[APPLAUSE] >> So thanks so much for
joining in the, in the career panel.
It's, for me, always been one of the most exciting parts of
is to have a little fire chat about various issues.
Related to careers, related to, women in data science, and
anything else that comes up. We'll spend the first 20
minutes or so, talking here on the podium and
I, will feed some questions to the, the panelists. And they
will introduce themselves, and after that it will be open for
quite a long Q&A. So please prepare questions and be ready
to raise your hand. And as we're wrapping up, I will
announce that we'll have last question here from the panel.
And so then you can already start raising your hands, so
the people with the mic can carry them through. If there
are people on the outside on the live stream who wanna ask
a question, please remember, you do that at the hashtag
which is #2018q or on Facebook.
And, we'll take your questions as well, we'll keep an eye
on that. But, let's start by quick introductions, and
you don't have to say much about your bio because it's,
it's in here. But I'm gonna to give you one prompt, just say
your name, that's good. I would like to hear from you,
one reason why you're here today, so why do you like. And
then I would love to hear from you,
what is the one thing that you heard so
far today that really resonated with you?
Yeah, think, can you do that? >> Yep.
>> [LAUGH]
>> You want to start, Ziya?
>> Yeah, sure.
>> Yeah.
>> I'm Ziya Ma,
from Intel, and I'm very excited to be here because
we all know data science is a fast growing area.
Yet, woman only make 20, well,
actually less than 20% of the data professionals. You know,
it's encouraging to see in the last few years that,
forums like has been working rigorously to drive or
to encourage a woman to get into this field. It's also
exciting to see so many women passionate about data science.
I think in the earlier discussion today,
one thing about that really resonates well with me is,
big data is playing a critical part for data
science advancement. So it is big data advancement and,
algorithm, machine learning, deep learning advancement
together that's pushing forward the advancement of
data science, so. >> Thanks.
>> That's one.
>> All right, so I'm Jennifer,
from Atlassian. Before somebody asks what Atlassian
is, Atlassian is the company that makes software like
confluence that help lots of companies collaborate with
their colleagues. So, so one interesting thing about me is,
actually, I'm a particle physicist originally.
And so I jumped into data science, a few years ago.
I absolutely don't, don't regret that, decision.
And so the reason why I'm here today and
why I think it's very important for all of us to be
here, is To never forget you are not alone in this world,
right. Because where you are, you're a woman in data
science among like large crowds of men. And, you know,
you feel like it's challenging to get your voices heard, and
it's not always easy on the daily basis.
And then at some point, it's reassuring to see that they're
all the people facing the same challenges and
to see other faces that, meet the same challenges that we
do. >> Yeah,
I have mentioned about my background when I was giving
my presentations, so I won't go into it.
But to answer your other question, what is that really
sort of inspired me today. One is to see so many women,
it's one thing to hear about it when you are in Texas.
But coming and seeing the women and
listening to all those women- >> It's not fake news.
>> It's amazing.
>> It's not.
>> It's not fake news, yes,
it's real news. >> [APPLAUSE]
>> And among
the presentations, I enjoyed every single one of them.
One, because I had this interview to give outside, so
I didn't listen. And
I had some credit card issues that I had to deal with,
some credit card fraud, so I missed one presentation. But
one thing that sort of really got me interested is
the Airbnb. Because I was always very kind of wondering,
should I, you know, go for Airbnb.
Who is going to clean the room once, you know,
the apartment or whatever once they. After hearing, right,
what Eleanor has talked about I feel sort of more.
>> You're gonna use it now?
>> I think I'm going to use
it. >> Wow, one more customer.
>> So that's the one thing
that's, you know- >> Thank you.
>> Yes, sort of,
I got very interested. >> Eleanor, how about you?
>> Yeah, that's so cool,
we're definitely working hard to make sure it's a great
experience. >> Yeah.
>> So I hope you give it
a try- >> Thank you.
>> And, yeah, so
I'm so thrilled to be here.
I was at the women in data science conference last year,
and had that kind of moment of shock and
awe at this amazing room of women.
