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Kirill: This is episode number 53 with Aspiring Data Scientist
Virginia Mendonca.
(background music plays)
Welcome to the SuperDataScience podcast. My name is Kirill
Eremenko, data science coach and lifestyle entrepreneur.
And each week we bring you inspiring people and ideas to
help you build your successful career in data science.
Thanks for being here today and now let’s make the complex
simple.
(background music plays)
Hello, hello, hello. Hope you're having a great week, a very
exciting and interesting week, and today we've got an
inspiring guest. Virginia is an aspiring data scientist. So
Virginia came from a background in databases and now
she's decided to transition her career into data science. And
the reason for that is because she has a greater vision for
her future. She has a vision of doing good for the world. And
she can see that it will be much easier to do that by knowing
data science. How cool is that.
In this podcast, we talked about quite a few things. We
talked about how Virginia goes about understanding what
skills she needs to learn and how to break into the space of
data science, about understanding when it's appropriate to
take a step back in your career and take a step sideways
without regretting all the effort that you've put into your
career, but instead leveraging your career to build a new
career in a different space, such as data science.
We also talked about how goals and dreams are different
and how it's important to have a dream and be passionate
about it and always work towards it and how to line yourself
up for success in your dream. How not to just jump at it,
but actually understand the right career path that you need
to select for yourself based on what type of person you are in
order to line yourself up for success in your dream, in your
vision for your future.
So a very interesting and inspiring podcast, especially if you
are in the outskirts of just starting into your career, of just
starting out into the space of data science, or if you already
have a career but you want to transition into the space of
data science. And that's what we're going to be talking about
today. And without further ado, I bring to you Virginia
Mendonca, an aspiring data scientist.
(background music plays)
Welcome everybody to the SuperDataScience podcast. Today
we’ve got a very special guest, Virginia Mendonca calling
from Slovakia. How are you going Virginia today?
Virginia: All fine here. Thank you for inviting me.
Kirill: Oh it's great to have you. So tell us a bit about yourself. You
are a student. You're studying data science. You're trying to
get into this space. Is that correct?
Virginia: Yes. I'm very thrilled to understand data science. It's been a
while I am into this study, basically online courses which
I've been finding one interesting then another. And that's it.
I'm studying and finally I found a chance to go deeper in it.
Kirill: Ok, awesome. And you're listening to the podcast at the
same time. Because the way we met, you actually sent me a
long comment on how I could do the podcast better. Or
basically some tips, which I really appreciated about how I
communicate with guests and so on. So how are you finding
the podcast?
Virginia: It's because I find it an amazing and brilliant idea from you
that I thought maybe I should tell him some tips that if I can
help him with something and if he can see this, maybe he
will think about it. Because it would be nice to improve. Just
improve. When you see something that's really great, you
think about improving it. When you don't believe in it, you
don't even go there. And you wouldn't bother with this. But
this for me is a really great way to reach out to all the data
scientists and future data scientists, to empower their vision
and that's what moves us.
Kirill: Yes, yes. And thank you, that was a great thing to hear.
Really appreciated the comments and definitely something I
take on board. And then I was a bit straightforward, and I
said ok, cool, looked at your LinkedIn and I was so surprised
by your background. I couldn't not invite you onto the show.
And then it was very interesting because when we just
started the call, just now for people listening, we were just
on the call before the podcast, and I honestly thought that
Virginia is from Bratislava and that she's travelled to all
these different places. But it turns out it's a completely
different story and it's even crazier than I thought. So tell us
a bit about your background. Where are you from and how
has your life taken you to all these different countries, all
these different places in the world?
Virginia: Well, first of all I would like to note that now I understand
why the recruiters come to me already thinking that I am
from Slovakia. It's not clear. But now I think I will make it
clear on LinkedIn. Anyway, my life has been challenging,
mostly challenged by myself. When I was in Brazil, I was a
DBA, working with SQL Server.
Kirill: Sorry, just to start, you're actually from Brazil? Because we
still haven't gotten that clear.
