December 10, 2021 • 40 mins
Welcome to a groundbreaking episode of the LA PIPA Studios podcast, where we dive deep into the forefront of innovation and empowerment in technology. Our special guest is Sadie St. Lawrence, the esteemed Founder & CEO of Women In Data, a premier community championing the advancement of women in the fields of AI and Tech. With a remarkable membership spanning over 20,000 individuals across 17 countries and 50 cities worldwide, Women in Data stands as a testament to the power of community and the spirit of innovation. Sadie St. Lawrence is not just a leader; she is a trailblazer in data science, having equipped over 350,000 individuals with critical data science skills. She is the creative mind behind the Machine Learning Certification for UC Davis, showcasing her profound expertise in technical innovation, analytics management, and transformative leadership. Her exceptional contributions to the field have not gone unnoticed. Sadie's influence and impact have been recognized in prestigious publications such as USA Today and Dataversity. She has been honored with the Outstanding Service Award, named one of the 10 Most Admired Business Women to Watch in 2021, and ranked as a Top 21 Influencer in Data. Beyond her professional accolades, Sadie embodies courage, vision, and compassion, prioritizing people and outcomes in her approach to leadership. Her commitment extends beyond her role at Women In Data; she is an active board member for multiple startups, the engaging host of the Data Bytes podcast, a sought-after speaker, and an artist. Join us on the LA PIPA Studios podcast as we explore Sadie St. Lawrence’s inspiring journey, her insights on the intersection of technology and humanity, and her unwavering dedication to fostering a more inclusive and innovative future in tech. Prepare to be inspired by a conversation that challenges conventions and celebrates the transformative power of data and diversity.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
Bedroom is an independent data science an aiphirm specialized in
data driven business change. In this podcast, our guests help
us spread knowledge and experience with our listeners.
Speaker 2 (00:27):
Hello, good morning, Sadie. How are you doing.
Speaker 3 (00:30):
Good morning, Great to be chatting with you.
Speaker 2 (00:32):
I'm good same here. It's a pleasure to have you.
I have to say we'll go into the details of
this later, but we've had some women from Women in
Data UK in this podcast before. It was a great
conversation and I'm expecting no less for today. So before
(00:54):
getting into all of that, Yes, tell me what do
you have ahead for the day and any goals that
you blend a chief today and for the rest of
the week.
Speaker 3 (01:05):
Yeah, I'm so glad you asked today. You called on
a great day because I'm getting ready to go to
an event later. We're launching our program. It's called the
Pass Forward. So it's a three month data Analyst certification
and then the students get to work on a capstone
project and then get paired with mentors and get career
services support after that. And so for this program, we're
(01:29):
targeting women who were affected by COVID nineteen and left
the workforce. And this is an opportunity for them to
rescale and upskill and re enter the workforce. So later
today I'll be heading over there and we'll be sitting
at the booth and talking to participants and people interested
in the program. So really looking forward to that.
Speaker 2 (01:49):
Awesome. When you say there, where do you mean, Like,
where are you calling from?
Speaker 4 (01:55):
Yeah?
Speaker 3 (01:55):
So I'm calling from Sacramento, California. And this is a
program that we'll be doing with the city of francial
Cordova in California. So I'll just be driving about thirty
minutes and we'll be local in their city and talking
to the residents of their city.
Speaker 2 (02:11):
Awesome. And this program that you're mentioning is something that
you are launching as Women in Data US or Women
in Data in a partnership with university the University of California,
because I know you collaborate with them as well. Or
how is this designed from an institution perspective?
Speaker 3 (02:30):
Yeah, so through Women in Data we're the ones running
the whole program. Part of the education we'll be using
is through Data Camp, but we're hosting everything from training
of the people, the capstone project, mentorship, et cetera. And
then we're just doing it in partnership with a local
city to better support their residents.
Speaker 2 (02:53):
Sounds interesting. I may ask a few more questions about
women in data because I think it's really interesting that initiative,
But before doing that, I like to, you know, get
to know you a bit more. Of course, everyone that
listens to this will know that we've had a previous
conversation to prepare for this. But I do not have
(03:15):
all of the context on how you started in the
data space, how you decided to make this movement, how
you decided to start women in data. Actually you told
me you hold a vacelor in psychology, so someone would
think that you'd be more focused or interested in the
(03:38):
philosophy arts kind of area. But you are currently doing
scientific technical work right when it comes to data. Is
a lot of that and the machine learning course that
we will discuss later. So when did you decide to
make this move, how did you make this transition, and
what made you choose a rere in this space or
(04:02):
to pursue a master of science and analytics. I'm a
bit curious in that because I'm an engineer myself, So
for me with I guess makes sense. I More and
more people that have for sure, the same background that
you have, and that to me is really really interesting
and it brings a new perspective to this discipline because
(04:23):
you have a different approach to things, if that makes sense.
So I know, yes, say the.
Speaker 3 (04:33):
Well, I'm glad you asked, because it's funny you mentioned
the arts because actually my first major that I started
off in college was piano performance, so I started even
in more arts field, and through that process and taking
general education classes, I really fell in love with science
and the scientific method. So I transitioned my major two
years into psychology because I was really interested in studying
(04:58):
how the brain interprets music. So it's a way for
me to keep some of what I had learned in
my knowledge of music, but also adding this new component
of the brain and adding the scientific method which I
fell in love with. And actually in psychology, I really
fell in love with statistics and pretty much, you know,
(05:19):
whether it's the social sciences or the heart sciences, there's
always statistics involved because that's a way we have to
validate our assumptions and you know, prove our hypotheses. And
so from my work in psychology, you know, I was
a ta for a lot of statistics class and then
started working in a neuroscience lab, and so I really
(05:42):
had the intention to go get my PhD in neuroscience.
And once I had finished my bachelor's and was working
in that lab, I was getting a lot of great
hands on experience. And one afternoon, you know, after running
multiple tests and keeping taking care of the rodents that
I was running these tests on, at the end of
(06:04):
these experiments, I had to uthanize the rodents and there
was a moment when my eyes locked with one of
the rodents sides right before I was about to euthanize it,
and I just had this kind of overwhelming sense of
like what am I doing? Like this thing is a
live I'm studying its motions and now I have to
(06:24):
kill it. Like this does not feel good to me, right,
So I went home from the lab that day a
little discouraged of like, you know, what am I going
to do? Like I don't know if I want to
spend the rest of my life doing this. And I
started to just look at like what parts of the
job I liked and which parts I didn't like, And
you know, obviously didn't like the working with rodents and
(06:45):
having to dispose them at the end of experiments. But
I really loved the analysis side of like when I
collect the data, when analysis and what statistical tests would
I show on that? And so that's what really led
me to data science. And you know, I'm one of
those people that like, once I get a clue, I
like make up my mind right away, and I'm like, yep,
this is the new direction. And so you know, like
(07:06):
two weeks after that changed and went to get a
job and data and started applaying the master's programs in
data science.
Speaker 2 (07:14):
To be honest, I think you made the right move.
And I'm saying, the way you've pictured this and the
way you've explained this sounds quite analytical. You're trying to
balance the pros and the cons and you're doing it
in a very analytical way, right, Very very reasonable what
you're saying. And when you started making this move and
(07:37):
this transition towards you want to call it analytics, data science,
whatever it is, did you face any blockage or any
challenge that made you think, Okay, there may be more
people as women making this change or this transition. They
would need some sort of organ I say that supports
(08:00):
that or that claims that there isn't an equal opportunity
in this space for that That's why Women in Data
started or how did this all begin?
