READ.ME

My goal for my final project was to shed light on the lack of diversity in the tech industry. With being a double minority, I felt the need to not only bring attention to this possible bias but to also provide resources for other minorities who are looking to break into a technological career.

I decided to pull the majority of my data from the Bureau of Labor Statistics (BLS) because not only did they have years of occupational data but information on wages, women, Black, Asian, and Hispanic indivdiauls in the field for over nine years.

• Python
• Tableau
• Machine Learning
• Microsoft Excel
• Beautiful Soup

According to the BLS, the tech career with the most growth is Software, Applications and System Software Developers. The occupation makes up has over 12,335 positions in the industry. `

According to that same data over the last nine years, Computer and Information Research Scientists only make up 207 positions.

The average highest weekly earnings across all occupations in 2019 came from men and was about $1,692.

The lowest median weekly earning in 2019 was $1,149; which came from women in the field.

According to both data points above, it's clear that there is over a $500 weekly discrepancy between men and women in the field accross all occupations in the last year.

Aside from the gender and wage gaps, there is also a major racial gap in the field. I decided to use both Tableau and Machine Learning for predicting percentages of black, Asian, and Hispanics in the industry. According to 2019 data: • Asians made up 16% of the tech industry • Black people made up 9% of the industry • Hispanic and/or Latinos made up 8% of the industry

**Please keep in mind that individuals can be more than one race. In addition, I understand that Hispanic and Latinos should have their own stats but they were grouped together in BLS data.

My Tableau predicitions for 2020 indicated that: • Asians will increase in the tech industry by .56% • Black people will decrease by .18% • Hispanics and/or Latinos will decrease by .37%

However, my Machine Learning predicitions for 2020 seem to be inconclusive at the moment.