visualisation possibilities.

It is strongly recommended that you avoid questions that are:

● too easy to answer (e.g., what is the correlation between x and y, what is the average value of z

variable, what are the top/bottom N values), or

● too difficult to answer (the work would take longer than the time available in the unit), or

● not relevant to the unit (e.g., training a machine learning model), or

● are not possible to answer from the available data.

Proposals with such questions will be rejected. If you are in doubt, talk to teaching staff during face-to-face

teaching times or ask for confirmation on Ed.

How do you know if you have appropriate data? This depends on your topic and questions. You should

ensure your data is big enough, i.e., has enough breadth and depth to invite interesting exploration.

Combining data from different data sources is an ideal way to help add to the originality of the topic. To

encourage different visualisation techniques your data will likely have a mixture of different data types.

Time series (whether this be aggregated or detailed, such as months and years, or milliseconds) may be

useful for your topic, and spatial, relational or text based data add useful complexity. If in doubt, talk to

teaching staff during face-to-face teaching times or in a consultation before the due date.

The chosen topic should be topical and some of the data should be recently collected, ideally from the last

two or three years. The data must be accessible to the teaching staff, so the use of open data is

encouraged (see the list of suggested data sources at the end of this document). Use of closed or

proprietary data is allowed as long as explicit permission for use in this assignment is granted by the

original authors or copyright holders. If you have closed data, you must still make it available to your

teaching staff to access, i.e., via a shared Google Drive.

Avoid common topics. Common topics including COVID-19, Netflix, AirBnB, car accidents, crime, house

sales, car sales, world cup soccer, or electric vehicle sales should be avoided. Topics similar to the proposal

example at the end of this document, i.e., traffic accidents and poor weather, must also be avoided. If you

do have personal motivation for any of these mentioned common topics, you will need to propose a

completely new angle to exploring the theme through novel questions with a mixture of new data sources.

It is highly recommended to discuss your intentions with the tutor of your Applied Session prior to the

proposal submission to avoid immediate rejection of the proposal.

Part 1: Project Proposal (2%)

Write a one-page PDF document consisting of the following sections:

1. Project Title

A descriptive title for your project.

2. Topic Introduction

One paragraph introducing the topic. This should include why it is a topical subject (for example,

has it been in the news recently), and who might benefit from the insights you seek from your

questions.

3. Motivation

One paragraph describing why you personally are motivated to study this topic.

4. Questions

Three questions you wish to answer using the data.

5. Data source(s)

Briefly describe the data source(s) you will use. This should include: URLs of data source(s) and a

description for each source: what is the data about, what is the size of the data (e.g., number of

rows, number of columns), the type of data (e.g., tabular, spatial, relational, or textual), the type of

attributes (e.g., categorical, ordinal, etc.) and the temporal intervals and period (e.g., monthly

between 2019 and 2023).

6. References

The bibliographical details of any references you have cited in the previous sections.

Include your full name, student ID, tutor names, and Applied Session class number. This can be in the

document header or footer. There should be no cover page.

Part 2: Data Exploration (33%)

The report should have the following structure:

1. Introduction

Topic detail, problem description, questions, and brief motivation.

2. Data Wrangling and Checking

Description of the data and data sources with URLs of the data, the steps in data wrangling

(including data cleaning and data transformations) and tools that you used. The data checking that

you performed, errors that you found, your method and justification for how you corrected errors,

and the tools that you used. A comprehensive checking process is expected to justify data

correctness, even if the data set is believed to be clean.

3. Data Exploration

Description of the data exploration process with details of the visualisations (including figures and

descriptions of findings) and statistical tests (if applicable) you used, what you discovered, and what

tools you used.

4. Conclusion

Summary of what you learned from the data and how your data exploration process answered (or

didn’t answer) your original questions.

5. Reflection

Brief description of what lessons you learnt in this project and what you might have done differently

in hindsight.

6. Bibliography

Appropriate references and bibliography (this includes acknowledgements to online references or

sources that have influenced your exploration) using either the APA or IEEE referencing system.

