This assignment will use employment data of Wales from the StatsWales data source. This dataset provides workplace employment estimates, or estimates of total jobs, for Wales and its NUTS2 areas, along with comparable UK data disaggregated by industry section.
For this assignment students will undertake a data analysis and machine learning approach to reveal the workplace employment landscape of Wales.
For each question provide graph/chart along with your own interpretation (~ 50 words)
1.1. Which industry employed highest and lowest workers over the period?
1.2. Which industry has the highest and lowest overall growth over the period?
1.3. Which years are the best and worst performing year in relation to number of employment. (highest and lowest employment)
Create a dynamic scatter/bubble plot showing the change of workforce number over the period using Plotly express.
3.1. Undertake a PCA (PC=2; columns should be like PC1, PC2, Industry) and produce a scatter plot. Write your interpretation about the plot and in relation to the analysis of section 2 & 3 (for example which industries are correlated over the years as well as in PCA etc.)
3.2. Make a year wise correlation for each industry. Does the aforementioned industries are also correlated over the years? Explain your answer.
4. Clustering (k means & hierarchical)
4.1. Using the best and worst performing year column’s employment data (2.3) undertake a K means clustering analysis (K=2 & 3) and identify industries cluster together. Write your own interpretation (~100 words).
4.2. Using the same dataset (best & worst performing) create a hierarchical cluster. Compare the cluster with k means clusters.
Provide a brief discussion (~ 300 words) on employment landscape of Wales based on the employment data analysis results.
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