Data Visualization – Data Ramble

Analyzing CIA Factbook Data Using SQLite and Python

Introduction

In this project, I’ll be working with data from the CIA World Factbook, a collection of statistics about all of the countries on Earth. The factbook contains demographic information like:

population – The population as of 2015.
population growth – The annual population growth rate, as a percentage.
area – The total land and water area.

I’ll be using pandas to run SQL queries and display the results neatly as a DataFrame object.

Visualizing The Gender Gap In College Degrees

In this project, I continue exploring the data set used in my post on employment rates of college graduates. Here, I’m looking at how gender gaps in college degrees have changed specifically during the time between 1968 – 2011. At the end of the post are grouped line charts comparing how the gap across all degree categories between genders have changed over time. Continue reading “Visualizing The Gender Gap In College Degrees”

Visualizing Earnings Based on College Majors

This project is a demonstration on using the plotting tools built into pandas to explore data on job outcomes for recent college graduates. Using visualizations, we can start to explore questions from the data set like:

Do students in more popular majors make more money?
Using scatter plots
How many majors are predominantly male? Predominantly female?
Using histograms
Which category of majors have the most students?
Using bar plots

The data released by American Community Survey and cleaned by FiveThirtyEight can be found here.