Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology

<p>In the United States, a significant population is facing an uphill battle trying to thrive in an industry that has seen exponential growth in recent years. Women, who account for approximately 50.8% of the U.S. population are statistically underpaid and underrepresented in science, technology, engineering, and mathematics (STEM). Despite women-led technology teams establishing a 21% greater return on investment than teams who don’t, and young women largely outperforming men in math according to a 2015 study, there are only three fortune 500 companies led by women, and they comprise only 10% of internet entrepreneurs. Research generates hundreds of articles, infographics, and reports showing that women are capable of, yet not thriving in technology occupations. <em>Brent Ozar Unlimited </em>and <em>PayScale </em>have provided data from the past seven years on top tech companies demographics and salaries. I have analyzed this data using various statistical methods and created <em>Envision Equality</em>. Using this platform, I’ve visualized and displayed data from these sources in the form of charts and infographics. The intention of this project is to start a dialogue surrounding the issue of gender discrimination in technology occupations, and begin to address it.</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Master

Discipline thesis:degree_discipline

Data Analysis & Visualization

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2021

Author dc:creator

• Bolewicki, Quinn

Advisor dc:contributor.advisor

• Aucher Serr

• Applied Statistics
• Categorical Data Analysis
• Digital Humanities
• Feminist, Gender, and Sexuality Studies
• Women's Studies
• gender
• pay gap
• statistics
• website
• data
• technology

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/4418

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-5497