Dahel Consultant and Techies Internship: This is a project that entails the analysis of the World Happiness. (The picture below is gotten from Medium Website).
Disclaimer: This is not a real company as we know this is a dataset compiled by Dahel Consultant Techies for Internship purposes.
This is a world happiness analysis. It is done by analyzing data from the happiness table which comprises of RANK, Country, Happiness score, Whisker-high, Whisker-low, Dystopia (1.83) + residual, GDP per capita, Social support, Healthy life expectancy, Freedom to make life choices, Generosity, Perceptions of corruption fields. I used Excel to get an overview of the dataset before importing it into Tableau where the actual analysis was carried out.
The goal of this analysis is to know:
- The highest happiness score within all countries
- The highest overall GDP per capita within all countries
- The country with the highest average healthy life expectancy to live.
- The country highest average social support
- The continent highest average perception of corruption.
- The most populated continent
- The trend between the freedom and social support of all countries
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Tableau concepts like:
- Data Connection:Connecting to various data sources such as Excel files, CSV files
- Data Preparation: Cleaning and transforming data using Tableau, Handling missing values, data type conversions, and other data cleaning tasks. Creating calculated fields to derive new insights or perform calculations on the data.
- Data Visualization: Building visualizations such as bar charts, bubble chart, scatter plots, heat maps, treemaps, histograms, and more to represent data. Using dual-axis charts, and stacked charts to compare multiple measures or dimensions effectively. Applying filters, parameters, and sets to interactively explore and drill down into data subsets.
- Dashboard Creation: Designing interactive dashboards by combining multiple visualizations, filters, and other elements onto a single canvas. Adding interactivity to dashboards using actions, filters, and highlight actions to facilitate exploration and analysis. Optimizing dashboard layout and design for clarity and usability.
- Advanced Analytics: Performing statistical analysis within Tableau using built-in functions and capabilities (e.g., trend lines, reference lines, forecasting). Conducting spatial analysis by visualizing geographic data and using spatial calculations. Utilizing clustering, regression analysis, and other advanced analytics techniques for deeper insights.
- Mapping: Creating maps to visualize geographic data using built-in geographic roles or custom latitude-longitude data. Customizing maps with various map layers, backgrounds, and map styles. Using spatial analysis tools to perform geographic analysis and create spatial visualizations.
- Calculations and Expressions: Writing calculated fields and calculated measures using Tableau's formula language to perform complex calculations. Using table calculations to compute values based on the data displayed in a visualization. Implementing level of detail (LOD) expressions to control the granularity of calculations.
- Data Exploration and Interactivity: Enabling interactivity in visualizations by allowing users to filter, highlight, and drill down into data dynamically. Utilizing tooltips, parameter actions, and dashboard actions to enhance user experience and provide context-sensitive information. Implementing quick table calculations, sorting, and ranking to explore data trends and patterns.
The dataset for the work is gotten from Dahel Consultant Techies. It consists of 147 records and 12 fields of data. I studied the dataset well to gain proper insight into the dataset. You can find a link to download the dataset here:
Step 1:
- The first row is represented as column headers (field names).
Step 2:
- Some certain data types were changed to values in the data source.
Step 3:
- Data derived from an Excel merged cell is changed to value in the data source.
Several expressions and functions were made to arrive at the desired KPI or Metrics. I arrived at a report with a single dashboard consisting of different visuals such as bar chart, doughnut chart, stacked bouble chart, and KPIs, giving the summary of the insights gained into the world happiness status.
The dashboard conveys information about the following key areas:
- highest happiness score within all countries
- highest overall GDP per capita within all countries
- country with the highest average healthy life expectancy to live.
- country highest average social support
- continent highest average perception of corruption.
- most populated continent
- trend between the freedom and social support of all countries
Summary of the insights gained into the chess Game performance:
The total number of countries for this analysis is 147. So many factors were considered to determine the global happiness of these countries and the findings are:
• Finland had the highest happiness score compared to other countries, followed by Denmark, then Iceland.
• LUXEMBOURG has the highest overall GDP per capita among other countries.
• Hong Kong S.A.R of China has the highest Avg. healthy Life expectancy to live.
• Switzerland has the highest average social support and Singapore has the highest average Perception of corruption.
• There is a trend between the Freedom and Social Support of all countries used in this analysis.
▪ Strengthen initiatives to foster social support, aiming to improve overall well-being and happiness scores among nations.
▪ Prioritize measures to address income inequality globally, ensuring equitable distribution to enhance happiness and life satisfaction.
▪ Focus on public health interventions to increase life expectancy and promote healthier living, contributing to elevated happiness levels.
▪ Implement robust anti-corruption measures globally, learning from the success of Singapore, to cultivate trust and positively impact happiness scores.
▪ Encourage policies that simultaneously foster freedom and social support, recognizing the observed trend and its potential impact on national happiness.
I am open for entry-level to mid-level data analyst role.