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This week was Global Women in Data Science was held.  I started by watching the New Zealand event.  Please note this is cross posted here.  I am posting this today as it is International Women’s Day 2018.  Congratulations to Moya Watson who is a 2018 YWCA Woman Honoree

You can watch this replay here:

My notes are only for the first two speakers.

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Source: WiDS

100+ events worldwide in 2018

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Above shows community spanning dots around the world

 

 

 

 

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Source: WiDS

Datathon – 5 teams from NZ

Diversity in Data Science

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Liz McPherson (pictured above) is a Government Statistician, NZ

She was the first government statistician

She is determined not to be a statistic

She wants to leave a legacy

 

“We can’t solve our problems with the same thinking we used to create them  – have to have diverse thinking”

She lost love of numbers at primary school; role model issue

She got it back when studying statistics for her Geography degree

She says we need stats to solve real world problems

Make stats/stories available to NZ decision makers

UN Big Data – data is the lifeblood of decision making

Why does diversity matter?  Data science is about identifying why humans do what we do – using math, theory, new tools such as machine learning to solve problems

Empirical tools – needed to be good, not sufficient to be great

At the heart of data science discipline is creativity to solve problems

Diversity and diverse teams are more creative

Diversity can help understand issues, more empathy for users and challenge norms

“algorithmic transparency” – come from diverse backgrounds

Women tend to have a more balanced approach to taking decisions

 

 

How improve diversity?  3 perspectives:

1. grow talent as early as possible

2. attract people into sector

3. (could not hear the third)

 

 

Grow talent – introduce coding in education – Code Girls; grow the pipeline

 

MasterCard study shows that 30% of 17-19 studying STEM would not choose STEM jobs; perceive STEM is a male dominated industry

 

Talk and celebrate “women in tech”

 

Make recruitment as “blind as possible”

 

Improve collaboration

 

Focus on Gender Pay (9.4% in NZ)

 

Think of yourself as a role model

 

What is the next big think for census?  Hoping for 70% online census; Canada holds online completion of 68%

Her aim is to bother NZ people as little as possible; look at digital sourcing, more real time data

She leads OpenData as well; get as much “safe” data out the door for innovation

 

Affirmative action is unfair – good go to line?  She says once achieve balance, won’t need affirmative action; want diversity because it leads to better outcomes

 

Rosalind Archer

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Source: WiDS

 

She spoke in Maori; association with a place.

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Source: WiDS

She focuses on computational geoscience

Slide above focuses on different data sources

Combine data for a shared view

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Source: WiDS

 

She is obsessed with geo data

She is the first geo-engineer to collaborate with the Mayo Clinic on healthcare

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Source: WiDS

 

She says she tells her students all her models are wrong.

The question is how far wrong are the models.

How connect what she does know with what she does not know

 

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Source: WiDS

 

She was one of the many “firsts”

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Source: WiDS

She was the 2nd engineering professor at the University of Auckland

Look at leadership and governance

She got shoulder-tapped to do this

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Source: WiDS

 

Her Dean’s goal for 2020; she is at 27% now

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Source: WiDS

 

Boys are blue, girls are red

% of females growing as faculty grows

Not about more shutting men out, but need to grow pipeline

Will be explaining about creativity in schools; GirlBoss

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Source: WiDS

Engineering New Zealand has also announced their goal to increase female participation

Challenge organizations for campaign

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Source: WiDS

Power of Grass Roots

Challenge is they want 50% male membership

Sign up to a charter; will call things out that are not cool.

Challenge conference organizers to be inclusive

Learn to say no in a complete sentence

 

There is a lot to learn and take in; what can we do better for the future?

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2 Comments

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  1. Michelle Crapo

     

    I think it starts early than college. If I look at my son’s advance math classes he took in high school = less girls. Math and Science program in middle school = less girls. So if we can get our younger girls interested earlier it would help.

    That means by the time they move to college education the field already has more men than women.

    Not sure how to drive that. Rewards usually work with young people. But sometimes just getting them to school is a chore.

    The teachers may have a good idea why the numbers drop. We might want to see if there is data available for that.

    Just thinking out loud – nice initiative! Love the pictures,

    Michelle

    (2) 
  2. Moya Watson

     

    Thanks Tammy for the shout-out!  and i’m impressed that you attended New Zealand – how cool is THAT? wish we were all there. The Palo Alto one was pretty awesome.  Saw a lot of cool data science nerds for sure!

    (1) 

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