Speaker 3 (08:09):
Yeah, so it was about a year or two my
master's program in twenty fifteen. And you know, as most
people didn't look up the genders theatistics before entering the field, right,
So when I got into my program, I was a
bit surprised to have only one other female student in
my cohort and one it was just like, hey, I'm
(08:30):
not a data scientist yet, but I know if I
take two points, the trend line for how much progress
we're going to make in this field is not very high. Right.
But at the same time, like you know, I was
newer in the field and knew that if I was
going to survive in the field, I needed a community
of people, like I needed people that I could collaborate
with and lean on and find mentorship. And so I
(08:55):
didn't feel like, you know, I was necessarily had the
time to do it. I was working full time. I
was also getting my masters full time, but just really
felt like there was a great need here. And so
in twenty fifteen started you know, the first chapter of
Women in Data in my local city by hosting a
first event to bring people together around this cause.
Speaker 2 (09:18):
And story in truck may I ask how many you
brought into the first event? I was it very well received?
Speaker 3 (09:27):
No, it was not. I think for the first event
we had there was only one person who came, and
thankfully she brought to other people. So it started from
really humble beginnings of only having three people come to
the first event. But that was really all that was needed.
You know, really, if you want to change something, I
think just take someone standing up, one person coming along.
(09:51):
And you know, now today we're in over fifty cities
in seventeen countries across the world, so it's really incredible
what happens when one person stands up and another person
comes along to support.
Speaker 2 (10:03):
Congratulations on that because it really makes a difference. And
I believe what you said there. Community it's a very
significant word, and I think that you've achieved that. So
you don't have this is something that probably I asked before.
You have not much to do with women in data
UK in that sense, right, it's not a different chapter
(10:23):
from yours. It's a completely different entity.
Speaker 3 (10:26):
Yep, it's a different entity, and you know, there's a
lot of fantastic organizations in this space as well, and
I think it's great. We need you know, this is
a big problem to solve, right, so the more people
we have coming together and working on it, just the
better the outcomes will be for that.
Speaker 2 (10:44):
And how big I mean internationally a membership why speaking
is women in data at the moment as with a
different chapters, different countries and everything. Have you quantified how
many people belong have taken part in the movement?
Speaker 3 (10:59):
Yeah, so we are in over fifty cities, so those
are chapter representation and then those cities span seventeen countries.
You know, we have We started in the US, so
we have a large representation there, but our Asia Pacific, Europe, Africa,
and South America regions are growing really rapidly. And then
(11:21):
our community bases a little over twenty thousand people. And
then we have a paid membership program where people get
bought into all the programs and services that we offer
as an organization. So yeah, it's quite incredible, true.
Speaker 2 (11:40):
I mean again, congratulations from three or four people to
twenty thousand, I mean there is a lot of work there,
so you deserve to hear congratulations a lot of times.
Now leaving as AID Women in data. I know that
you also spend some time into education, specifically data science education. Right,
(12:04):
you become a well now well known structure. When I
was first learning about you, I was listening to this
podcast by John Crown in super Data Science, and I
heard that you've done or that you've taught too many
students people over this time. I'm curious what do you
(12:28):
do as a data science in structure, Like which kind
of content content do you try to focus on? Do
you also bring some of these soft kills needed when
it comes to working with data because of your background
in psychology, I mean, what do you cover during these courses?
Speaker 3 (12:49):
Yeah, So just to provide a little context for people.
Of course, my career path is a little zig zaggy,
but the problem is I'm a big pan of what
I call slash careers. Right, so you have like a
full time job and then you do a lot of
other things on the site. So you know, for the
majority of my career, I spent at least six years
(13:10):
in data science where I started a data science team.
I let that team and then moved into consulting and
was a consultant as an AI strategy consultant and got
to work with some amazing companies such as Google and
worked on contact tracing for different states. And so during
that time, really at the same time, I was running
(13:33):
Women in Data on the side and that really allowed
me to dive deep into my technical skills and about
you know, three or four years into getting the practice
as in a sense of data science practitioner.
Speaker 4 (13:48):
I had the opportunity in addition to teach as well.
Speaker 3 (13:52):
And again it was a time when I was like,
I don't know if I have the time to do
this right, Like, I have a full time job, I'm
running Women in Data on the side. But the opportunity
was to teach a class on Coursera, and I felt
like it was important for me to get back to
the data science community. I had started some of my
initial learnings on the Courserra platform and again I noticed
(14:14):
there just wasn't female representation of teachers on the Courserra
platform too, and so I felt it was essential for
me to you know, share what knowledge I had. And
so that allowed me to teach the first course SQL
for Data Science with UC Davis and Coursera, and you know,
I really didn't know what to expect going into it.
I had teaching experience, but that was as a piano teacher.
(14:38):
I had a ran piano studio studios forever, but not
from a technical matter or in a mook. So I
really went into it like if I could just benefit
one person's life. Again, like I take a very much
like it starts with one person approach, and today we've
had over three hundred and fifty thousand people take the class,
which is really incredible. So I think it just comes
(15:00):
down to having a really great you know, really when
you're going into teach, having a learner's mindset, Like when
I get ready to teach curriculum, I try and take
a step back from everything I know and like look
at it as if I was relearning it. And having
that like student mindset really allows me to provide content
(15:21):
in a way of how would the student receive it
versus how do I teach it right? How do I
share my knowledge? It's more of like how do I
share the knowledge from a learning perspective, because I'm just
as much of a student as my students are, Like,
I learned from my students all the time, and I
think that is really essential in delivering some of these courses.
Speaker 2 (15:42):
Right. Plus, you know, a lot of people think the
way that I do this in this sense that when
you are trying to explain something and you are preferred
to explain something enough detail, it's because you've digested that
well enough to understand that properly. So I think that
through that process you not only learn from these students,
(16:04):
but that it is a solid knowledge that stays with
you in anything that you that you may teach or
that you may cover during your instruction. And I'm curious
because you moved from psychology towards analytics. Now you are
(16:26):
into dedication. Over the course of the years, you also
developed women in data. I mean those are very busy
years for sure. And then how do you get a
hold of all these knowledge to develop a machine learning course?
I guess that you also go through some courses that
didn't exist before. Are you a self learner? Do you
(16:47):
consider yourself a self learner?
Speaker 3 (16:49):
Yes, very much so. I think you know a lot
of times people ask like, how do you do all
the things you do? And it's like, Oh, they're just
they're interesting to me and I want to learn more
about these are right, And I have a unique perspective.
So I was homeschooled. My you know, whole childhood, and
in that process, I a lot of times, you know,
(17:13):
was given books or textbooks, and it was kind of
left to just teach myself on my own by like
reading and sometimes I would grade my own work. And
I think that experience is different from maybe people who
went through a traditional, you know, high school or education program,
and it really allowed me to understand how how do
(17:34):
you learn? Like how do you just how do you
teach yourself? And that has proven really beneficial because now
we don't live in an era where you go to college,
you get one degree, and then you go work that
degree for the rest of your life. You know, it's
estimated you'll have between four to six different careers within
your life now. And if that's the case, then there's
(17:57):
a high need to always continually be learning reinventing yourself,
and even more so in the technical space. Right as
new developments enhance, it just never stops. And I'm super
excited about kind of the next avenue that I'm looking
to get into and what I'm learning right now, because
it just the learning is really what excites me the most.
