The Gapminder Foundation has collected a lot of data about how people live their lives in different countries, tracked over the years, on a number of different indicators. In this project, I'll take a look at four indicators I'm interested in understanding better and perform the Data Analysis Process (DAP) on them to uncover insights.
We'll be looking at each indicator across four decades by extracting data from 1980, 1990, 2000, and 2010/2011.
Indicator 1: Democracy Score (-10 is low, 10 is high)
This score is based on Polity IV, which is calculated by subtracting an autocracy score from a democracy score. It's a summary measure of a country's democratic and free nature. Anarchy, periods when normal government is suspended are coded as 0. There as been some interpolation for instances of foreign interruption, dissolved or merged countries, etc.
Indicator 2: GDP per Capita
This is in constant 2000 USD. The inflation but not the difference in cost of living between countries has been taken into account.
Indicator 3: Human Development Index - HDI (0 is low, 1 is high)
This is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. This is calculated by taking the geometric mean of normalized indices for each of the three dimensions (life expectancy index, Education index, GNI index).
Indicator 4: Urban Population (as a percentage of total population)
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Does there appear to be a relationship between more Democratic countries and GDP, HDI, or Urban Population? That said, do we need to first see if countries are becoming more democratic over time?
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Does there appear to be a relationship between urban population and HDI trends?
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Does there appear to be a relationship between GDP and HDI trends?
Create a Python environment in Jupyter Notebook tooled up with the following packages:
- pandas
- numpy
- matplotlib.pyplot
- seaborn
- don't forget to add % matplotlib inline to see your graphs seamlessly
Upload the datasets for the four indicators.
- Clean and wrangle data
- Conduct Exploratory Data Analysis (EDA) to both validate and verify that all wrangling and cleansing is done, as well as understand the entirety of the data a bit better.
- This will best prepare you to effectively pursue any sort of discovery that would bring light to the stated questions.
- Dive into seeking solutions to the stated questions, visualize and describe what you see.
- Make conclusions about what you've discovered, state the findings, and clarify any limitations in the data.
- Make recommendations based on the findings as well as any further analysis to conduct.
Find any typos? Have another resource you think should be included? Contributions are welcome.
First, fork this repository.
Next, clone this repository to your desktop to make changes.
$ git clone {https://github.com/ccaddel/investigate_gapminder_data.git}
$ cd investigate_gapminder_data
Once you've pushed changes to your local repository, you can issue a pull request by clicking on the green pull request icon.
Instead of cloning the repository to your desktop, you can also go to README.md in your fork on github.com, hit the Edit button (button with the pencil) to edit the file in your browser, then hit the Propose file change button, and finally make a pull request.