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.

1. 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
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2. 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?
3. 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
4. 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
5. Conclusion and evaluation [10%]
   • Summarise key findings.
     • future directions
     • evaluate your process and visualisations
     • things to improve and/or pointers to future research
6. 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

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Native Singapore Writers Team

Assessment Criteria:

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

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