Speaker 2 (18:20):
I guess that I share that feeling. I don't know,
have you read the one hundred Year Life Book by
any chance, because it really covers what you're saying that
you know, based on how old we are going to
be getting progressive leming. Now people get to eighty five
(18:40):
year olds being very healthy, then it's going to be
ninety and longevity will continue to expan. Of course, the
evolution or the pace at which technology advances continues to grow,
meaning either you stay ahead or you stay on top
(19:01):
of all of these changes and you keep learning and
you have that self learning and eager mentality or your
struggle because as you said, now it's data science and
data scientist, this kind of the sexier role. But in
five years, in five years, that's going to change, and
you know, to be awake and to be paying attention
(19:21):
to all of these newly emerging roles or market niche
I think makes a difference to what you can do
uh in your career. One quick question because it is
not specifically related to what we were covering about. You know,
education and you've in it, uh in structuring, data science
(19:44):
or anything. But I also saw in LinkedIn that you
serve from various startup boards. It kind of companies do
you support or do you work with and again I'm curious.
I'm thirty one myself, and you know, it's an amazing
thing to be serving up in serving in boards, and
(20:07):
I guess that you're quite young too, So what do
you bring to the table?
Speaker 3 (20:11):
Yeah, so you know, similar to how I looked at
kind of my life and skills when I transition from
neuroscience to data science, of like what skills are transferable?
I recommend people do that when they're looking for board
positions or advisory positions, like, hey, what what knowledge has
have I developed at this point that may be useful?
Speaker 4 (20:34):
And I think that's really like the first step is
clearly identifying like your knowledge base and skill set, and
that really is ageless, right.
Speaker 3 (20:42):
So you know a lot of times we think of
these positions as you know, having to be fifty year older,
but with how fast technology is changing, like a lot
of boards now are looking for younger people who've developed
this emerging technology knowledge that they can bring into their organization.
And so that's how I got started with working for
(21:03):
Open Grants dot io. So they're they're looking to democratize
how public funding is distributed, and a big portion of
that is being able to use machine learning and so
you know, I am an expert in that space, and
so that was the area that I brought into the
table for them. And then you know, as you continue
(21:23):
to grow and evolve in your career, there's other areas
that you become an expert in. And the other board
position that I work with is Global Urban Nomads. So
Global Urban nomads is mission is to create compassionate leaders
around the world, and given my experience working within women
in data and forming a community, they really wanted to
(21:47):
bring me in for that knowledge base. So I think
it really just comes down to knowing yourself and then
networking and finding people and missions that resonate with you,
and there will be a spot for you as long
as you are connected to that mission and know the
skills that you're bringing to the table.
Speaker 2 (22:07):
This is kind of the answer that I was looking for.
The thing is I respond that we have so many
pieces of knowledge that we can bring in terms of technology,
in terms of our own professional development to the table,
even though we can be considered young. You know, when
looking at other professionals that have thirty for the year's experience,
(22:28):
I guess that I would be quite biased if I
said there is so much that we can bring to
the table. So it was kind of looking for that
answer to reinforce what I also think. And based on
the experience that you have when working or supporting these companies,
and also based on the experience that you have taking
(22:51):
students through your courses, probably you have a compelling view
on the different challenges that you know, when we're going
toization phases when trying to adopt analytical data science and
mechanisms within their company, you know, across different business units
and so on and so forth. So what do you
(23:12):
think if you were to highlight one major challenge that
CEO the organization's hold phases when when doing this right,
when trying to embed these data science techniques, what do
you think that is it related to people to you know,
the understanding of technology, a combination uh, the difficulty to
(23:34):
get a hold of the talent that you need to
build these capabilities, finding the right partner to outsource some
of these responsibilities, and what do you think?
Speaker 3 (23:44):
Yeah, the biggest challenge you see over and over again
is really on the education standpoint, And what I see
companies do a lot is they want to implement AI
and so they go and get all the tools they
need they hire the right technical team, right, But then
the technical team is separate from the business, and the
(24:07):
business has not been educated on how to use AI
and their business, how to understand data and how it
will enhance them, and so there's just this huge communication block,
and the technical team unfortunately usually doesn't have the knowledge
of the business either for how it should be implemented.
(24:28):
And so that's I think a big portion of why
we see statistics like eighty percent of data science projects
fill right is the communication isn't there. And I think
we put way too much focus on the technical side
of things and not enough on first helping everyone and again,
not just the technical teams, all the business teams really
(24:52):
understand what this technology is and how it can be
implemented into our businesses. And I think if we start
with educating everyone, not just upskilling, our technical teams will
have much greater success. So that's really kind of the
base one. We can get into all the nitty gritties
(25:13):
of details of tools and you know, project planning and
things like that, but I think if we can do
a better job of educating everyone in our business, we'll
see a huge change in the success of those projects.
Speaker 2 (25:25):
And if you were to think of unbiased independent advice too.
You know, technology leader, you know someone like the head
of technology CEO of a company that you know is
feeling some uncertainty on how to bet or how to
invest on this, you'd share any piece of advice you
(25:50):
know that's narrow or what what would you say to
him or her?
Speaker 3 (25:55):
What do you can you clarify? What you mean on
how to bet on? Is like where to put their
investment in? Is that?
Speaker 2 (26:03):
I wouldn't say bad, That's not the right way of
saying it. Would say how would you tell them to start? First?
I mean living a side the educational context that's needed,
you know, to tackle this a strategic level in a company.
If you were to recommend a first step towards this,
(26:23):
or to provide a single advice to someone that needs
to make this decision, what would you say or what
would you recommend? In aspect would it be to focus on,
you said, promoting a culture that understands what state analytics
right and understanding the capabilities of that. But now moving
(26:43):
into the tactical side of things, what do you think
would be the first step to take?
Speaker 3 (26:50):
Yeah, So, if you're a CEO or CIO. So if
you're more on the technical side, it really comes down
to having that close connection with the business and really
clearly understanding what the business strategy and priorities are. Anytime
I'm working with a business, that's my first step is
to take off my technology hat, right and fully understand
(27:14):
their business operating model, their priorities, their KPIs. Once I
fully understand that, then I'm able to clearly see opportunities
for enhancement and improvement, and then the AI or the
machine learning is just secondary. Right. That is just it
(27:34):
almost goes without even saying that you can use if
you're knowledgeable in that space. Right, once you've clearly identified
those opportunities, it just falls into place. It's a it's
almost like a no brainer of like, yes, we could
use essentially AI to monitor churn rate better and take
steps to add reminders and enhance that customer experience so
(27:58):
that we don't experience the rate we're experiencing. Right, So,
really it comes down to fully understanding and grasping what
those priorities are and then leaving the technology to be
the last aspect of it. Too often, I see you know,
leaders essentially coming in with oh, we're going to move
(28:21):
to the cloud, or you know, get this new tool
that's going to solve all of our problems. I have
never once seen any single tool solve all the problems, right, Like,
the tool should always be secondary, and the true business
problem understanding should always be first.
