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Data Visualisation Coursework Assignment Sample

Report

You are asked to carry out an analysis of a dataset and to present your findings in the form of a maximum of two (2) visualisations, (or a single (1) dashboard comprising a set of linked sub-visualisations) along with an evaluation of your work.

You should find one or more freely available dataset(s) on any topic, (with a small number of restrictions, see below) from a reliable source. You should analyse this data to determine what the data tells you about its particular topic and should visualise this data in a way that allows your chosen audience to understand the data and what the data shows. You should create a maximum of two (2) visualisations of this data that efficiently and effectively convey the key message from your chosen data.

It should be clear from these visualisations what the message from your data is. You can use any language or tool you like to carry out both the analysis and the visualisation, with a few conditions/restrictions, as detailed below. All code used must be submitted as part of the coursework, along with the data required, and you must include enough instructions/information to be able to run the code and reproduce the analysis/visualisations.

Dataset Selection

You are free to choose data on any topic you like, with the following exceptions. You cannot use data connected to the following topics:

1. COVID-19. I’ve seen too many dashboards of COVID-19 data that just replicate the work of either John Hopkins or the FT, and I’m tired of seeing bar chart races of COVID deaths, which are incredibly distasteful. Let’s not make entertainment out of a pandemic.

2. World Happiness Index. Unless you are absolutely sure that you’ve found something REALLY INTERESTING that correlates with the world happiness index, I don’t want to see another scatterplot comparing GDP with happiness. It’s been done too many times.

3. Stock Market data. It’s too dull. Treemaps of the FTSE100/Nasdaq/whatever index you like are going to be generally next to useless, candle charts are only useful if you’re a stock trader, and I don’t get a thrill from seeing the billions of dollars hoarded by corporations.

4. Anything NFT/Crypto related. It’s a garbage pyramid scheme that is destroying the planet and will likely end up hurting a bunch of people who didn’t know any better.

Solution

The data used for this reflective study is from the World Development Indicators. In this, the dataset consists of information regarding the trade business, income factors for different regions and countries and income groups as well. So, a dashboard is created for assignment help with the help of Tableau Software using the two datasets, named as counry and country codes. The form of presentation used is a bar graph (Hoekstra and Mekonnen, 2012).

1. Trade data, Industrial data and Water withdrawal data vs regions.


Figure 1: Region vs Trade data, Industrial data and Water withdrawal data.

The first visualization created is about the Trading data, Industrial data and Water Withdrawl data. All three data are presented together with a comaprison in different regions to get an overview of all the regions and their holding place in the following trading sectors. For the Tading data in several regions, it is clear that the leading area is europe and central asia, with the maximum occupancy of 98,600, while the count is nearly equal to the water withdrawl count with a differnce of 311 only. But in this region, the industrial count is only 82,408, yet the highest in all data taken.

The next leading region is Sub Suharan Africa, which is only for the Tade data and Water Withdrawl data. While the leading region for industrial data is Middle East and North Africa.

Overall, these findings suggest that Europe and Central Asia offer the most significant opportunities for businesses and organizations in terms of Trading and Industrial sectors. Meanwhile, Sub-Saharan Africa and Latin America and Caribbean offer promising opportunities in the Trading sector, and the Middle East and North Africa have potential in the Industrial sector.

These findings also highlight the need for policymakers to focus on improving access to resources and infrastructure in regions where the count of these data is lower, to boost economic growth and development. The visualization depends on several factors, such as the choice of visual encoding, the clarity of the labels and titles, and the overall design of the visualization. Therefore, it can be considered as a successful visualization.

Moreover, the visualization provides a comprehensive overview of the data, allowing viewers to understand the relationships and patterns between the different sectors and regions. The correlation of Exchanging, Modern, and Water Withdrawal information in various locales additionally permits watchers to rapidly recognize what districts are driving in every area and which ones have potential for development.

The analysis provided in the visualization also adds value by highlighting the implications of the data, such as the need for policymakers to focus on improving access to resources and infrastructure in regions where the count of these data is lower to boost economic growth and development. This contextual information helps viewers to understand the underlying causes and implications of the data, providing a more complete picture of the situation (Batt et al., 2020).

Furthermore, the analysis provides insights into the regions that offer the most significant opportunities for businesses and organizations in terms of trading and industrial sectors, and the regions that have potential for growth and development. This information can be valuable for policymakers and stakeholders looking to invest in or improve infrastructure and resources in these regions.

The visualizations are well-designed, using different colours to represent a group, with proper labels and tags on it to make it easily understandable for the viewers, so it is a success on the completion of the visualizations. Although, it is important that additional analysis and contextual information may also be required to understand the underlying causes and implications of the data.

2. Source of income and expenditure cost for different income groups and regions.

Figure 2: Count of source of income and expenditure cost for different income groups and regions.

This visualization is about the income group in different regions and the comparison of count of source of income and the expenditure in total. This provides a well information of the data for all class of groups regarding income and their data.

One key observation is that the lower middle-income group seems to have more balanced results compared to other income groups. However, there are still significant difficulties faced by people in South Asia, where the count of income sources is low for all income groups.

Another important observation is that Sub Saharan Africa appears to have the highest count for the source of income overall, while Latin America and the Caribbean have the highest count for the upper middle-income group. On the other hand, the Middle East & Africa and North America have the lowest count of income sources among the high-income group, which indicates that there is a significant disparity in income sources and expenditures across different regions. It is important to create more opportunities for income generation and improve access to education, training, and resources to enable people to improve their income and standard of living (Lambers and Orejas, 2014).

The visualization effectively communicates the findings about the disparities in income sources and expenditures across different regions and income groups. It highlights the areas where there are significant difficulties faced by people, such as in South Asia where the count of income sources is low for all income groups. The perception likewise gives significant experiences into the areas where there are open doors for money age, like in Sub Saharan Africa and Latin America and the
Caribbean.

Generally, this perception is an outcome in imparting complex data about pay gatherings and their kinds of revenue and consumptions in an unmistakable and reasonable manner. It successfully features the incongruities between various locales and pay gatherings and the requirement for approaches and projects to further develop admittance to schooling, preparing, and assets to empower individuals to work on their pay and way of life.

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