Global Women in Data Science New Zealand #WiDS2018 #IWD2018
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.
100+ events worldwide in 2018
Above shows community spanning dots around the world
Datathon – 5 teams from NZ
Diversity in Data Science
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”
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
She spoke in Maori; association with a place.
She focuses on computational geoscience
Slide above focuses on different data sources
Combine data for a shared view
She is obsessed with geo data
She is the first geo-engineer to collaborate with the Mayo Clinic on healthcare
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
She was one of the many “firsts”
She was the 2nd engineering professor at the University of Auckland
Look at leadership and governance
She got shoulder-tapped to do this
Her Dean’s goal for 2020; she is at 27% now
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
Engineering New Zealand has also announced their goal to increase female participation
Challenge organizations for campaign
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?
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,
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!