Speaker 2 (28:40):
Okay, So it's from what you've responded to this question
that I've asked. The previous one is you know, once
you've gathered or I'm sure enough knowledge at least at
the management level, you know what's datentalytic state science and
what's capable of and what you can do with it.
It's also knowing how to link it with the business
(29:04):
and the strategic goals of the business appropriately. And then
the technology is yes, I mean to an end to
make it happen.
Speaker 3 (29:11):
I guess, yes, definitely. I mean how I look at
it is, you know, I have a library in my
mind of all the machine learning algorithms right that there are,
and so that's my framework for solutions. But you know,
just as a doctor when they're meeting with a patient
has to first understand, okay, what are your symptoms before
(29:33):
they prescribe the plethora of drugs or treatments that are
available if they're coming in and prescribing a drug first
or a treatment without understanding truly what are the symptoms,
they're going to go right. Right, So I have to
leave kind of my library of tools and algorithms, you know,
on the side first to make sure I'm having that
(29:54):
conversation to either understand the symptoms, the problems, or what
the opportunity is. Then once I fully understand it, then
I can.
Speaker 4 (30:01):
Go back to my library and say, Okay, given these factors,
I can now match that with the perfect algorithm or
match that with the perfect technology solution to meet your need.
Speaker 2 (30:12):
To be honest, it's similar to an anecdote. It isn't
an anecdote. It's an analogy that I always say, right
when when trying to think if someone should be using
data science or a yeah, a specialist to partner with
when launching an initiative in house or in house plus
a partner or an ally. It's related to I mean,
(30:34):
if you're trying to decorate your house and you're trying
to make changes in your living room, your kids in
and everything, you wouldn't do that. You wouldn't do that yourself,
only right, you would look for someone who has in
many homes and has some context on what's in the market,
no different tastes and all of that. I know it's
(30:55):
very simple and very vague, but it all comes down
to having you know that life that you're talking about
in your mind of which techniques can I apply to
work on this because I have more context on what
has worked for different use cases, different business contexts, and
so on and so forth. So I kind of agree
(31:16):
with that. Okay, say the I think I've covered a
lot of interesting questions and more of all, thanks for
spending almost I think this is thirty five in its
sort of with with me. It's been a very interesting conversation.
But just before we close the call off, I always
(31:37):
ask a few questions that you know will help a
lot to anyone who's listening to this conversation. One being
a book recommendation. It doesn't have to be data related though,
or red can be related to anything. But I'm curious
to know what do you read? What what do you
read that makes you move from psychology to data analytics
(32:00):
or do anything? What makes you be so motivated to
you know, launch very interesting and powerful initiatives as women
in data is start related to a book to a
combination of books, and what do you read and what
do you recommend?
Speaker 3 (32:14):
Yeah, so I have so many books I love, so
I usually tell people just what I'm reading right now,
or like my favorite book I'm reading right now, because
I'm usually reading like three books at a time. So
right now I'm reading two books, the Trivium and the Quadrivium,
and I think these are really essential going back to
like always being a student. I've started having some conversations
(32:37):
where I'm like, I think I need to start my
whole education over, and that means starting it over from
a liberal arts perspective of like truly understanding grammar and
rhetoric and history and then diving in again to like
mathematics and geometry right from a basic standpoint. And so
(32:59):
these two books are fa fantastic, and they both start
with like just breaking down the meaning of each letter,
breaking down the meaning of each number, and then building
off of there for a full liberal arts education. So
I am loving this experience because I'm someone who finds
patterns in the abstract and connections between things, and I
(33:21):
think it's just like such a great opportunity as an
adult and a lifelong learner, like we can always be
a student and always be refreshed in these areas. And
the liberal arts is an extremely important skill set to
have a knowledge based to acquire for whatever your line
of work is amazing.
Speaker 2 (33:40):
I'll pay attention to those, Okay, And if now more
onto the I wouldn't say ticky, but more into the
hands on material or the data analytics, data science material
that you may read. Can be a newsletterer, any sort
of learning portal. Of course, you cannot mention GURSA because
(34:00):
I understand you're going to suggest that course are an
amazing toolkit. Of course you can. You can mention your course.
I'm just kidding. But there any other needs letter that
you think it's it's really useful and you would recommend
to someone listening to this conversation.
Speaker 3 (34:15):
Yeah, So for reading in AI and data science, I
really love Medium. I think there are just so many
great individual contributors and I try and stay away from
news sources and go to people who are actually working
and practicing in the field, and so medium is a
really great place for that. And then you have a
(34:37):
lot of subsites on there with like towards data science
and Hacker News, et cetera. But that's usually for like
newsletter stuff where I'm going to go and then kind
of emerging. I'm really loving the website seventeen twenty nine.
This is all talking about the network state and essentially
you know, mathematics, cryptocurrencies, transhumanism, space travel, all all the
(35:04):
fun new things that are coming out there. So highly
recommend checking out seventeen twenty nine.
Speaker 2 (35:09):
It does sound amazing. I'm just saying those many topics
are well, they require a life to learn about them
in themselves. And the currency with blockchain, that's a whole
learning block. Then you have space travel, I mean, and
then you may be interested in reading about the analytics.
(35:30):
So wow, so many interesting things. I'll definitely pay attention
to that. When that was seventeen ninety.
Speaker 3 (35:37):
One, seventeen twenty nine.
Speaker 2 (35:40):
Twenty nine, seventeen twenty nine, okay, I'm muwful with remembring
numbers you see. Okay. And so after the book recommendation,
the newsletterary portal one, the last one which or who
should I be calling and should they be spending some
time with chatting as I'm doing with you today, Someone
that you'd be keen to, you know, hear or learn from.
Speaker 3 (36:02):
Yeah, so actually I would look up the author of
seventeen twenty nine biology. It's amazing Twitter channel as well.
I think he's really on the forefront of just what's
happening in technology, and it was the former CTEO of
(36:23):
coinbase Wow, and you know is doing a lot of
awesome work in terms of how we transform education and
how people get paid and work in the future, and
you know has kind of dedicated his life now to
expanding these areas. So if you could get him on
(36:43):
the podcast, I think that would be incredible. I will
definitely be listening to him multiple times.
Speaker 2 (36:49):
Usually ask for guest recommendations that you know someone would
be able to facilitate an introduction. If this is the case,
and you know he's a high profile and definitely he's
very interesting. I'll try chase that because it sounds really
appealing to me. And if I get hold of him,
(37:11):
I'll let you know it will be a very very
interesting and insightful conversation.
Speaker 3 (37:17):
Okay, I can make another recommendation for you so that
I can make some introduction. So the other the other
one I have is Lisa t and.
Speaker 4 (37:26):
She started an AI company to protect children's privacy online,
especially for things like child pornography, and it's doing amazing
work in this space, and I would highly recommend her
as a guest on the show as well.
Speaker 2 (37:46):
This sounds really really interesting. I think it's another kind
of it's another movement that you know, has you know,
a powerful positive impact to society, you know. So thanks
for that. Okay, So again I've said it before, I
(38:08):
do mean it. I'm not saying it out of courtesy,
but this has been an interesting conversation. I think it
inspires many many people. You may not think how awesome
it is to build a community of twenty thousand people,
but indeed you may guess it sounds amazing from the outside,
(38:28):
and it does look really promising into what's going to become,
because I think it will have, you know, very positive
impact for many many women out there that may want
to make the same move as you did, or there
are simply already working in the data certain space because
they come from a steps up subject and they feel
(38:50):
misrepresented or anything like that. So I think it's amazing.
