Exploring the World Through Data
Did you know europe without russia is smaller than australia
How does wealth relate to the quality of life across different countries? Is a country’s economic prosperity reflected in its literacy rate and life expectancy?
To answer these questions, I conducted a data analysis project examining population, literacy rate, life expectancy, GDP per capita, land area, and Human Development Index (HDI) across countries around the world.
The goal of this project was to use real-world data to uncover patterns and relationships between economic and social indicators. I collected data from several online sources, cleaned and combined the datasets, analyzed the relationships using SQL, and built an interactive dashboard in Power BI.
Business Questions
The analysis focused on five main questions:
- What is the current global literacy level?
- What is the world’s average life expectancy?
- What is the current world population?
- How does GDP per capita relate to literacy rate and life expectancy?
- How are population and land area distributed across continents, and does geographic or population size relate to economic and social outcomes?
Dataset
I collected and combined data from several sources:
- Population by Country
- Literacy Rate by Country
- HDI by Country
- GDP by Country
- Countries by Continent
- Largest Countries in the World
The final dataset brought together information such as country, continent, population, total area, land area, GDP, GDP per capita, literacy rate, life expectancy, and HDI.
Data Collection and Tools
To collect data, I used BeautifulSoup, a Python library for web scraping
The main tools used throughout the project were:
- Excel for data cleaning
- SQL (PostgreSQL) for data transformation and analysis
- Power BI for data visualization and dashboard development
Code & Resources
You can view the complete project, including the Python web-scraping notebook, cleaned dataset, SQL analysis, and Power BI dashboard, in GitHub .
Data Cleaning
Before analyzing the data, I cleaned and standardized the different datasets because they came from multiple sources and were not initially in a consistent format.
1. Standardizing the data
I standardized the values and data types across the columns.For example:
- Country and continent names were trimmed and standardized.
- Country and continent columns were converted to text.
- Numerical columns such as population, land area, GDP, GDP per capita, life expectancy, HDI, and literacy rate are converted to appropriate numerical data types.
- I corrected the continent classification for Russia, changing it from
AsiaEuropeto Asia for consistency in the analysis.
2. Handling missing values
I identified empty values and values represented as N/A empty strings and converted them to NULL values.
3. Combining datasets
The data was originally spread across multiple tables. I created a consolidated table containing only the columns needed for the analysis.
This made it easier to perform SQL queries and build the Power BI dashboard.
4. Removing duplicates
I identified duplicate records and removed them to ensure that countries were not counted more than once in calculations such as population totals and averages.
Data Analysis
After cleaning and combining the data, I used SQL (PostgreSQL) to analyze the relationships and patterns within the dataset.
Get GakiiViolet’s stories in your inbox
Join Medium for free to get updates from this writer.
I used SQL to:
- Calculate total population and area by continent.
- Calculate average population, literacy rate, GDP per capita, and life expectancy by continent.
- Rank countries based on literacy rate.
- Compare GDP per capita, literacy rate, and life expectancy across continents.
- Examine relationships between variables such as area, population, GDP, GDP per capita, life expectancy, literacy rate, and HDI.
- Use correlation analysis to measure the strength and direction of relationships between numerical variables.
These analyses helped identify patterns in the data and provided the numerical results used to support the visualizations and findings presented in the Power BI dashboard.
Data Visualization
I used Power BI to transform the analysis into an interactive dashboard.
The dashboard presents:
- Total world population
- World literacy rate
- Average world life expectancy
- Area by continent
- Population by continent
- Life expectancy vs. GDP per capita
- Literacy rate vs. GDP per capita
Log Scale Details
GDP per capita varies greatly between countries, with some countries having much higher values than others. To make the differences easier to visualize, I used a logarithmic scale for GDP per capita on the scatter plots in Power BI.
The log scale helps show the distribution of countries across different income levels more clearly and makes it easier to observe the relationship between GDP per capita and indicators such as life expectancy and literacy rate.
The scatter plots show a general positive relationship: countries with higher GDP per capita tend to have higher life expectancy and literacy rates. However, the relationship is not perfectly linear, and the improvement in social outcomes tends to become less pronounced at higher income levels.
Key Findings
1. Larger continents generally have larger populations
The analysis shows that continents with larger total areas generally tend to have larger populations.
However, Europe is an interesting outlier. Despite having the smallest total area among the major continents considered in this comparison, Europe has a larger population than South America and Australia, which have considerably larger areas.
This demonstrates that land size alone does not determine how many people live in a region.
2. GDP per capita is positively related to life expectancy and literacy
One of the clearest patterns in the analysis is the positive relationship between GDP per capita, life expectancy, and literacy rate.
The scatter plots suggests that countries with higher GDP per capita generally have higher life expectancy and literacy rate. This pattern is also visible when comparing continents. Europe has the highest average GDP per capita at approximately $62,523.52, the highest average life expectancy at 81.01 years, and the highest literacy rate at approximately 99%.
In contrast, Africa has the lowest average GDP per capita at approximately $3,220.34, a lower average life expectancy of 66.21 years, and the lowest average literacy rate at approximately 68%.
These findings suggest that higher economic prosperity is generally associated with better social outcomes, including longer life expectancy and higher literacy rates.
3. Regional Scale vs. Human Development Performance
A comparison of geographic, demographic, and socioeconomic data reveals an important pattern: having a larger land area or population does not necessarily translate into higher economic or social outcomes.
Demographic and Geographic Scale: Africa has the second-largest land area and population among the continents, with approximately 28.98 million km² of land and 1.54 billion people. In comparison, Europe has a much smaller land area of approximately 5.74 million km² and a population of about 587 million people.
Socioeconomic Outcomes: Despite its smaller geographic and population size, Europe records the highest average values across the three socioeconomic indicators examined: GDP per capita of $62,523.52, life expectancy of 81.01 years, and literacy rate of approximately 99%. Africa records the lowest average values, with a GDP per capita of $3,220.34, life expectancy of 66.21 years, and literacy rate of approximately 68%.
4. Global literacy rate is 86.79%
The calculated global literacy level is approximately 86.79%.
This means that, based on the dataset, the large majority of the world’s population is represented by countries with relatively high literacy levels, although significant differences exist between individual countries.
5. Average life expectancy is 74.47 years
The average life expectancy across the countries in the dataset is approximately 74.47 years.
However, life expectancy varies considerably between countries, with higher values generally appearing among countries with higher GDP per capita.
Conclusion
This project helped me explore how economic, demographic, geographic, and social indicators are connected across countries.
The analysis revealed several important patterns. One of the strongest patterns identified was the positive relationship between GDP per capita and both life expectancy and literacy rate. Countries with higher GDP per capita generally tend to have better social outcomes, although the relationship is not perfectly linear.
I hope you enjoyed exploring the world through data as much as I enjoyed analyzing it.
Happy learning and happy analyzing! 😄📊🌍