Include your full name, student ID, tutor names, and Applied Session class number. This may be on a cover

page, or in the header or footer of the first page.

The written report should be not longer than 10 pages for all sections mentioned above, excluding cover

page, table of contents and appendix. Your written report will be the sole basis for judging the quality of the

data checking, data wrangling, data exploration, as well as the degree of difficulty. Thus, include sufficient

information in the report. It should, for instance, contain images of visualisations used for exploration and

the results of any statistical analysis. You should include any analysis that you carry out even if it is

incomplete or inconclusive as it demonstrates that you have thoroughly explored the data set.

If you wish to provide additional material, an Appendix of up to 5 pages may be added at the end of the

document. However, the Appendix will not be marked. Therefore, you should only use it to provide

supplementary material that is not essential to the report or the reader's understanding. Be sure to clearly

title this section as Appendix.

Marking Rubric

Part 1: Project Proposal (2%)

● Completeness and Timeliness [1%]: All components of the Proposal are included and it is submitted

on time.

● Suitability and Clarity [1%]: Motivation, Questions and Data Sources.

Motivation: A well-formulated project description with detailed information; a compelling and worthwhile topic to

explore and visualise as a real-world problem.

Questions: Three well-crafted questions that can be clearly answered through data visualisations. Each question

requires sophisticated analysis of relationships and patterns across multiple attributes and demonstrates potential for

innovative visualisation approaches to reveal insights and complex patterns.

Data Sources: A clear description of data sources and datasets, including justification for which questions you will

answer with each. The data must be sufficiently large or complex to require exploration and analysis. All datasets must

be easily available, with URLs provided. For private and proprietary data, evidence of permission and a link to the

dataset must be provided.

After submission you will meet with your tutor during the Week 3 Applied Session to discuss your Project

Proposal, receive feedback and ideally approval to start. If your proposal is rejected, your tutor will specify

the reasons and suggest areas for improvement. You will need to make these amendments to your proposal

and get it approved by your tutor prior to commencing your project work.

Part 2: Project Report (33%)

Criteria Below 50% Pass (50%+) Credit (60%+) HD (80%+)

Data Complexity,

Wrangling, Checking

and Cleaning (7%)

Inappropriate checking,

cleaning, or wrangling.

0 if no demonstration of

data checking and

cleaning.

Appropriate data

cleaning and checking.

Demonstrated ability to

get data into R or

Tableau;

Good choices and clear

justifications for error

checking, cleaning and

transforming of

non-tabular data (e.g.

spatial, relational,

textual); large datasets

(observations or

dimensions) and/or

multiple data sets.

Excellence in data

processing

demonstrated and

documented. Evidence

of significant complexity

in the wrangling,

cleaning,

transformation, or data

collection (e.g.

scrapping).

Data Visualisation and

Design Choices (9%)

No visualisations;

unsuitable or poor

choice of visualisations;

pixelated / poor quality

images or illegible

visualisations.

0 if not using Tableau or

R.

Suitable visualisations,

which are well

presented, described,

readable and

interpretable.

Visualisations are

appropriate for the

intended purpose;

appropriate labeling of

axes and visualisations;

clear legends when

needed; saliency of

patterns and trends.

Variety of high-quality,

complex and/or creative

visualisations with high

attention to detail.

Clearly justified design

choices incl.

visualisation idioms,

choice of visual

variables, layout and

labelling.

Analytical Methods and

Interpretations of Data

and Topic Questions

(9%)

Unsuitable analysis or

misinterpretation of the

data and topics

questions.

0 if no data analysis is

demonstrated.

Demonstrated suitable

analysis and

interpretation of the

data and topic

questions.

Analysis that is

appropriate for the

intended purpose;

justification and

explanation of the

exploration process and

use of statistical

measures; identification

of trends, patterns, and

insights.

High quality of visual

analysis demonstrated.

Sophisticated and

correctly used analytical

methods such as

clustering;

dimensionality

reduction; sophisticated

aggregation and/or

filtering; non-linear

model fitting; correct

use of statistical tests;

or complex time series

analysis.