Plus you've done this while doing your it's say day
work at other companies and very very interesting call the
one I the one I had today, So Thank you, Sadie.
Thanks for spending this time with me this morning.
Speaker 3 (39:10):
My pleasure. It's been a fantastic.
Speaker 2 (39:11):
Conversation, awesome, and I hope you'll have a nice day
as well.
Speaker 3 (39:15):
You two, thanks so much.
Speaker 2 (39:16):
Thank you. Said by.
Speaker 4 (39:59):
M hmm
Speaker 2 (40:03):
Todt uber bent to the
Bedroom is an independent data science an aiphirm specialized in
data driven business change. In this podcast, our guests help
us spread knowledge and experience with our listeners.
Speaker 2 (00:27):
Hello, good morning, Sadie. How are you doing.
Speaker 3 (00:30):
Good morning, Great to be chatting with you.
Speaker 2 (00:32):
I'm good same here. It's a pleasure to have you.
I have to say we'll go into the details of
this later, but we've had some women from Women in
Data UK in this podcast before. It was a great
conversation and I'm expecting no less for today. So before
(00:54):
getting into all of that, Yes, tell me what do
you have ahead for the day and any goals that
you blend a chief today and for the rest of
the week.
Speaker 3 (01:05):
Yeah, I'm so glad you asked today. You called on
a great day because I'm getting ready to go to
an event later. We're launching our program. It's called the
Pass Forward. So it's a three month data Analyst certification
and then the students get to work on a capstone
project and then get paired with mentors and get career
services support after that. And so for this program, we're
(01:29):
targeting women who were affected by COVID nineteen and left
the workforce. And this is an opportunity for them to
rescale and upskill and re enter the workforce. So later
today I'll be heading over there and we'll be sitting
at the booth and talking to participants and people interested
in the program. So really looking forward to that.
Speaker 2 (01:49):
Awesome. When you say there, where do you mean, Like,
where are you calling from?
Speaker 4 (01:55):
Yeah?
Speaker 3 (01:55):
So I'm calling from Sacramento, California. And this is a
program that we'll be doing with the city of francial
Cordova in California. So I'll just be driving about thirty
minutes and we'll be local in their city and talking
to the residents of their city.
Speaker 2 (02:11):
Awesome. And this program that you're mentioning is something that
you are launching as Women in Data US or Women
in Data in a partnership with university the University of California,
because I know you collaborate with them as well. Or
how is this designed from an institution perspective?
Speaker 3 (02:30):
Yeah, so through Women in Data we're the ones running
the whole program. Part of the education we'll be using
is through Data Camp, but we're hosting everything from training
of the people, the capstone project, mentorship, et cetera. And
then we're just doing it in partnership with a local
city to better support their residents.
Speaker 2 (02:53):
Sounds interesting. I may ask a few more questions about
women in data because I think it's really interesting that initiative,
But before doing that, I like to, you know, get
to know you a bit more. Of course, everyone that
listens to this will know that we've had a previous
conversation to prepare for this. But I do not have
(03:15):
all of the context on how you started in the
data space, how you decided to make this movement, how
you decided to start women in data. Actually you told
me you hold a vacelor in psychology, so someone would
think that you'd be more focused or interested in the
(03:38):
philosophy arts kind of area. But you are currently doing
scientific technical work right when it comes to data. Is
a lot of that and the machine learning course that
we will discuss later. So when did you decide to
make this move, how did you make this transition, and
what made you choose a rere in this space or
(04:02):
to pursue a master of science and analytics. I'm a
bit curious in that because I'm an engineer myself, So
for me with I guess makes sense. I More and
more people that have for sure, the same background that
you have, and that to me is really really interesting
and it brings a new perspective to this discipline because
(04:23):
you have a different approach to things, if that makes sense.
So I know, yes, say the.
Speaker 3 (04:33):
Well, I'm glad you asked, because it's funny you mentioned
the arts because actually my first major that I started
off in college was piano performance, so I started even
in more arts field, and through that process and taking
general education classes, I really fell in love with science
and the scientific method. So I transitioned my major two
years into psychology because I was really interested in studying
(04:58):
how the brain interprets music. So it's a way for
me to keep some of what I had learned in
my knowledge of music, but also adding this new component
of the brain and adding the scientific method which I
fell in love with. And actually in psychology, I really
fell in love with statistics and pretty much, you know,
(05:19):
whether it's the social sciences or the heart sciences, there's
always statistics involved because that's a way we have to
validate our assumptions and you know, prove our hypotheses. And
so from my work in psychology, you know, I was
a ta for a lot of statistics class and then
started working in a neuroscience lab, and so I really
(05:42):
had the intention to go get my PhD in neuroscience.
And once I had finished my bachelor's and was working
in that lab, I was getting a lot of great
hands on experience. And one afternoon, you know, after running
multiple tests and keeping taking care of the rodents that
I was running these tests on, at the end of
(06:04):
these experiments, I had to uthanize the rodents and there
was a moment when my eyes locked with one of
the rodents sides right before I was about to euthanize it,
and I just had this kind of overwhelming sense of
like what am I doing? Like this thing is a
live I'm studying its motions and now I have to
(06:24):
kill it. Like this does not feel good to me, right,
So I went home from the lab that day a
little discouraged of like, you know, what am I going
to do? Like I don't know if I want to
spend the rest of my life doing this. And I
started to just look at like what parts of the
job I liked and which parts I didn't like, And
you know, obviously didn't like the working with rodents and
(06:45):
having to dispose them at the end of experiments. But
I really loved the analysis side of like when I
collect the data, when analysis and what statistical tests would
I show on that? And so that's what really led
me to data science. And you know, I'm one of
those people that like, once I get a clue, I
like make up my mind right away, and I'm like, yep,
this is the new direction. And so you know, like
(07:06):
two weeks after that changed and went to get a
job and data and started applaying the master's programs in
data science.
Speaker 2 (07:14):
To be honest, I think you made the right move.
And I'm saying, the way you've pictured this and the
way you've explained this sounds quite analytical. You're trying to
balance the pros and the cons and you're doing it
in a very analytical way, right, Very very reasonable what
you're saying. And when you started making this move and
(07:37):
this transition towards you want to call it analytics, data science,
whatever it is, did you face any blockage or any
challenge that made you think, Okay, there may be more
people as women making this change or this transition. They
would need some sort of organ I say that supports
(08:00):
that or that claims that there isn't an equal opportunity
in this space for that That's why Women in Data
started or how did this all begin?
Speaker 3 (08:09):
Yeah, so it was about a year or two my
master's program in twenty fifteen. And you know, as most
people didn't look up the genders theatistics before entering the field, right,
So when I got into my program, I was a
bit surprised to have only one other female student in
my cohort and one it was just like, hey, I'm
(08:30):
not a data scientist yet, but I know if I
take two points, the trend line for how much progress
we're going to make in this field is not very high. Right.
