| 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
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- Introduction
- 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?
- Briefly explain how you use the two datasets in this project.
- 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
- Data types and pre-processing
- 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
- Summarise key findings.
- 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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Assessment Criteria:
Please refer to Appendix C of the Programme Regulations for detailed Assessment Criteria