And hearing the speakers, it's so inspiring. I love that
there is this combination of technical talks and also,
kinda more broad talks like a career panel. It's a really,
really nice mix and the people here are just incredible.
So that is a reason that I was inspired to come again.
And obviously, I'm a huge supporter of women in data
science and so excited for this community to grow and
to make sure that people don't feel alone and feel supported.
That's really important,
so that's what I'm here for. Definitely, a highlight has
been connecting with Bhavani about Airbnb. Also, you know,
I think a lot of the talks are really inspiring.
I, one that resonated for me was Letanya Sweeney's talk,
and that kind of call to action at the end about using
data science to make the world a better place.
And the power there, and, you know,
I think that's why I'm in it, and, you know, I think that's
a really inspired message. >> And
we hear this from a lot of people, and
one of the questions that often comes up, you know,
when you talk to women who are thinking about entering
the field or women who are wanting to grow in the field.
They always say, now what do I really need to have
a fantastic career? Now you've all been very accomplished,
but the interesting thing is that you, most of you you have
different backgrounds. So you did computational sciences and
engineering, you did particle physics, you did math and-
>> [CROSSTALK]
>> And computing,
and then you,
from education. So that alone shows that women, and
men, of course, from all sorts of different backgrounds
find data science at some point. And
it leads me to this, this one really critical question for
many is, what is the skill set that you really need to be
successful in this field? Judging from the talks today,
you need to do everything. You need to be mathematics,
computing, you need to have empathy. You need to
understand ethics, you need to understand social sciences,
you need to a good humanist, right? What else do you need
to do, you need to actually be able to program a little bit.
And then you need to have team skills,
you need to be good communicator.
Right, and so a lot of you are probably thinking,
I don't have all of that. >> I think there's actually
just one skill you really need, and
that's the willingness to be challenged when you learn new
things on a daily basis. Because it's all about like
trying new things all the time and being comfortable with
the uncomfortable. >> Being comfortable with
the uncomfortable, yeah, I buy into that,
what else, Ziya? >> I think it also depends on
what kind
of job you want to get into in this field. And sure,
there are marketing jobs, there are sales jobs, even in
the technical field, there are data engineering jobs,
data visualization. The jobs that need,
statistician type jobs, and machine learning,
deep learning, algorithm development type of jobs, or
data. You know, all the type of jobs. I think you have to
ask yourself what is the job that you wanna get into? And
then assess the skill sets that you have today,
and the job that's required for your dream job.
And then you need to figure out
a way to get into that.
Actually I provide professional coaching for
a lot of women f rom Intel and
also from the industry. The many women still
find it's a high bar to move into the data science field.
My advice is, that if you find the gap is too significant for
you, you may wanna make adjacent move first.
Move to an area that is not too much a stretch for
you, you can still leverage your previous expertise. But
also it opens the door for
you to learn about new skills in the data science field.
And then it will better prepare you for
finally moving into the field. So that would be-
>> You wanna add something?
>> Yeah, yes,
I think very good points.
But as you said, one person cannot do everything right? So
for me, focus, focus, focus, that's very important.
You'd work hard, and then look at data science, and
look at the areas that you want to focus on, right?
If it's algorithm development, if it's sort of applying
the algorithms, in that case you really need to be a domain
expert. Whether it's healthcare or it's finance or
cyber security or whatever you need.
So, either focus on your developing these deep
algorithms, or learn enough about the algorithms,
focus on the domains, or maybe educating and
teaching people about data science. So whatever you do,
I believe that you've got to work hard and really focus
on what you have to do. >> That being said,
I'd like to actually add something,
because I'm actually an interesting story, because I
was a data scientist in a lot of different companies,
working on different types of projects, right?
I mean so I definitely see the value of
finding like this one type of data that we really think is
absolutely amazing. So I think you know like obviously
[INAUDIBLE] that was because I believe that the data that we
have is really amazing because, we're in a good
position to understand the way people work.
I would even say we're in a good position to understand
how women work compared to men, I even think that we have
a role to play here. But from switching from an industry to
another one, I actually started realizing that it's
just amazing how much of the knowledge I acquired
as a physicist I was able to transpose to e-commerce,
to software and to other things like an. You also meet
people who have like, you know, this disability of
switching one role to another to actually expand knowledge
across different areas. >> Yeah I think that's very
important, and the agility and you hear this very often.