But at the same time, like you know, I was
newer in the field and knew that if I was
going to survive in the field, I needed a community
of people, like I needed people that I could collaborate
with and lean on and find mentorship. And so I
(08:55):
didn't feel like, you know, I was necessarily had the
time to do it. I was working full time. I
was also getting my masters full time, but just really
felt like there was a great need here. And so
in twenty fifteen started you know, the first chapter of
Women in Data in my local city by hosting a
first event to bring people together around this cause.
Speaker 2 (09:18):
And story in truck may I ask how many you
brought into the first event? I was it very well received?
Speaker 3 (09:27):
No, it was not. I think for the first event
we had there was only one person who came, and
thankfully she brought to other people. So it started from
really humble beginnings of only having three people come to
the first event. But that was really all that was needed.
You know, really, if you want to change something, I
think just take someone standing up, one person coming along.
(09:51):
And you know, now today we're in over fifty cities
in seventeen countries across the world, so it's really incredible
what happens when one person stands up and another person
comes along to support.
Speaker 2 (10:03):
Congratulations on that because it really makes a difference. And
I believe what you said there. Community it's a very
significant word, and I think that you've achieved that. So
you don't have this is something that probably I asked before.
You have not much to do with women in data
UK in that sense, right, it's not a different chapter
(10:23):
from yours. It's a completely different entity.
Speaker 3 (10:26):
Yep, it's a different entity, and you know, there's a
lot of fantastic organizations in this space as well, and
I think it's great. We need you know, this is
a big problem to solve, right, so the more people
we have coming together and working on it, just the
better the outcomes will be for that.
Speaker 2 (10:44):
And how big I mean internationally a membership why speaking
is women in data at the moment as with a
different chapters, different countries and everything. Have you quantified how
many people belong have taken part in the movement?
Speaker 3 (10:59):
Yeah, so we are in over fifty cities, so those
are chapter representation and then those cities span seventeen countries.
You know, we have We started in the US, so
we have a large representation there, but our Asia Pacific, Europe, Africa,
and South America regions are growing really rapidly. And then
(11:21):
our community bases a little over twenty thousand people. And
then we have a paid membership program where people get
bought into all the programs and services that we offer
as an organization. So yeah, it's quite incredible, true.
Speaker 2 (11:40):
I mean again, congratulations from three or four people to
twenty thousand, I mean there is a lot of work there,
so you deserve to hear congratulations a lot of times.
Now leaving as AID Women in data. I know that
you also spend some time into education, specifically data science education. Right,
(12:04):
you become a well now well known structure. When I
was first learning about you, I was listening to this
podcast by John Crown in super Data Science, and I
heard that you've done or that you've taught too many
students people over this time. I'm curious what do you
(12:28):
do as a data science in structure, Like which kind
of content content do you try to focus on? Do
you also bring some of these soft kills needed when
it comes to working with data because of your background
in psychology, I mean, what do you cover during these courses?
Speaker 3 (12:49):
Yeah, So just to provide a little context for people.
Of course, my career path is a little zig zaggy,
but the problem is I'm a big pan of what
I call slash careers. Right, so you have like a
full time job and then you do a lot of
other things on the site. So you know, for the
majority of my career, I spent at least six years
(13:10):
in data science where I started a data science team.
I let that team and then moved into consulting and
was a consultant as an AI strategy consultant and got
to work with some amazing companies such as Google and
worked on contact tracing for different states. And so during
that time, really at the same time, I was running
(13:33):
Women in Data on the side and that really allowed
me to dive deep into my technical skills and about
you know, three or four years into getting the practice
as in a sense of data science practitioner.
Speaker 4 (13:48):
I had the opportunity in addition to teach as well.
Speaker 3 (13:52):
And again it was a time when I was like,
I don't know if I have the time to do
this right, Like, I have a full time job, I'm
running Women in Data on the side. But the opportunity
was to teach a class on Coursera, and I felt
like it was important for me to get back to
the data science community. I had started some of my
initial learnings on the Courserra platform and again I noticed
(14:14):
there just wasn't female representation of teachers on the Courserra
platform too, and so I felt it was essential for
me to you know, share what knowledge I had. And
so that allowed me to teach the first course SQL
for Data Science with UC Davis and Coursera, and you know,
I really didn't know what to expect going into it.
I had teaching experience, but that was as a piano teacher.
(14:38):
I had a ran piano studio studios forever, but not
from a technical matter or in a mook. So I
really went into it like if I could just benefit
one person's life. Again, like I take a very much
like it starts with one person approach, and today we've
had over three hundred and fifty thousand people take the class,
which is really incredible. So I think it just comes
(15:00):
down to having a really great you know, really when
you're going into teach, having a learner's mindset, Like when
I get ready to teach curriculum, I try and take
a step back from everything I know and like look
at it as if I was relearning it. And having
that like student mindset really allows me to provide content
(15:21):
in a way of how would the student receive it
versus how do I teach it right? How do I
share my knowledge? It's more of like how do I
share the knowledge from a learning perspective, because I'm just
as much of a student as my students are, Like,
I learned from my students all the time, and I
think that is really essential in delivering some of these courses.
Speaker 2 (15:42):
Right. Plus, you know, a lot of people think the
way that I do this in this sense that when
you are trying to explain something and you are preferred
to explain something enough detail, it's because you've digested that
well enough to understand that properly. So I think that
through that process you not only learn from these students,
(16:04):
but that it is a solid knowledge that stays with
you in anything that you that you may teach or
that you may cover during your instruction. And I'm curious
because you moved from psychology towards analytics. Now you are
(16:26):
into dedication. Over the course of the years, you also
developed women in data. I mean those are very busy
years for sure. And then how do you get a
hold of all these knowledge to develop a machine learning course?
I guess that you also go through some courses that
didn't exist before. Are you a self learner? Do you
(16:47):
consider yourself a self learner?
Speaker 3 (16:49):
Yes, very much so. I think you know a lot
of times people ask like, how do you do all
the things you do? And it's like, Oh, they're just
they're interesting to me and I want to learn more
about these are right, And I have a unique perspective.
So I was homeschooled. My you know, whole childhood, and
in that process, I a lot of times, you know,
(17:13):
was given books or textbooks, and it was kind of
left to just teach myself on my own by like
reading and sometimes I would grade my own work. And
I think that experience is different from maybe people who
went through a traditional, you know, high school or education program,
and it really allowed me to understand how how do
(17:34):
you learn? Like how do you just how do you
teach yourself? And that has proven really beneficial because now
we don't live in an era where you go to college,
you get one degree, and then you go work that
degree for the rest of your life. You know, it's
estimated you'll have between four to six different careers within
your life now. And if that's the case, then there's
(17:57):
a high need to always continually be learning reinventing yourself,
and even more so in the technical space. Right as
new developments enhance, it just never stops. And I'm super
excited about kind of the next avenue that I'm looking
to get into and what I'm learning right now, because
it just the learning is really what excites me the most.
Speaker 2 (18:20):
I guess that I share that feeling. I don't know,
have you read the one hundred Year Life Book by
any chance, because it really covers what you're saying that
you know, based on how old we are going to
be getting progressive leming. Now people get to eighty five
(18:40):
year olds being very healthy, then it's going to be
ninety and longevity will continue to expan. Of course, the
evolution or the pace at which technology advances continues to grow,
meaning either you stay ahead or you stay on top
(19:01):
of all of these changes and you keep learning and
you have that self learning and eager mentality or your
struggle because as you said, now it's data science and
data scientist, this kind of the sexier role. But in
five years, in five years, that's going to change, and
you know, to be awake and to be paying attention
(19:21):
to all of these newly emerging roles or market niche
I think makes a difference to what you can do
uh in your career. One quick question because it is
not specifically related to what we were covering about. You know,
education and you've in it, uh in structuring, data science
(19:44):
or anything. But I also saw in LinkedIn that you
serve from various startup boards. It kind of companies do
you support or do you work with and again I'm curious.
