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Coding like a Girl

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This is a presentation from #LaraconEU where I was a speaker talking about gender equality.

It contains data about women in the tech workforce.

Published in: Technology

Coding like a Girl

  1. 1. Coding Like a Girl How teams with women gain with the diversity.
  2. 2. About me • Major in Digital Game Development • Senior Software Engineer with 7 years in the market • Web Development • Women in Technology Advocate • Lego fanatic =)
  3. 3. Diversity • Gender • Racial • Ethnic • Sexual Orientation
  4. 4. Bias
  5. 5. Bias • Project Implicit® from Harvard University • Preferences, attitudes and memory
  6. 6. Project Implicit®
  7. 7. Project Implicit® 0% 7.5% 15% 22.5% 30% 1% 3% 6% 18% 18% 28% 26% Strong automatic association of Male with
 Science and Female with Liberal Arts Moderate automatic association of Male with
 Science and Female with Liberal Arts Slight automatic association of Male with 
 Science and Female with Liberal Arts Little to no association between
 genders and academic domains Slight automatic association of Male with 
 Liberal Arts and Female with Science Moderate automatic association of Male with 
 Liberal Arts and Female with Science Strong automatic association of Male with 
 Liberal Arts and Female with Science Source: Project Implicit
  8. 8. IAT Scores Male with Science
 Female with Liberal Arts Female with Science
 Male with Liberal Arts Source:ProjectImplicit
  9. 9. Like a Girl
  10. 10. “You know, you are really intelligent, for a girl.” –Undisclosed friend
  11. 11. Like a Girl • You drive like a girl • You punch like a girl • You fight like a girl • You [verb] like a girl
  12. 12. Diversity Reports
  13. 13. All Areas 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 37%30%30%31%30% 62% 70%70%69%70% Male Female Non Disclosed* Source:CompaniesDiversityReports
  14. 14. Tech 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 15%10%17%15%20% 85% 90% 83%85% 80% Male Female Non Disclosed* Source:CompaniesDiversityReports
  15. 15. Non-tech 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 52%50%48%47%35% 47%50%52%53% 65% Male Female Non Disclosed* Source:CompaniesDiversityReports
  16. 16. High Level 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 23%21%21%23%28% 77%79%79%77% 72% Male Female Non Disclosed* Source:CompaniesDiversityReports
  17. 17. All Areas 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 37%30%30%31%30% 62% 70%70%69%70% Male Female Non Disclosed* Tech 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 15%10%17%15%20% 85% 90% 83%85% 80% Male Female Non Disclosed* Non-tech 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 52%50%48%47%35% 47%50%52%53% 65% Male Female Non Disclosed* Senior Level 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter Yahoo 1% 23%21%21%23%28% 77%79%79%77% 72% Male Female Non Disclosed* Source:CompaniesDiversityReports
  18. 18. Portrait of the tech workforce facts • Men 2.7x more chance of leading positions • Women gravitates towards other women • Lack of role models • Women values flexibility more than men Source: Anita Borg Institute, Climbing the technical ladder
  19. 19. Rank Levels Source: Anita Borg Institute, Climbing the technical ladder 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Women Men 20.2% 10.9% 55.2% 56% 24.6% 33.1% Entry Mid High
  20. 20. Why diversity matters?
  21. 21. Diversity Increases Group Performance
  22. 22. Group Performance • Collective Intelligence is increased • Diverse teams are more efficient • Better problem solving • More innovative solutions Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute
  23. 23. Group Performance • Three factors:
 1 - Social sensitivity
 2 - Numbers of speaking members
 3 - Proportion of females on the group Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute
  24. 24. Diversity Powers Innovation
  25. 25. Innovation • Competitive advantage • Diverse groups outstanding performance • Patents with mixed gender cited more often Source: Ernest & Young, Prof. Anita Williams Woolley, Anita Borg Institute
  26. 26. Innovation Source: London Business School, Anita Borg Institute "If people think alike, then no matter how smart they are they most likely will get stuck at the same locally optimal solutions. Innovating, requires thinking differently. That's why diversity powers innovation." –Scott Page, University of Michigan
  27. 27. Neil deGrasse Tyson
  28. 28. Neil Degrasse Tyson Astrophysicist
  29. 29. –Neil deGrasse Tyson, at 2009 New York Conference - Link “Before we start talking about genetic differences, you got to come up with a system that is equal opportunity. Then we can have that conversation.”
  30. 30. Thank you • Twitter: @gabidavila • Web: http://davila.blog.br • Email: gabidavila@gmail.com
  31. 31. References • ANITA BORG INSTITUTE - Climbing the Technical Ladder • ANITA BORG INSTITUTE - The Case for Investing on Women • ANITA BORG INSTITUTE - Women Technologists Count • ANITA WOLLEY - Evidence for a Collective Intelligence Factor in the Performance of Human Groups • CATALYST, Why Diversity Matters? • ERNST & YOUNG - Groundbreakers • ILLUMINATE VENTURES - High Performance Entrepreneurs

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