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Coding like a girl - DjangoCon

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Talk given at DjangoCon US 2015

Many projects have been in place to increase the numbers of female workers in the IT industry. Through diversity reports we can see that the real scenario is not pretty, most IT companies have less than 20% of women technical jobs. So, why it is important to increase these numbers? And why are they so low? In this talk I will show how teams with women gain with the diversity.

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Coding like a girl - DjangoCon

  1. 1. C O D I N G L I K E A G I R L How teams with women gain with diversity
 
 Gabriela D’Ávila @gabidavila
  2. 2. A B O U T M E • Major in Digital Game Development • Senior Software Engineer with 8 years in the market • Web Development • Women in Technology Advocate • Lego =) 2
  3. 3. W H AT TO E X P E C T • Diversity • Bias • Like A girl • Diversity Reports • How diversity is important • Initiatives 3
  4. 4. D I V E R S I T Y 4
  5. 5. D I V E R S I T Y • Gender • Racial • Ethnic • Sexual Orientation 5
  6. 6. B I A S 6
  7. 7. P R O J E C T I M P L I C I T ® 7 http://implicit.harvard.edu/implicit
  8. 8. 8
  9. 9. P R O J E C T I M P L I C I T ® 9 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 2015 2014
  10. 10. L I K E A G I R L 10
  11. 11. 11 "You know, you are pretty intelligent, for a girl. " - Undisclosed friend
  12. 12. L I K E A G I R L • You drive like a girl • You punch like a girl • You fight like a girl • You [verb] like a girl 12
  13. 13. D I V E R S I T Y R E P O R T S 13
  14. 14. A L L A R E A S 14 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 34%30%32%31% 66%70%68%69% Male Female Source:CompaniesDiversityReports 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 30%30%31%30% 70%70%69%70% Male Female 2015 2014
  15. 15. T E C H 15 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 13%18%16%21% 87%82%84%79% Male Female Source:CompaniesDiversityReports 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 10%17%15%20% 90% 83%85%80% Male Female 2015 2014
  16. 16. N O N - T E C H 16 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 50%47%52%37% 50%53%48% 63% Male Female Source:CompaniesDiversityReports 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 50%48%47%35% 50%52%53% 65% Male Female 2015 2014
  17. 17. H I G H L E V E L 17 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 22%22%23%28% 78%78%77%72% Male Female Source:CompaniesDiversityReports 0% 20% 40% 60% 80% 100% Apple Facebook Google Twitter 21%21%23%28% 79%79%77%72% Male Female 2015 2014
  18. 18. T E C H W O R K F O R C E F A C T S • Men 2.7x more chance of leading positions • Women gravitates towards other women • Lack of role models • Women values flexibility more than men 18 Source: Anita Borg Institute, Climbing the technical ladder
  19. 19. R A N K L E V E L S 19 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. W H Y D I V E R S I T Y M AT T E R S ? 20
  21. 21. D I V E R S I T Y I N C R E A S E S G R O U P P E R F O R M A N C E 21
  22. 22. G R O U P P E R F O R M A N C E • Collective Intelligence is increased • Diverse teams are more efficient • Better problem solving • More innovative solutions 22 Source:Ernest&Young,Prof.AnitaWilliamsWoolley,AnitaBorgInstitute
  23. 23. G R O U P P E R F O R M A N C E • Three factors:
 Proportion of females on the group
 Social sensitivity
 Numbers of speaking members 23 Source:Ernest&Young,Prof.AnitaWilliamsWoolley,AnitaBorgInstitute
  24. 24. D I V E R S I T Y P O W E R S I N N O VAT I O N 24
  25. 25. I N N O VAT I O N • Competitive advantage • Diverse groups outstanding performance • Patents with mixed gender cited more often 25 Source:Ernest&Young,Prof.AnitaWilliamsWoolley,AnitaBorgInstitute
  26. 26. 26 I N N O VAT I O N 26Source: 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. I N N O VAT I O N 27Source: London Business School, Anita Borg Institute
  28. 28. N E I L D E G R A S S E T Y S O N 28
  29. 29. N E I L D E G R A S S E T Y S O N Astrophysicist 29
  30. 30. 30 “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.” – Neil deGrasse Tyson, at 2009 NewYork Conference - Link
  31. 31. H O W C A N I H E L P ? 31
  32. 32. T H E C O D E M A N I F E S TO • Discrimination limits us • Boundaries honor us • We are our biggest assets • We are resources for the future • Respect defines us • Reactions require grace • Opinions are just that: opinions • To err is human 32 http://www.codemanifesto.com
  33. 33. 33
  34. 34. P R O G R A M S 34
  35. 35. Q U E S T I O N S ? 35
  36. 36. T H A N K YO U • Twitter: @gabidavila • Web: http://gabriela.io • Email: gabidavila@gmail.com 36
  37. 37. R E F E R E N C E S • 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 • CODE MANIFESTO - Website 37

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