Data update 2 - female primary school 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)  https://opendata.vancouver.ca/explore/dataset/animal-control-inventory-lost-and-found/export/?disjunctive.breed&disjunctive.color&sort=date

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?

Tracking global data 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.