I'm thirty one myself, and you know, it's an amazing
thing to be serving up in serving in boards, and
(20:07):
I guess that you're quite young too, So what do
you bring to the table?
Speaker 3 (20:11):
Yeah, so you know, similar to how I looked at
kind of my life and skills when I transition from
neuroscience to data science, of like what skills are transferable?
I recommend people do that when they're looking for board
positions or advisory positions, like, hey, what what knowledge has
have I developed at this point that may be useful?
Speaker 4 (20:34):
And I think that's really like the first step is
clearly identifying like your knowledge base and skill set, and
that really is ageless, right.
Speaker 3 (20:42):
So you know a lot of times we think of
these positions as you know, having to be fifty year older,
but with how fast technology is changing, like a lot
of boards now are looking for younger people who've developed
this emerging technology knowledge that they can bring into their organization.
And so that's how I got started with working for
(21:03):
Open Grants dot io. So they're they're looking to democratize
how public funding is distributed, and a big portion of
that is being able to use machine learning and so
you know, I am an expert in that space, and
so that was the area that I brought into the
table for them. And then you know, as you continue
(21:23):
to grow and evolve in your career, there's other areas
that you become an expert in. And the other board
position that I work with is Global Urban Nomads. So
Global Urban nomads is mission is to create compassionate leaders
around the world, and given my experience working within women
in data and forming a community, they really wanted to
(21:47):
bring me in for that knowledge base. So I think
it really just comes down to knowing yourself and then
networking and finding people and missions that resonate with you,
and there will be a spot for you as long
as you are connected to that mission and know the
skills that you're bringing to the table.
Speaker 2 (22:07):
This is kind of the answer that I was looking for.
The thing is I respond that we have so many
pieces of knowledge that we can bring in terms of technology,
in terms of our own professional development to the table,
even though we can be considered young. You know, when
looking at other professionals that have thirty for the year's experience,
(22:28):
I guess that I would be quite biased if I
said there is so much that we can bring to
the table. So it was kind of looking for that
answer to reinforce what I also think. And based on
the experience that you have when working or supporting these companies,
and also based on the experience that you have taking
(22:51):
students through your courses, probably you have a compelling view
on the different challenges that you know, when we're going
toization phases when trying to adopt analytical data science and
mechanisms within their company, you know, across different business units
and so on and so forth. So what do you
(23:12):
think if you were to highlight one major challenge that
CEO the organization's hold phases when when doing this right,
when trying to embed these data science techniques, what do
you think that is it related to people to you know,
the understanding of technology, a combination uh, the difficulty to
(23:34):
get a hold of the talent that you need to
build these capabilities, finding the right partner to outsource some
of these responsibilities, and what do you think?
Speaker 3 (23:44):
Yeah, the biggest challenge you see over and over again
is really on the education standpoint, And what I see
companies do a lot is they want to implement AI
and so they go and get all the tools they
need they hire the right technical team, right, But then
the technical team is separate from the business, and the
(24:07):
business has not been educated on how to use AI
and their business, how to understand data and how it
will enhance them, and so there's just this huge communication block,
and the technical team unfortunately usually doesn't have the knowledge
of the business either for how it should be implemented.
(24:28):
And so that's I think a big portion of why
we see statistics like eighty percent of data science projects
fill right is the communication isn't there. And I think
we put way too much focus on the technical side
of things and not enough on first helping everyone and again,
not just the technical teams, all the business teams really
(24:52):
understand what this technology is and how it can be
implemented into our businesses. And I think if we start
with educating everyone, not just upskilling, our technical teams will
have much greater success. So that's really kind of the
base one. We can get into all the nitty gritties
(25:13):
of details of tools and you know, project planning and
things like that, but I think if we can do
a better job of educating everyone in our business, we'll
see a huge change in the success of those projects.
Speaker 2 (25:25):
And if you were to think of unbiased independent advice too.
You know, technology leader, you know someone like the head
of technology CEO of a company that you know is
feeling some uncertainty on how to bet or how to
invest on this, you'd share any piece of advice you
(25:50):
know that's narrow or what what would you say to
him or her?
Speaker 3 (25:55):
What do you can you clarify? What you mean on
how to bet on? Is like where to put their
investment in? Is that?
Speaker 2 (26:03):
I wouldn't say bad, That's not the right way of
saying it. Would say how would you tell them to start? First?
I mean living a side the educational context that's needed,
you know, to tackle this a strategic level in a company.
If you were to recommend a first step towards this,
(26:23):
or to provide a single advice to someone that needs
to make this decision, what would you say or what
would you recommend? In aspect would it be to focus on,
you said, promoting a culture that understands what state analytics
right and understanding the capabilities of that. But now moving
(26:43):
into the tactical side of things, what do you think
would be the first step to take?
Speaker 3 (26:50):
Yeah, So, if you're a CEO or CIO. So if
you're more on the technical side, it really comes down
to having that close connection with the business and really
clearly understanding what the business strategy and priorities are. Anytime
I'm working with a business, that's my first step is
to take off my technology hat, right and fully understand
(27:14):
their business operating model, their priorities, their KPIs. Once I
fully understand that, then I'm able to clearly see opportunities
for enhancement and improvement, and then the AI or the
machine learning is just secondary. Right. That is just it
(27:34):
almost goes without even saying that you can use if
you're knowledgeable in that space. Right, once you've clearly identified
those opportunities, it just falls into place. It's a it's
almost like a no brainer of like, yes, we could
use essentially AI to monitor churn rate better and take
steps to add reminders and enhance that customer experience so
(27:58):
that we don't experience the rate we're experiencing. Right, So,
really it comes down to fully understanding and grasping what
those priorities are and then leaving the technology to be
the last aspect of it. Too often, I see you know,
leaders essentially coming in with oh, we're going to move
(28:21):
to the cloud, or you know, get this new tool
that's going to solve all of our problems. I have
never once seen any single tool solve all the problems, right, Like,
the tool should always be secondary, and the true business
problem understanding should always be first.
Speaker 2 (28:40):
Okay, So it's from what you've responded to this question
that I've asked. The previous one is you know, once
you've gathered or I'm sure enough knowledge at least at
the management level, you know what's datentalytic state science and
what's capable of and what you can do with it.
It's also knowing how to link it with the business
(29:04):
and the strategic goals of the business appropriately. And then
the technology is yes, I mean to an end to
make it happen.
Speaker 3 (29:11):
I guess, yes, definitely. I mean how I look at
it is, you know, I have a library in my
mind of all the machine learning algorithms right that there are,
and so that's my framework for solutions. But you know,
just as a doctor when they're meeting with a patient
has to first understand, okay, what are your symptoms before
(29:33):
they prescribe the plethora of drugs or treatments that are
available if they're coming in and prescribing a drug first
or a treatment without understanding truly what are the symptoms,
they're going to go right. Right, So I have to
leave kind of my library of tools and algorithms, you know,
on the side first to make sure I'm having that
(29:54):
conversation to either understand the symptoms, the problems, or what
the opportunity is. Then once I fully understand it, then
I can.
