aaronremski / investigatedata

Udacity Project 1 - Investigate economic, inequality, & corruption data. This project sets out to find a relationship between the fastest-growing countries, corruption, & inequality.

Investigate Economic Data

Udacity Project 1

Official Project Notebooks: Investigate_a_Dataset_pg.1.ipynb & Investigate_a_Dataset_pg.2.ipynb
Additional Notebooks Used: investigate_data_energy.ipynb, investigate_GDP.ipynb, investigate_data_political.ipynb, investigate_data_poverty.ipynb

Language: Python 3

Packages Used: pandas, numpy, matplotlib, seaborn

Data type definitions:

1. GDP per capita is gross domestic product divided by mid-year population. GDP is the sum of gross value added by all resident producers in the economy.
2. Gini shows income inequality in a society. A higher number means more inequality.
3. CPI (Corruption Perception Index) is transparency international's score of perceptions of corruption. Higher values indicate LESS corruption

Questions:

1. How does the world's countries GDP/capita compare with one another?

After calculating the Compound Annual Growth Rate (CAGR) for all country's GDP/capita from 2000-2018, determine characteristics such as:

2. Which regions, as determined by the World Bank, contain the top 10 fastest growing countries?
3. Which income groups are represented, as determined by the World Bank, by the top 10 fastest growing countries (highest CAGR values)?
4. What did the GDP/capita trend look like for the top 10 countries?
5. How did perceived corruption change, if any, during expansive growth?*
6. How did the Gini coefficient change, if any, during expansive growth?*

*See Investigate_a_Dataset_pg.2.ipynb for questions/answers to questions 5 & 6

Table of Contents

• Data Wrangling
• Exploratory Data Analysis
  • Questions/Analysis 1-6
• Conclusions

Data Source/Location

Source: Data obtained from GapMinder.org.
Location: Data files reside in directories, data_income & data_political