Data update 4

 Chart

The above line chart illustrates the trends in female out-of-school rates across four income groups: high-income, upper middle-income, lower middle-income, and low-income countries. 

Key Observations from the Line Chart:

Low-Income Countries

In 2000, the female out-of-school rate for low-income countries was approximately 48%. Over time, this rate has decreased significantly, dropping to 24% by 2023. While this is a positive trend, the rate remains far higher than in other income groups, showing that girls in low-income countries still face significant barriers, including poverty, lack of schools, and cultural practices that deprioritize girls’ education.

Lower Middle-Income Countries

For lower middle-income countries, the out-of-school rate was approximately 28% in 2000 and steadily declined to approximately 9% in 2023. These countries showed substantial progress, reflecting potentially improvements in access to education through international aid and local government initiatives or increasing country development and wealth. However, in 2023 this rate was still twice that of upper middle-income countries.

Upper Middle-Income Countries

In 2000, upper middle-income countries had a much lower out-of-school rate than the lower income countries at around 5.5%. By 2023, this rate had fallen further to approximately 4.3%, reflecting close to near universal access to education. These figures demonstrate the effectiveness of stronger economies and better-funded education systems.

High-Income Countries

High-income countries consistently had the lowest female out-of-school rates, starting at 3.8% in 2000 and dropping to just 1.76% in 2023. This reflects near-universal education access. It was not clear why this rate was not even higher, based on the available information, and if further progress was warranted or not to support universal education access.

The data shows clear progress in reducing the number of girls out of school globally. However, there are still significant gaps, especially in low-income countries, where about one in four girls remains out of school. 

This analysis shows how crucial it is to prioritize policies and programs for low- and lower middle-income countries. These regions face unique challenges, such as poverty, cultural expectations, and lack of resources, which must be addressed to ensure every girl has the opportunity to go to school

Key Observations from the line graph:

 I thought it would be an interesting approach to explore how female out-of-school rates varied across different regions and to see if any patterns emerged globally. Although the dataset included regions, it did not provide year-by-year data on the percentage of girls out of school for those regions. To address this, I selected specific countries from different regions to act as proxies, aiming to provide a global perspective by geography.

The dataset also had several gaps, including missing data for certain years in the selected countries. For these missing entries, I interpolated values by using the most recent available data for each country, ensuring a more complete dataset. Upon closer examination, while the aggregated data for income-level categories appeared to be generally reasonable, there were noticeable inconsistencies. Even countries like the United States, which one would expect to have reliable annual data, had gaps in the dataset. This highlights some limitations in the data’s completeness and consistency, even for well-documented countries. Despite these challenges, I believe this analysis provides valuable insights into regional trends and global disparities in female out-of-school rates.

Link to my dataset

Here

Links for sources

Click here for links:

Here

Here

Here

Here

Findings

As I analyzed the data on female out-of-school rates from 2000 to 2023, I was struck by how it reflects the challenges millions of girls face worldwide in accessing education. Girls’ education is one of the most powerful tools for breaking cycles of poverty, promoting equality, and improving communities, yet so many girls are still being left behind. To give more depth to my findings, I looked into research from leading organizations, and here’s what I’ve learned:

 The Importance of Girls’ Education

The World Bank’s research says when girls are educated, their lives and communities improve dramatically. Educated girls are more likely to marry later, earn better incomes, and raise healthier families. UNICEF highlights that education isn’t just a benefit; it’s a fundamental right and a crucial step toward gender equality. It’s clear that when girls stay in school, they avoid many risks like early marriage and violence while gaining opportunities to thrive.

Barriers to Education

Through my research, I found that the barriers keeping girls out of school are significant. Plan International Canada and UNICEF highlight issues like long distances to schools, the lack of female teachers, and cultural norms that often prioritize boys’ education. Girls are also disproportionately affected by child marriage, gender-based violence, and inadequate sanitary facilities in schools. These barriers are especially severe in low-income countries and conflict zones, where education systems struggle the most.

The Cost of Not Educating Girls

I also came across a powerful report from the Global Partnership for Education (GPE), which revealed just how costly it is when girls are denied education. Countries lose trillions of dollars in potential earnings, and communities miss out on the benefits of healthier, more educated women. The report shows that when girls are educated, they strengthen economies, improve health outcomes, and build more resilient societies. The cost of not investing in their education is simply too high.

Over time, I’ve noticed clear patterns in the data. While there has been progress in reducing the number of girls out of school in some regions, large gaps still remain. Girls living in poverty or areas affected by conflict are often the most impacted. This matches what I’ve learned from organizations like the World Bank, UNICEF, and GPE. What stood out most to me during this project is that every number in the data represents a real girl with untapped potential. Giving girls access to education is one of the most effective ways to build a fairer, better world, and I hope this work inspires others to take action to make that a reality.

Data update 3

 Chart

I've included a description at the bottom of my chart.