The other thing that you, that you hear a lot and I was just
reading an interview with Maria Clove in Wired Magazine
in January. And she was saying look, sometimes or
very often and maybe that's different with Intel now,
the the way that positions are advertised and
what people are asking for in the in the job position
descriptions, a lot of people look at this and say I don't
have any of that. Knowing that is not really inclusive of
some of the other skills that are really necessary for
a good data scientist. You know,
some of the communication skill, for example, or
team work, what we sometimes call the softer skills.
Although I wish they weren't called softer,
they're just as important as anything else.
Do you think, for those of you who are hiring people and
helping them, that the way that jobs are described
is actually good enough? >> Actually, you know,
that's a great question. I'll start by sharing an example.
You know, my team actually have deep learning or
AI engineers. I also have people that have been working
in data management or data engineering field for
long time. About 18 months ago, a few data engineers in
my team decided to take, the deep learning training, deep
learning class from Stanford. So the two of them went to
class, they finish the class at the end of the semester,
they passed on the exam and they got some certificate.
Immediately afterwards, both of them, without applying,
they got AI job offers from a top cloud service provider.
That shows actually how desperate the industry is for
the skill set and how low the bar is for hiring people.
>> [LAUGH] So we can,
you can all do it, the bar is really low.
>> [LAUGH]
>> So don't be afraid
of the job descriptions. You know, put put yourself through
some necessary training, develop the basic skills,
you can learn from the field after you get the jobs.
But you just have to take that risk and
give it a try. >> You know.
>> That's a good starting
point. >> The, the other thing that
you hear very often and the difference in when talk about,
people talk about the difference between men and
women. I don't like to make this so generic but-
>> We are, we are different.
>> Yeah, we are a little
different. One of the things that you often hear,
is that when, when man see requirements for, for
the next job or promotion or new job,
they will apply even if they don't have many of those. But
with women we always say well I don't know this, and
I don't know that, so maybe I'm not suitable. In your
encounters in Intel and, and UT Dallas, or at Air BNB,
when you're interviewing people, or Atlassian and,
do you see this? Do you see that women or men, male versus
female applicants come to you? >> I have seen that,
I'm beginning to realize more and
more now because I'm aware of it. In the beginning I wasn't
aware, but I've been around for
so many years, that I am seeing now men are just there,
they regardless of where they come from, it doesn't have to
be, it's not American men or Chinese men or
Indian men, it's regardless, they want to go for it. But
women are always, not always most of the time,
they hesitate, and I'm seeing that. And
it's sort of frustrating, but that's, so that's why I think
women really need other women to support them.
And for me my strongest supporters, as I said,
when I went to Honeywell, is a woman who hired me.
National Science Foundation,
again is the woman who supported me. So
my strongest supporters have been women.
So that's really important. >> I definitely think that
women are more prone to like impostor syndrome you know?
Right so, it's much easier for women to
doubt ourself than I think men in similar situations,
so that's definitely something you observe.
>> And it is very true,
because I have been hiring for many years, it is very true.
Usually when women are 80% confident or ready, that we
think we have 80% or 90% of the skills, that's the time
when we feel comfortable saying I'm ready to apply,
I'm qualified for this job.
But I've seen in many of the cases, men there are only 50%
qualified, but they speak with great confidence that I'm so
ready for this job. >> [LAUGH].
>> I am still convinced this
is where we really need to earn ourselves that
opportunity. We need to talk about our qualification,
the potential, our ability to learn, and then go and fight
for those job opportunities. And before we worry about how
big the gap is, sometimes you just have to make that step.
>> Yeah, it comes back to what
you said, being comfortable, being uncomfortable.
And then of course how much discomfort you can deal
with varies a little bit from person to person, right? But
the message I think here is just jumping, right and do it,
that's great. Okay, so let's shift focus a little bit.
You know, a lot of people also ask,
what is it like to be a data scientist from day to day?
And when, when I give data science lectures or courses,
I always say, well, you know, don't make it too idealistic.
