Learning Outcomes:
A. Critically analyze and evaluate various statistical and computational techniques for analyzing datasets and determine the most appropriate technique for a business problem;
B. Critically evaluate, develop and implement solutions for processing datasets and solving complex problems in various environments using relevant programming paradigms;
C. Evaluate and apply key steps and issues involved in data preparation, cleaning, exploring, creating, optimizing and evaluating models;
D. Evaluate and apply aspects of data science applications and their use.
Assessment Requirements
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.
Part 1: Data processing
1.1. Download the dataset for the period 2009 - 2018 and create a dataframe that concatenates Wales (total)employment value only.
1.2. Check for any null value or outlier. If found replace that with mean value.
1.3. Change the name of the industries as bellow
Part 2: Data analysis
For each question provide graph/chart along with your own interpretation (~ 50 words)
2.1. Which industry employed highest and lowest workers over the period?
2.2. Which industry has the highest and lowest overall growth over the period?
2.3. Which years are the best and worst performing year in relation to number of employment. (highest and lowest employment)
Part 3: Visual analysis
Create a dynamic scatter/bubble plot showing the change of workforce number over the period using Plotly express.
Part 4: Correlation
4.1. Taking average employment number for each industry over the period, show and identify the highest and lowest correlated industries.
4.2. Make a year wise correlation for each industry. Does the aforementioned industries are also correlated over the each year? Explain your answer.
Part 5: Clustering (k means & hierarchical)
5.1. 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. Writeyour own interpretation (~100 words).
5.2. Using the same dataset (best & worst performing) create a hierarchical cluster. Compare the cluster with k means clusters.
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Attachment:- Assessment Python.rar