Question

Why are the number of girls in the lower-income families so much higher?

link & Brief summary

Click here for the link

UNICEF’s page explains why millions of girls worldwide are unable to attend school. Around 129 million girls miss out on education due to a range of challenges, including poverty, cultural pressures, conflict, early marriage, and lack of essential resources like menstrual products and safe bathrooms. These barriers make it especially hard for girls to stay in school, as many face pressure to work at home or are held back by financial limitations.

For girls from lower-income families, missing school often comes down to cost: they may need to work to support their family, lack basic supplies, or don’t have access to private sanitation facilities, which is crucial, especially during their menstrual cycle. UNICEF is working to change this by providing resources, improving school facilities, and advocating for every girl’s right to an education.

New Data Slice

I made a new data slice to show the percentage of girls missing school. That is also what I used to create the chart.

Click here for the data slice 

Link to Original Dataset

I have chosen a new but similar dataset that shows the percentage of girls missing school.

Click here for the link

Data update 2 - female primary dropout rate

 Lead

 Over the past 20 years, there has been significant progress in reducing the number of girls missing school. However, girls from lower-income backgrounds, still make up the largest portion of those who remain out of school worldwide.

Excel workbook link and explanation 

My excel workbook can be found here 

The dataset shows the female primary school dropout rate worldwide. It provides insight into how dropout rates for girls in primary education have changed over time. 

The RAW sheet contains country level data on the number of female children out of primary school. Data is organized by country, with values from 1990 to 2023 for each. Some entries have missing data.

The Slice sheet aggregates data by income group (e.g, high income, low income) and a total global count. This dataset shows yearly values from 2000 to 2023, giving a more general overview of trends in school dropout rates by income level.

There were several ways to analyze this data set on girls missing school in each country, but I found it most interesting to focus on the differences between high income and low income families. 

Here are my findings:

Lower middle income families had A decrease in the percentage of girls out of school, indicating improved school attendance overtime.

Upper middle income families experienced a slight increase in the percentage of girls missing school.

Low income families had a rising percentage of girls out of school, showing that more girls in these families missed education.

High income families showed a minor decrease suggesting consistent attendance.

Im assuming COVID-19 had an impact among the lower income families because the percentage of girls missing school spiked in 2020. Meanwhile, lower middle income families displayed the opposite trend.

When adding the four income categories, the global total of girls out of school drop from 65.1 million in 2000 to 33.9 million in 2023, highlighting significant overall progress.

Original dataset link

  here

Data update 1

  1. What dataset will you use for your final report? (Title of your dataset, include a link to it) 

2. Describe the dataset. What kind of data does it contain?

3. Is there anything about your data that you don’t understand? (I.e. what a column heading means) how will you find this out?

4. What are some questions you hope to answer with your data? List at least three. (You don’t need the answers at this point)

1. Animal Control Inventory (Lost and Found)  here

2. This dataset contains information about lost and found animals, including their breed, colour, date of entry, name, sex, and current status (e.g., lost). Each entry seems to represent an individual animal.

3. One column that might need further clarification is the "Sex" column, which includes entries like "F/S" and "M/N." These abbreviations likely refer to whether the animal is spayed/neutered. To confirm this, I would refer to any accompanying documentation for the dataset or consult with someone familiar with animal control records.

4.

   - What are the most common breeds of animals reported as lost?

   - Are there specific times of the year when more animals go missing?

   - What are the most common colours or patterns of animals that go missing or are found?

Global tracking on electric vehicles

 

The data visualizations on the Our World in Data page, “Tracking global data on electric vehicles” for electric car sales are generally well done, providing a clear and interactive look at global trends. They use simple line graphs to show how sales have grown over time, making it easy to understand the overall pattern. The world map gives a useful visual context, showing which regions are leading in electric car adoption and which are behind. You can hover over different countries on the map to see specific numbers, which adds an engaging and personalized way to explore the data. The colours used in the charts help differentiate between countries without being overwhelming, making it easier to compare trends. Alongside these visuals, there are notes and explanations that help explain what's happening in the data, like how policy changes have impacted sales.

However, this simplicity also has some drawbacks. The line charts and maps give a broad view but don't go into deeper details, such as how economic factors or technology developments have influenced sales in different areas. Including other types of charts, like scatter plots, could provide more insights into these relationships. The map shows the big picture well, but it would be helpful if users could zoom in on specific regions or countries for more detailed information. By relying mainly on line graphs and a single map, the page misses the chance to show other important comparisons, like how electric car sales stack up against total car sales in each country. Also, with so much data and interactivity available, some people might find it overwhelming, especially if they're not used to analyzing data. Offering simpler guides or breaking down some of the information into smaller sections could make it easier to understand.

Overall, these visualizations do a good job of showing the global state of electric vehicle adoption. They follow many good practices in data visualization, such as being clear, using colour effectively, and providing context. But they could be improved by offering more detail, a wider variety of charts, and making the information more accessible for all viewers.