Because as data scientists here,
you can solve the world's problems, but you know,
really, 85% of the time you're doing data wrangling. So,
now maybe I'm not right about this, but The, so the question
is, you know, what does your day look like? And if you're
now managing and not doing much data science yourself, go
back to the time when you were starting data science is. What
do you, what do you do on a, on a daily basis? [INAUDIBLE]
do you wanna talk about it? >> Yeah,
I'd be happy to jump in.
This is actually something we collected data on,
our data science team. >> Of course, yeah.
[LAUGH] >> We took a survey of,
>> So it's a lot of,
lot of coffee drinking, yeah, you know.
>> Yeah, we definitely have
a lot of surveys from the data science team.
We're d, very data-driven. We looked at, time use and asked
people how they're spending their time. And, you know,
actually, I talked about, you know, three tracks of work,
analytics, inference, and algorithms. And, you know,
we do see people spending time in those three areas and,
and having some spikes, depending on their area of
expertise. But I would say that data wrangling is
a constant throughout, for any data scientist on the team.
And, you know, I think that's just what you need to expect.
I mean, if you want to produce great results,
you need to great data. And so spending the time to make sure
that your data is high quality and, you know, that, that's
just gonna pay off in, in the results that you can achieve.
You know, I do think that it's something that,
many companies are trying to decrease right because
it is something that we spend a lot of time on. And
I mentioned the global metrics project as one example, where,
you know, if we can build tools to kind of automate and
scale, defining metrics or building dashboards.
The more that we can, develop those tools to,
scale the work that is taking the most time for
data scientists and, and maybe could be automated,
then that could be a big win for our people on the team. So
that's, that's definitely an active area of research for
us in development with our engineering team,
helping to build those tools.
But, yeah, I'm trying to remember what the exact stat
was now of, like, how much time people spent data
wrangling. It was actually a lot less than I thought.
I think people give the impression that's it's, like,
90% of their time. It wa, it was a lot less than that, but
I'm sure that varies, you know, by company and role, so.
>> I actually believe it
depends on the level of maturity of the company,
right? >> Definitely, yeah.
>> I mean, because actually,
like, working for different types of companies,
different sizes,
I actually find out that when you are on an early
stage company that just gets started with data,
you to do a lot of education. Like, people would tell you,
like, hey, you have a lot, ton of data, you can do anything
you want with it. I don't have the right data.
So it's not necessarily as you see, as you think it is. So,
I mean, obviously, like, there is always an educational part,
like, talking to the other stakeholders.
This is how you need to do your data collection.
This is how much you need to
invest for hiring, for technologies, etc.
So I mean, I personally,
I spend a good deal of my career talking to people and
explaining what data science was about. You always, always
face this world with, like, this idea that, you know,
like, you know, well, [LAUGH] >> It's okay, yeah no, that's
great. But, I was hoping that one of you would say, yes,
you do spend, spend a lot of time on data wrangling, but
that's also fun. >> Mm-hm.
>> [LAUGH]
>> Yes.
>> Yeah?
>> Yeah.
>> Yeah, okay, good.
You know, we don't wanna leave you with this impression 85%
of the job is gonna be totally boring and, you know, I'm not,
I'm not gonna go for it. So, so before we,
we give it to the audience, I, I always like to ask people,
you know, you're working in this field,
you're all very passionate about what you do.
And, and you all have a lot of in-influence and
impact on the field. So tell us about, looking at the field
as a whole, or, or maybe even your own narrower, area.
What are your fondest hopes and your biggest fears?
You know, we hear, we heard so much today about the amazing
opportunities of, of, data science and
all the things that we could do with it. And then also,
you know, so in the questions, some, some fears, and, and
some worries about this. And of course, there's,
there's a reason for this. So tell me, I'm interested to
hear from all of you. What, what is your fondest hope,
you know, what you think will be achieved and, and
your, your biggest fear? Or maybe the other way around,
so that we end on a positive note, so-
>> [LAUGH]
>> Biggest fear,
fondest hope. >> I think, my biggest fear or
concern is the, the supply demand gap for the skill sets,
needed. And I, earlier, I, I gave, an example. And
actually, in the last, few years, both academia and
industry have recognized th-this gap, and
that's why we're seeing an increasing number of academic
programs and, industry workshops to help accelerate
to the closure of the gap. But honestly, in my view,
it's a long journey. It will take quite some time before
we see a true ready and a mature workforce.
