Assignment specification Produce a report as a PDF file (3500–4500 words) based on a Jupyter Notebook of a data visualisation-led investigation different from coursework assignment 1. Your pr

Data Visualisation

Coursework description

1.1 Assignment specification

Produce a report as a PDF file (3500–4500 words) based on a Jupyter Notebook of a data visualisation-led investigation different from coursework assignment 1. Your project must be based on analysis of two datasets found online and publicly accessible. IMPORTANT: beware of data found on Kaggle with multiple python codes/analysis. This will reduce your score.

Write your report as a Jupyter notebook using inline markdown.
You must also submit a PDF as a hard-copy (using ‘print to pdf’ in the browser is fine you don’t have to install XeLaTeX to export from within Jupyter).
Your ZIP file must include:
- your notebook (ipynb)
- a copy of the public data used in your analysis
- any supplementary scripts
The maximum word limit is 4,500 words (suggested range 3,500–4,500 words).
Include any supplementary information not essential to the main body of the report as appendices. References and appendices do not count towards the word limit.
No marks will be directly awarded for material submitted in appendices.
No marks will be awarded for analysis discussion submitted as comments in code cells.
Do not put the PDF inside the ZIP!

1.2 Report guidelines

Reports should include discussion of the following points.

Research topic and background [15%]
- Introduction
  - overview of topic
  - relevant news or research articles
  - research objectives and motivation
  - overview of key findings
- Research question(s)
  - population and sampling method
  - explicitly stated research question(s)
  - scope (should be appropriate for the assignment)
- Domain concepts
Data sources [5%]
- Briefly explain how you use the two datasets in this project.
  - where/how did you find them?
  - how/why was the data initially collected?
  - are there any ethical or legal issues?
  - critically evaluate your data, is the data trustworthy, and valid for your purposes?
Data overview and pre-processing [10%]
- Data types and pre-processing
  - brief description of key variables
  - describe and justify data cleaning and preprocessing (i.e. tidy data)
  - handing of missing or erroneous data
- Data summary statistics
  - number of observations in the data
  - summary of demographics and key variables
  - use of tables or easily understandable quantities in pros
Analysis [50%]
- Visualise key variables.
- Visualise relationships between variables.
- Aim for high quality explanatory visualisation that describe or tell a story about the behaviour or phenomena under investigation.
- Aim for one high quality advanced visualisation (choose from topics 6-10).
- Marks will be awarded for (see rubric for more detail):
  - appropriate plots for variable data types
  - presentation quality
  - visual communication
  - methodical data visualisation process
Conclusion and evaluation [10%]
- Summarise key findings.
  - future directions
  - evaluate your process and visualisations
  - things to improve and/or pointers to future research
Code [10%]
- All python code should be submitted in your notebook (.ipynb file).
- All pre-processing and data cleaning should be implemented in code for transparency and reproducibility (do not manually edit data in a spreadsheet programme or hard-code data values in your notebooks).
- Code should be legible, with brief comments.
- Re-using and adapting code you find in documentation or elsewhere online is acceptable, but sources must be attributed correctly (web link and date accessed).
- Re-using and adapting code covered during the module is encouraged.
- Make sure all code runs correctly prior to submission.

clearly define important terms and concepts in the study

Native Singapore Writers Team

Assessment Criteria:

Please refer to Appendix C of the Programme Regulations for detailed Assessment Criteria