Average Wages and Employment Rates for Different Educational Attainments

United States data from 1970s to 2022 which compares educational attainment with factors such as race and gender, exploring the impact on employment rates and wages.

Project Topic Choice

In our project, we chose to explore the correlation between educational attainment and the resulting effect on wages and employment rates. We pulled from data sources which included those factors, and additionally included variables such as race and gender.

Exploration Questions


1. What is the general trend in wages and employment rates from the 1970s to present day?

2. How do different educational attainment levels effect wages and employment rates, and how does has this change overtime?

3. How do factors such as race and gender play into the wages and employment rates at different education levels? Specifically, how is this effect different for individuals with high school degrees when compared to individuals with bachelor's degrees?

4. How did the Covid-19 pandemic impact employment rate and wages? And how is this impact furthered by factors such as race and gender?

Data Sources

We considered and explored data from two different csv files. One included data regarding wages from 1973 to 2022 for different educational attainments, with race and gender included. The second file included data regarding employment rates from 1979 to 2022 for different educational attainments, also including race and gender as variables. In the carousel below, snapshots of our csv data files represented in tabular form are displayed. Both data sources contained around 20 factors and almost 50 years worth of data for each of the columns.

Data Journey and Caveats

Step 1: data acquisition in the form of csv files
Caveats: Had difficulty finding a data source that had content adequate for the scope of the project, but were able to eventually ensure that our data from the two csv files was accurate, wide in scope, and useful for insights.
Step 2: data parsing, cleaning, and creation of DataFrames in Python
Caveats: Encountered difficulty with creating DataFrames from csv data converted to list form. We found that originally, the column names were being marked as the first row of data in our DataFrames rather than the column name, which required further adjustment to correct.
Step 3: creating visualizations from DataFrames
Caveats: We found that our visualizations were limited by our data and variables, especially we had difficulty with creating animated graphs that could accurately display our data in a useable form.
Step 4: website creation using bootstrapr and html code
Caveats: Learning and using html coding language in a short period of time proved difficult and required trial and error. Uploading images to our webpage also yielded problems, and we opted to insert links to the interactive images while displaying a static image on the main screen as a resolution to this problem.

Project Website Demo Video

Learn how to navigate our project website by watching this short video! We will run through the basic data acquistion and composure of our project in Python and html, in addition to showing all of the features built into our website to best display our findings. For best results, set video quality settings to 720p.

About the Project Members

Marcy Barnhorst: I am a sophomore at the University of Notre Dame studying Biological Science on a Pre-Medical track, with a minor in Foundations of Business. I took this introductory python course to further my skills to include basic computer programming proficiency because of its useful application in both the academic and professional realms. In my free time, I enjoy spending time with loved ones and friends, volunteering for the Notre Dame College Mentors for Kids program, playing viola in the Notre Dame Symphony Orchestra, and conducting research on antibiotic resistance in colistin resistant bacteria, Acenitobacter baumannii.

Delaney Cosentino: My name is Delaney Cosentino and I am a sophomore studying finance and sociology. I had taken a coding fundamentals class in my freshman year which was my only prior knowledge of Python. Since then, I have become a TA for the coding fundamentals class and am taking an advanced analytics class for accounting and finance where we use Python and the code for certain scenarios brought by clients. In my free time, I like to hang out with my friends, explore nature, and try new restaurants.