And my, hope is that, you know,
data science today still has a high bar. And
even though we expect a wider deployment of data science for
businesses or even into our daily life, but the bar is too
high, and my hope is that in the foreseeable future,
we can truly democratize data science so
that it's more accessible for individuals and
businesses to improve our life quality and
our business results. >> Thanks, Ivani,
do you want to jump in? >> Yes, yes, so
I think, my concern,
the fear is what I said,
right? Is cybersecurity because this data mining,
or data science techniques, could be attacked.
So that is something that I'm really concerned about. And
I don't think, I mean, what I talked about is just one tiny,
tiny, tiny solution about modifying the support victim
issue. The hope, the fondest hope is there's so
much that data science can do, including in cybersecurity
when you apply. But I also agree sort of with what they,
said, the supply and demand. I mean, we really need more and
more data scientists, you know, for all these companies.
And so can we have, a workforce,
a trained workforce? One other thing I wanted to mention,
very briefly, is that we need more federal government
support funding. Cybersecurity fortunately,
we have got strong education like NSF has,
scholarship facilities where we train US citizens for
cybersecurity education, BSMS, B, anyone of those levels. And
then they go, and they have to go and work for
the federal government for a certain number of years.
Two-year scholarship means they've got to work for
two years. But I don't think we have such programs,
at the federal level because sometimes they would say,
why can't the companies pay for science, sorry,
data science? But I think we, I really believe that we need
more support to develop these fundamental techniques and
algorithms. >> Right, so everybody,
NSF listening right now, yep.
[LAUGH] >> NSF and
Department of Defense, and anyone.
>> Trump, in the new budget,
should be in there. >> Yes.
>> Yeah.
>> [LAUGH]
>> What about you?
>> No, I think,
I think for me the biggest worry is,
I still see a huge disconnect between what's, the business,
the business side of things and
data science part of things, right?
I mean, so usually, you have, like, a company that at some
point decides that they have sufficient enough data to,
use it for, like, improving their products or whatever.
And, they give a very severe misunderstanding of what data
science is about, right?
I mean, so you have, like, the two extremes of the spectrum
that on the one side you have the person that didn't use
that with the data you can do anything you want. And
then on the other side, you have the, unbelievers, right?
And so, one of the problems that you see a lot in,
in the industry is that, sometimes you have data
science structured as a lab, right? I mean, so
you will have, like, only data scientist siloed in one place.
Far remote from the stakeholders and
the business people, right? And so I'm always worried
about, like, how do you engage the conversation?
How do we explain to people what data science can do for
your business, right?
I mean, so I think, it's really important to try to
engage the conversation with the right stakeholders.
And I think There is not enough investors in
the science field to do just that.
And this is where we, women, I think are probably you know
like a good communicators. And we have a role to play to
actually like try to tie together the business value of
the together with the- >> So
that is your fondest hope, that more women will come and
then that problem will be solved.
>> Yes.
>> [LAUGH] What about you,
Helena? >> So
I would say my biggest fear is the misuse of data, and
the unintended consequences from doing that.
You know, we have so much machine learning.
And a lot of the talks have covered you know,
some of the negative consequences of that. And
how we have people who maybe are applying the methods but
aren't really paying attention to those consequences, and so
that, that scares me. And I, I'm worried about that and
how do we make sure that that's not happening.
I think it's a really interesting open question.
On the flip side, you know, I, I'm really excited about how
there's a shift to use data more and more for
all kinds of applications.
And really to be thinking strategically and
logically. And for that to be something that many more
people are learning about. One of the programs that's been so
inspiring that we've had at Airbnb is what we call
Data University. And the idea is that we educate people at
the company about how to first ask a good question.
And then how to, you know, do simple things with data to be
able to answer and understand that question.