Speaker 4 (30:01):
Go back to my library and say, Okay, given these factors,
I can now match that with the perfect algorithm or
match that with the perfect technology solution to meet your need.
Speaker 2 (30:12):
To be honest, it's similar to an anecdote. It isn't
an anecdote. It's an analogy that I always say, right
when when trying to think if someone should be using
data science or a yeah, a specialist to partner with
when launching an initiative in house or in house plus
a partner or an ally. It's related to I mean,
(30:34):
if you're trying to decorate your house and you're trying
to make changes in your living room, your kids in
and everything, you wouldn't do that. You wouldn't do that yourself,
only right, you would look for someone who has in
many homes and has some context on what's in the market,
no different tastes and all of that. I know it's
(30:55):
very simple and very vague, but it all comes down
to having you know that life that you're talking about
in your mind of which techniques can I apply to
work on this because I have more context on what
has worked for different use cases, different business contexts, and
so on and so forth. So I kind of agree
(31:16):
with that. Okay, say the I think I've covered a
lot of interesting questions and more of all, thanks for
spending almost I think this is thirty five in its
sort of with with me. It's been a very interesting conversation.
But just before we close the call off, I always
(31:37):
ask a few questions that you know will help a
lot to anyone who's listening to this conversation. One being
a book recommendation. It doesn't have to be data related though,
or red can be related to anything. But I'm curious
to know what do you read? What what do you
read that makes you move from psychology to data analytics
(32:00):
or do anything? What makes you be so motivated to
you know, launch very interesting and powerful initiatives as women
in data is start related to a book to a
combination of books, and what do you read and what
do you recommend?
Speaker 3 (32:14):
Yeah, so I have so many books I love, so
I usually tell people just what I'm reading right now,
or like my favorite book I'm reading right now, because
I'm usually reading like three books at a time. So
right now I'm reading two books, the Trivium and the Quadrivium,
and I think these are really essential going back to
like always being a student. I've started having some conversations
(32:37):
where I'm like, I think I need to start my
whole education over, and that means starting it over from
a liberal arts perspective of like truly understanding grammar and
rhetoric and history and then diving in again to like
mathematics and geometry right from a basic standpoint. And so
(32:59):
these two books are fa fantastic, and they both start
with like just breaking down the meaning of each letter,
breaking down the meaning of each number, and then building
off of there for a full liberal arts education. So
I am loving this experience because I'm someone who finds
patterns in the abstract and connections between things, and I
(33:21):
think it's just like such a great opportunity as an
adult and a lifelong learner, like we can always be
a student and always be refreshed in these areas. And
the liberal arts is an extremely important skill set to
have a knowledge based to acquire for whatever your line
of work is amazing.
Speaker 2 (33:40):
I'll pay attention to those, Okay, And if now more
onto the I wouldn't say ticky, but more into the
hands on material or the data analytics, data science material
that you may read. Can be a newsletterer, any sort
of learning portal. Of course, you cannot mention GURSA because
(34:00):
I understand you're going to suggest that course are an
amazing toolkit. Of course you can. You can mention your course.
I'm just kidding. But there any other needs letter that
you think it's it's really useful and you would recommend
to someone listening to this conversation.
Speaker 3 (34:15):
Yeah, So for reading in AI and data science, I
really love Medium. I think there are just so many
great individual contributors and I try and stay away from
news sources and go to people who are actually working
and practicing in the field, and so medium is a
really great place for that. And then you have a
(34:37):
lot of subsites on there with like towards data science
and Hacker News, et cetera. But that's usually for like
newsletter stuff where I'm going to go and then kind
of emerging. I'm really loving the website seventeen twenty nine.
This is all talking about the network state and essentially
you know, mathematics, cryptocurrencies, transhumanism, space travel, all all the
(35:04):
fun new things that are coming out there. So highly
recommend checking out seventeen twenty nine.
Speaker 2 (35:09):
It does sound amazing. I'm just saying those many topics
are well, they require a life to learn about them
in themselves. And the currency with blockchain, that's a whole
learning block. Then you have space travel, I mean, and
then you may be interested in reading about the analytics.
(35:30):
So wow, so many interesting things. I'll definitely pay attention
to that. When that was seventeen ninety.
Speaker 3 (35:37):
One, seventeen twenty nine.
Speaker 2 (35:40):
Twenty nine, seventeen twenty nine, okay, I'm muwful with remembring
numbers you see. Okay. And so after the book recommendation,
the newsletterary portal one, the last one which or who
should I be calling and should they be spending some
time with chatting as I'm doing with you today, Someone
that you'd be keen to, you know, hear or learn from.
Speaker 3 (36:02):
Yeah, so actually I would look up the author of
seventeen twenty nine biology. It's amazing Twitter channel as well.
I think he's really on the forefront of just what's
happening in technology, and it was the former CTEO of
(36:23):
coinbase Wow, and you know is doing a lot of
awesome work in terms of how we transform education and
how people get paid and work in the future, and
you know has kind of dedicated his life now to
expanding these areas. So if you could get him on
(36:43):
the podcast, I think that would be incredible. I will
definitely be listening to him multiple times.
Speaker 2 (36:49):
Usually ask for guest recommendations that you know someone would
be able to facilitate an introduction. If this is the case,
and you know he's a high profile and definitely he's
very interesting. I'll try chase that because it sounds really
appealing to me. And if I get hold of him,
(37:11):
I'll let you know it will be a very very
interesting and insightful conversation.
Speaker 3 (37:17):
Okay, I can make another recommendation for you so that
I can make some introduction. So the other the other
one I have is Lisa t and.
Speaker 4 (37:26):
She started an AI company to protect children's privacy online,
especially for things like child pornography, and it's doing amazing
work in this space, and I would highly recommend her
as a guest on the show as well.
Speaker 2 (37:46):
This sounds really really interesting. I think it's another kind
of it's another movement that you know, has you know,
a powerful positive impact to society, you know. So thanks
for that. Okay, So again I've said it before, I
(38:08):
do mean it. I'm not saying it out of courtesy,
but this has been an interesting conversation. I think it
inspires many many people. You may not think how awesome
it is to build a community of twenty thousand people,
but indeed you may guess it sounds amazing from the outside,
(38:28):
and it does look really promising into what's going to become,
because I think it will have, you know, very positive
impact for many many women out there that may want
to make the same move as you did, or there
are simply already working in the data certain space because
they come from a steps up subject and they feel
(38:50):
misrepresented or anything like that. So I think it's amazing.
Plus you've done this while doing your it's say day
work at other companies and very very interesting call the
one I the one I had today, So Thank you, Sadie.
Thanks for spending this time with me this morning.
Speaker 3 (39:10):
My pleasure. It's been a fantastic.
Speaker 2 (39:11):
Conversation, awesome, and I hope you'll have a nice day
as well.
Speaker 3 (39:15):
You two, thanks so much.
Speaker 2 (39:16):
Thank you. Said by.
Speaker 4 (39:59):
M hmm
Speaker 2 (40:03):
Todt uber bent to the
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy, Jess Hilarious, And Charlamagne Tha God!