My second data science assignment as part of my Data Science in Python module
The objective of this assignment was to construct different time series representations for three countries based on the supplied mobility data, and analyse and compare the resulting series
The first task involved within-country analysis. For each of the three countries, this involved:
- Constructing a set of time series that represented the mobility patterns for the different location categories for the country (e.g. workplaces, residential, transit stations etc).
- Characterising and visualising each of these time series.
- Comparing and contrasting how the series for the different location categories changed over time for the country
- Suggesting explanations for any differences that were observed between the time series for the location categories.
The second task involved between-country analysis. Taking the three countries together, the requirements were to:
- Construct a set of time series that represent the overall mobility patterns for the three countries.
- Characterise and visualise each of these time series.
- Compare and contrast how the overall time series for the three countries changed over time.
- Suggest explanations for any differences that were observed between the time series for the countries.
I received an A+ for this assignment because the assignment had:
- Extremely detailed level of analysis across both tasks, including decomposition of the series into their different components and pairwise correlation analysis.
- In Task 2 the 'overall' country time series were constructed correctly.
- Very detailed interpretation and discussion was provided in both tasks.
- Clear code structure, clearly explained and commented.
- Good use of different visualisations throughout the notebook to support the analyses of the time series.
- Overall, this was an excellent assignment submission which showed a substantial amount of effort.