And also to be able to you known, think critically about
what they might read in the newspaper, right?
Like there are so many studies that get published
that are correlation and not causation,
and how do you think about that? And you know,
I think that there's so much power in everyone being able
to think critically about how the data is being used. And
to start to use data more and more to make better decisions.
So I think that's like an exciting future,
so I guess the, the scare is we don't do it well and
the exciting future is we do it well?
[LAUGH] >> Yeah,
well it does make sense.
Okay we're gonna open it up to questions from the audience.
So lot's of questions, can we get some mics? It's easier.
When you ask a question, if there is a particular person
you would like to answer that question, please just address
it to that person, okay? >> Hi, thank you so much for
all your inputs and your experiences and your stories.
It really helps us.
The one thing that I am very curious to found out is
that as recruiters you might have come across a lot
many applications in a, in your years of experience.
I'm just wondering what really stands out to you when you see
these applications versus data science job positions?
And when you look at these applicants usually,
I mean from a third person's perspective,
I'm just wondering as to whether you really try to keep
a men to women ratio? Do you really look at that? Or
is it just your short list based on what appeals to
you and what fits the job? >> Zia, you want to start
with that, cuz you've done a lot of hiring?
>> Yeah definitely, and
we actually we consider, we take both into considerations.
So the first, we, you know,
you have to have the right skill sets. So
we're looking for, especially, we're not looking for somebody
with all the skills that Margot would just explaining.
[LAUGH] So we're looking for a few critical skill sets.
Let's say if we have an opening for
data engineering job. We look at if the person has any, you
known data management and data storage type of experience.
If we're looking for AI a type of skills, then we wanna see
if the person has algorithm development or
machine learning, deep learning type of skills.
But also we all know this,
Intel is very committed to diversity and inclusion. So
we put a lot of effort, after Intel is going to be the first
high tech company to achieve full representation for women
and underrepresenting minority by the end of this year. And
we'll well on track for that. >> [APPLAUSE]
>> And it's not easy to
achieve that since two years ago we set the goal.
We put a lot of emphasis onto like hiring,
the hiring process. We go extra miles to make sure
that we bring the right diversity candidates
into the the screening pool. We also, you know,
once we hire people, we focus a lot on retention and
inclusion. We wanna to make sure everybody in our company
come to work, you know, bring the full selves to,
to the work environment. So there are a lot of extra work
that we have to do but, yes, we pay attention to both skill
sets and also you know, supporting a, a diversity and
include it into the process. >> Same for the others,
or some significant differences with that?
>> Like, I can
say actually, so
when I started like hiring I was really focused on like
keeping the ratios, right? And so at some point I was like I,
I, I was feeling like I was spending too much time trying
to focus on this. So I just completely ignored the names
and just looking at the resumes. And, and
now what I'm really after is really diversity of thoughts.
So I actually realized maybe a little bit late in my career,
that'll, I come from a applied math background, right? And so
for a while I was working on the team where all
the others were coming from an engineering background. And so
I realized like I'm the only person here who cares about
validating a model. Who cares about doing things right or
doing things like off, you know,
like the mathematical way, right?
And so I, I realized later that it really matters to have
people coming from different backgrounds. And so this like,
I really value a lot when I see on the resume like what
has this person been doing? Do they have something different
to bring to the team that no one on the team has right now?
>> Very nice.
>> I just wanted to add
something very quick. Because I don't do recruiting in
terms of hiring people and but students and faculty. But
what I find is that, in data science, also cyber security,
especially data science and finding there are a lot of,
international students. Master's for graduate level,
Master's and PhDs. So what we are really trying to do is
to actively recruit domestic students for a Master's and
PhD in data science. And
I think cyber security also it's in the same situation, so
you really need to do a much better job.
>> Yeah.
>> I think the only thing I
would add is also looking for people who demonstrate growth
mindset to some degree. You known, trying out new courses,
or, you know, demonstrating that they really
enjoy learning. Because again that's been a theme throughout
that the field is constantly evolving.
And so someone who's excited to learn something new and,
and try something different so that they can keep up to date
with their skills. I think that's really important.