Written Report: Quality

and Completeness (8%)

Poor report, or missing

sections.

Good report with logical

structure with all the

expected sections:

Introduction, Data

Wrangling, Data

Checking, Data

Exploration, Conclusion,

Reflection, Bibliography.

Referencing of sources,

figures and tables.

Correct grammar and

spelling.

High quality of writing

and figures/images with

minimal errors. Correct

referencing of figures

and tables within the

text, and correctly used

academic referencing of

sources.

Professional report with

excellence of writing

combined with high

quality figures/images.

Clearly articulated

findings; awareness of

limitations; deep

exploration; thorough

conclusions.

Originality

Since this is academic work, it must be original and clearly distinguish between your own contributions and

those based on other’s work. If you include data, facts, opinions or any other written or graphical

information from another source, you must cite and reference it according to the APA or IEEE style guide.

This includes third-party programming code, software used in data exploration and analysis, and any

definitions or descriptions of concepts or software. Direct quotations or reproductions must adhere to the

appropriate APA or IEEE style.

In your report you are encouraged to repeat the questions from your proposal. This is the only

self-plagiarism that is allowed. If you are retaking this unit from a previous semester, you must choose a

completely new topic and dataset. The topic and dataset cannot have been used in any other unit. You may

not reuse any code or written content from previous assessment tasks for any unit. Additionally, content

from previous assignments or sample reports cannot be used.

You may use Generative AI tools, such as ChatGPT, to improve writing and expression. However, your writing

must be logically structured, clear and concise. Repetitive, poorly structured, or vague gibberish as often

generated by Generative AI tools will result in a low grade. AI is generally unsuitable for data checking,

cleaning, wrangling, exploration and visualisation of this level and should be avoided. It is important to

remember that generated content can be biased. Any use of Generative AI in the preparation of your

assessment must be acknowledged at the end of your submitted document.

If concerns arise regarding the originality of your work – whether due to plagiarism, collusion, contract

cheating, or the use of unapproved software – your academic integrity will be reviewed. Confirmed

breaches of academic integrity may result in penalties affecting your assignment mark, this unit, or even

your enrolment.

Submission and Due Dates

Once you have completed your work, take the following steps to submit your work.

1. Save your proposal or report as a PDF document.

2. Name your file using the following structure: Proposal_Surname_StudentID.pdf or

DEP_Surname_StudentID.pdf

3. Submit and upload your document.

● Project Proposal: Submit a one-page PDF in Week 3.

● Project Report: Submit a 10-page PDF (excluding cover page and appendix) in Week 7.

See Moodle for dates and times.

Your assignment must show a status of ”Submitted for grading” before it can be marked. Any submission in

“Draft” mode will not be marked.

Late Submissions

● There will be zero marks for late Project Proposal submissions. Everyone must submit the Project

Proposal. Even if the deadline has passed, you must still submit a proposal (with a grade of 0) as

your project must be approved before you can continue working on the Data Exploration Project.

The proposal is a hurdle requirement. If it is not submitted and approved by your tutor, the mark for

the Data Exploration Project is 0.

下面这一部分全在说原创性

● For the Project Report, submissions received after the deadline (or after an extended deadline for

those with an extension or special consideration) will be penalised at 5% of the total available

mark [33%] per calendar day up to a maximum of 7 days. If submitted after 7 days, it will receive

zero marks and no feedback will be provided.

● For further information on eligibility for Extensions or Special Consideration, see:

https://www.monash.edu/students/admin/assessments/extensions-special-consideration

Example Data Sources

The following is a list of data sources to get started. Feel free to use these as a source of inspiration and

ideas for your project. You are not limited to the data sources listed below.

● Data search tools and repositories, e.g.:

○ Google dataset search: https://toolbox.google.com/datasetsearch

○ Google Trends: https://www.google.com/trends/explore

○ Google Ngram Viewer: https://books.google.com/ngrams

○ Registry of Open Data on AWS: https://registry.opendata.aws/

○ Kaggle: https://www.kaggle.com Note that using data from Kaggle exclusively is not

acceptable, you must use at least one additional data source.

