Today’s challenge was centered around World Happiness Data and Power BI. The data provided was a good starting point, but I felt it was essential to look for additional data sources to contribute to the analysis. After conducting some research, I discovered fascinating datasets on tertiary education levels per country and the number of individuals living in urban areas. To determine the percentage of the population living in cities, I included the population data in my analysis.
The primary objective of creating this dashboard was to:
- Identify if there is a correlation between urbanization and the happiness index.
- Determine if there is a correlation between the percentage of individuals with tertiary education and the happiness index.
To achieve these goals, I created an Alteryx workflow, which I then loaded into Power BI. After analyzing the data, I established the following metrics:
- Average percentage of the urban population.
- The correlation between urban population and the happiness index.
- Scatter plot depicting the relationship between urban population percentage and the happiness index.
- Dynamics of urban population changes.
- Average percentage of individuals with tertiary education.
- Correlation between tertiary education level and happiness index.
- Scatter plot depicting the relationship between education percentage and happiness index.
In conclusion, it is evident that education and urbanization are significant factors that can predict a country’s happiness index. Countries with a higher percentage of individuals who complete tertiary education tend to have a higher happiness index. Similarly, countries with a higher urban population also exhibit a higher happiness index. The dashboard I created shows the correlation between these factors and the happiness index, which can be useful in making informed decisions that promote well-being.