You know, other than that, we definitely focus a lot on
ensuring that we have a diverse
pipeline. And, you know, I think thinking holistically
about diversity as well is really important. You know,
that's, that's, that's what will bring that diversity of
thought that leads to innovation. And so, you know,
not just focusing only on gender, but thinking
about other aspects as well. >> Great.
>> And I really like the idea
of, like, different backgrounds, too. U, I think
that's super important. >> Well, thanks very much for
that, I think. Useful enough, yeah?
You are happy with your answer?
Julia, she, by the way, she's also a high school student.
So and this is her second with. [LAUGH] Go for it.
>> Well thank you.
Miss Jennifer, frankly, you mentioned that you loved
the type of data that you're working with, partly because
it enables you to understand, how people might work.
What about that data, and are there potentially other
data sources out there that you think lend themselves to
a similar purpose? >> So
there is definitely a lot of data, and
there is an explosion of like the different tools that
people use to get their work done. And so you know,
like obviously there is a lot of data.
And so for me like this is like a completely unscratched
part of data science, right? Try to understand how people,
I mean, people talk about social media,
so how do people talk together like casually, right? But
not necessarily how do they get work done, right? But what
I think is really unique about Atlassian is that we don't
have just one product, we have an entire suite of product.
And usually we have customers who have the entire suite, and
so basically, it means that we can form a team from,
you know, like the time that they start a project all
the way to completion. And then, we can do a tone of
really cool things that have not been done yet.
And so that's why I'm definitely excited that we're,
with what we're doing. >> Great, next question.
>> Hi, thank you very much,
and I had a concern with the word democratization.
Just because it just seemed like there was a connotation
there of speed and ease and perhaps shallowness.
And the word that kind of popped into my head as
the counterpoint was respect, respect for
privacy, respect for human rights. And also kind of
respect for the professional integrity of the data science.
I know that for myself going from learning,
there was this big jump when I got a job. Like, no,
it's on me to know what this data is and ask where it is.
It's on me to choose the, the models that I'm gonna use.
It's on me to decide what gets stored and
what gets thrown away. And there were so
many of these things that you couldn't possibly cover in
school. But I was interested in what the panel
had to say about this concept of.
I mean, of course, I love the idea of democratization,
I would love the idea of more women being in the field. But
this idea of what it means, what is our professional
integrity, what is the, what is the, where is our respect?
[LAUGH] And what do we shoot for, I don't know
if that makes sense. >> I would say,
although I'm not working in the industry.
I did work in the industry and federal research lab, NSF, and
now UT Dallas. I think some of the things I'm finding,
you've got to teach the students at an early age when
they are in grad school.
I was, during the lunch I was at the ethics,
data science ethics. Because with data science, you have,
the people that are working in data science,
they have access to all this data. Right,
we are seeing from companies like Airbnb and Atlassian and
Intel and Visa, they have all this data. So you have to make
sure that they are not misusing the data, and
that is something that I am really concerned about.
And that's why I think also
it's very important to have a data science ethics course,
as well as cybersecurity ethics, ethic,
ethical hacking, all of that is so
important to the students. >> Yeah, I so agree with you,
especially nowadays where trust in science is really not
that high. And so a lot of us are really worried about it,
what about you, Ziya, how would you respond?
>> So yeah,
I think it's a very valid concern, but if I look at this
field, I think today the field is still not that mature, and
it's too busy advancing the basic,
what's the basic capabilities. So, and
as we have more complete and
more visible capabilities come into the field,
I think privacy, security will definitely follow.
If you look at it in the domains, before like AI,
before data science, you know big data has been in place for
the last ten years now. But when, the first five years
when big data was evolving, very few people, very few
customers even talked about privacy or data protection.
But nowadays, it is a prerequisite in order for
any enterprise to use big data.
You must have a well governed data store,
well managed data with great privacy.
So I think it's just a natural progression with this field,
and as the capabilities advance, I think,
privacy will come into place. >> Well,
we're catching up a little bit, aren't we, yeah.