○ Science Hack Day: http://sciencehackday.pbworks.com/w/page/24500475/Datasets

● Open local and national government data portals, e.g.:

○ Victorian Government Data: http://data.vic.gov.au/

○ Australian Government Data: http://data.gov.au/

○ National Map: https://nationalmap.gov.au/ (Australian data)

○ Australian Bureau of Statistics: https://www.abs.gov.au/statistics

○ Atlas of Living Australia https://ala.org.au/

○ European Union Open Data: https://data.europa.eu/en

○ UK Government Open Data: https://data.gov.uk/

○ U.S. Government Open Data: https://www.data.gov/

● Humanitarian data sources, e.g.:

○ UNdata: http://data.un.org/

○ The World Bank Data Catalog: https://datacatalog.worldbank.org/

○ Our World in Data: https://ourworldindata.org/

○ Berkeley Library Health Statistics:

http://guides.lib.berkeley.edu/publichealth/healthstatistics/rawdata

● Open corporate/industry data, e.g.:

○ Uber: https://movement.uber.com/?lang=en-AU

○ Inside Airbnb: http://insideairbnb.com/get-the-data.html

Example Project Proposal

Please note this mock example is relatively old now. We expect your data to ideally include recent data, i.e.,

data from 2022, 2023 or even 2024. It is possible to complete this example project with only Data Source A

and B, but C provides different opportunities and additional difficulty when doing the exploration and

visualisations. If done well, this added depth and difficulty can gain extra marks but might take longer to

complete. The student could use both datasets A and B to identify temporal aspects in the data, such as

accidents near to sunset and sunrise across the whole dataset, but dataset C allows them to identify areas

which are poorly lit and see if this correlates with the spatial pattern of pre-sunrise and post-sunset

accidents. Furthermore, whilst Data Sources A and C are currently tabular data, they can be converted to

spatial features and spatial analysis can be carried out.

Name: Jesse van Dijk, Student ID: 12345678, Teaching Associate: Jo Bloggs & Alex Smith, Applied 01.

Project Title: Causes of Serious Bicycle Accidents in Canberra

Introduction

Recent media and industry reports indicate that Australian roads are becoming even more dangerous for cyclists

[1,2]. I believe this is an important topic for many audiences such as cyclists, road safety officers, and public

health policy makers. Therefore I want to find out more about the factors that affect bicycle accidents in

Canberra.

Motivation

I am a keen cyclist and am concerned about cycling in Australia. I have recently moved to Canberra from the

Netherlands where cycling is very safe and accidents linked with road vehicles is unusual. I have noticed it is

difficult to see during sunset on a number of roads and would like to see if this pattern is evident in the data.

Questions

1. What are the most common kinds of serious bicycle accidents in Canberra, and how do these vary over

different time periods (e.g. hour of day/day of week/month/season)?

2. How do lighting conditions affect these accidents?

Data sources

A. ACT Road Cyclist Crashes 2012 to 2021, which have been reported by the Police or the Public through

the AFP Crash Report Form. This data is tabular data: ~1K rows × 11 columns. It has both spatial and

temporal attributes including the geographical (latitude and longitude) location and a datetime stamp

for the time of accident. Some numerical and simple text attributes relating to the incident. i.e. number

of casualties, description of accident, including direction of traffic.

B. Canberra’s sunrise and sunset times, 2012 to 2021. Tabular data in HTML: ~365 rows × 4 columns for

each year to be scrapped from sunrise website. Columns are simply date, time of sunrise, time of sunset

and hours of daylight.

C. ACT Streetlights, 2021. Tabular data in CSV format with ~80K rows × 10 columns. These include latitude

and longitude for the streetlight location and various text columns including lamp type, Luminaire,

height and street and suburb name. There is no date column for the age of the lamp, but the source of

the data is dated from 2017 and was last updated in Nov 2021.

Data Source A will be used to address Question 1, whilst A to C will allow me to answer Question 2.