Why Are Less Women in Computer Science - Data Science Project
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The document discusses the declining presence of women in computer science despite their strong performance in science tests globally. It highlights the disparity in undergraduate degrees awarded to women, particularly in the U.S., and outlines the data analysis phases and research questions explored throughout the project. The author emphasizes the importance of thorough data exploration and procedural problem-solving in addressing the gender gap in STEM fields.
Why Are Less Women in Computer Science - Data Science Project
- 1. WHY ARE LESS WOMEN IN COMPUTER SCIENCE? GA - Data Science Final Project By, Sana
- 2. WHY THIS TOPIC?? Girls outperformed boys in more countries in a science test given to 15-year- old students in 65 countries — but in the United States, boys led the girls. What is most startling about that is it does not represent progress. In 1985, women earned 37% of computer-science undergraduate degrees but the number slowly started to decrease. Three decades later, STEM careers has become a much more vital gateway to high-paying jobs and chance to influence the software-driven future of society. Yet mostly more men than women are stepping through it.
- 3. LITTLE STATS: Women earn just 18% of undergraduate degrees awarded for computer science. 30,000 students took the Advanced Placement Computer science exam in high school last year - 2014. Less than 6,000 of them were women.
- 4. ASK RESEARCH QUESTIONS This is what made me think about it
- 5. There were three main data files: all-ages.csv Recent Grad.csv (<28yrs) - Contains detailed breakdown by gender and type of job grad-students.csv (ages 25 +) - Contains basic earnings, labor force information, unemployment rate and details on graduate attendees. women-stem.csv - Contains total of Women enrolled in all different majors. Extracted from Census data (http://www.census.gov/programs- surveys/acs/technical-documentation/pums.html)
- 8. PHASES Phase 1: Data Collection- Exploring the data, cleaning and analyzing what could be done with the data Phase 2: Modeling Plan Linear Regression Phase 3: Building my Test Cases. Why are there less Women who sign up for Engineering major? Is there a correlation between unemployment rate and Women count?
- 10. VISUALIZING LINEAR RELATIONSHIP Using Seaborn
- 12. LESSONS LEARNT Take a lot of time analyzing and understanding the data before jumping into code. Think about the goal and problem you are trying to solve. Explore the data well. Remember to keep it within scope - Keep the focus on the problem narrow with two or three questions at most.
- 13. BE PROCESS ORIENTED Implement a procedural approach to any problem….
- 14. WHATS NEXT?? Experimenting with other different modeling techniques and finding which one is better. Building a dashboard using Flask and predicting how many women will sign up for “Engineering” next year. Taking more time to think about the problem before starting with the code. Remember it never works the first time, might not work the second or the third - but just don't give up!
- 15. THANK YOU 😊 By, Sana Nasar