So let's go the next question because we only have another
four minutes or so and there are many questions, so
[INAUDIBLE]. >> Hi, good afternoon,
thank you for sharing all your knowledge,
the one piece that I took away was, jump in. Don't be afraid,
like, be comfortable with uncomfortable. So what, so
my question is more around when you jump in, and
I just did that three months ago.
I'm also at Intel [LAUGH] on the marketing side. And
companies can be in a whole paradigm on how they adopt
a data science. So what's your advice on to keep going and
not give up if, and convince your organization to,
to be under, to support data science,
as you keep, >> Not give up,
what's your advice? >> Yes, never give up,
you have to keep going. >> I mean there are days where
you might want to just give it all up,
but never. You got to, I mean, I want to work for
another 20 years, health permitting. Right, cuz you,
there's so much to do, and I really feel sometimes,
I recently tell my husband, I wish I was 30 years younger or
40. It's just glorious days for data science right now,
and it's just so fantastic in all the opportunities that
you guys have. >> So jump in, keep going,
that's the big thing. >> I will say that something
that's very important is to get the right support for it,
I mean, because it's very easy.
Especially because, there are still not a lot of women in
data science. You can end up being the only woman on
your team, right, I mean, so basically, like, make sure you
have the right support system in your family, among your
friends. They understand what you're doing and
what you're going through and, and tap on that
resource as much as possible. >> Well, Eleanor,
now what about you? >> That's great, no,
I 100% agree with, you know, figuring out what will help
you to feel supported. And, you know,
everyone has a different way to kinda get
through adversities, so, find what works for you.
And if it's, you know,
listening to talks like this or talking to your friends,
I think just figure out what you need to do to keep going.
Because the reality is that all of us have had times when
we've failed or times when things didn't go well.
And, and those are dark times,
but, you know, you get through them. And, again,
that's when you really know you're learning. So
I, I tend to think that like if I'm like really stressed or
don't know what to do, that's kind of a signal to me.
That like okay,
I'm gonna learn something here, so keep that in mind.
And I think that also can give you hope to get through it.
That maybe you don't get it right the first time, but
you'll learn and you'll do it better the second time.
>> All right, well,
some last thoughts, I mean, the time just flies,
it's amazing. I don't know Darren what you're doing
with this clock, but I think the time in this auditorium
goes faster than outside. >> So
the clock is being maybe manipulated or-
>> I'm not sure,
I'll have to talk to Darren here. But before we leave,
I would like to give you the opportunity to give one quick
message to give to the audience, your last thought.
Well, not your very last thought, but your last thought
in this career panel. >> [LAUGH]
>> I'm happy to start,
I think that my final thought is to be curious and
look at the data. If you see something that looks funny,
go and investigate. Some of the most fun I've ever had
are looking into something that seemed a little off and
figuring it out. And, I think that's something that once you
develop the habit to do that, it will be really powerful for
you. >> I have said all the things
I need to say except one more thing. I would say,
don't oversell data science, because that's
only going to come and hurt us in the end. So be realistic,
be true to yourself, and as I said, work hard,
never give up. And that's my sort of last piece of advice.
>> [LAUGH]
>> Great, Jennifer?
>> I would say like one thing
that really helped me been like here is that I was
actually a very soft, soft-spoken kind of person.
And then, at some point,
I realized I have to be more assertive, right.
And so, to the point that now sometimes like,
when you like somebody, guys. I tried to explain somebody,
like this is not the right way to do it, you're pushing your
ideas on me, right. So if somebody tells you that,
it's okay, it's like, it's what we're trying to do,
right. We're trying to change the way
data science is perceived and
women in profession are being perceived.
So, I mean, build some ways to build your own self esteem,
and be ready to be assertive and speak up your mind
whenever that is needed. >> Wonderful,
Ziya? >> And
data science is fastly growing, actually,
it's the most promising area. I think we're definitely
moving or already working in the right space. But to me,
I think the advice I have is, collaboration is key.
Because today, a lot of innovations are happening with
academia, with industry top leaders, and
also with open source community.
So collaboration with those partners to make sure that you
stay on top of the curve.
And you are able to leverage the latest technology trend to
solve real business problems. >> Well, thank you all so very
much for joining this panel. >> Thank you.
>> [APPLAUSE]
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