Virginia: Yes. I am Brazilian. Totally Brazilian!
Kirill: Ok.
Virginia: And I started working with databases. It was my first
experience with the IT area. Since then, I've spent 7 years
working with databases, mostly SQL Server. I was working
in Brazil, and I felt like I missed some international business
knowledge. I wanted to improve my business knowledge
because I felt it was needed to have my own business. So
thinking much about it, I thought about studying it outside
the country to have a bolder idea of it. So I went to Ireland. I
was in Dublin for a year studying and after that, I really
liked the style here in the European Union and I decided to
stay. And then I applied for jobs in all of Europe. And the
first one to give me an opportunity was AT&T here in
Slovakia. So I thought, why not? And there I go.
Kirill: Amazing, amazing. So you challenged yourself to get out of
your comfort zone. Big change from Brazil to Dublin. Even
just temperature-wise, it's crazy!
Virginia: Yes. Mainly from Brazil to Ireland. Because in Slovakia, right
now you can feel it's warm outside. It's 18 degrees. And it
can even reach much more, like 30 degrees! In Ireland, it's
super windy and cold. And that's what I faced the entire year
I was there. And a few sunny times.
Kirill: So Slovakia is a bit better than that?
Virginia: Yeah, it is.
Kirill: Fantastic. There’s so many places we can start, but let’s
probably start with your decision to stop your career in
database administration and move to data science and data
analytics. What triggered that change? For somebody
listening to this podcast, if they have a career of 7 years in a
certain area, they might be attached to it, they might be
thinking, “I’m already very good at this. If I decide to go to
data science or data analytics, I will have to start from zero. I
will lose all of the years of effort that I’ve put into my career
and the progression in my career that I’ve had.” So how did
you go about thinking about that challenge? It’s such a big
step to move to data science.
Virginia: First of all, it’s about the perspective you take over the
situation. If you see this like an opportunity to increase your
skills, your real skills, it’s never lost. Even the database
knowledge, managing database knowledge that I’ve got, it
will help me to understand, to have a broader view over the
data science itself because we need to know also where it is
stored and also how it is managed, it’s the backstage of this
focus. So I think every knowledge adds up, mainly when
we’re talking about data science. Because you can find
people from all the other professions.
I’ve been reading and listening and watching videos and
people that have other backgrounds, they can even reach
data science and be successful in it. So I’ve been very keen
to go into this path which offers such broad ways to go. Like,
I can use it like a tool in whatever I would like to study, and
this really fits on me because I love having different options
and freedom to choose and go wherever I want to go. So this
is perfect because I can apply it in whatever I feel that is
interesting.
By the way, statistics is something that I had before. When I
started my university life, the first course I did was statistics
and I thought, “No! When am I going to use that?” At that
time, I was a teenager and now I understand. Maybe now I
can finally use and understand that knowledge.
Kirill: Okay. Wow, that’s a great overview. I also had a course in
statistics at university. At the time, it was so vague in terms
of the applications, how would you ever apply this unless
you go into actuarial sciences or something very specific, but
now I actually rediscovered statistics for myself just recently
when I was creating the statistics course. It’s so interesting.
There’s so many different applications you can do in
analytics and data science.
Yeah, thank you so much for that overview. It’s great that
you have this perspective, that you’re not missing out.
You’re actually learning something new. You’re progressing
in a bit of a different direction, but at the same time you’re
leveraging your skills where you can. I think Einstein said
that if you’re not learning, you’re dying, something like that.
So, if you feel that you’re not learning in your career, then
why stay there, right?
Virginia: Exactly. Yeah. I believe knowledge totally adds up. You’re
never losing. You’re always increasing your capacities or
perspective, actually. The more experience you have, the
more knowledge you have, the more different perspectives
you can have over happenings in your life. That’s all it’s
about.
Kirill: Yeah, totally. So what was the first step that you took? Was
it that degree in Dublin? Was that your first step into data
science, into analytics?
Virginia: Actually, that was into business. I had no idea I would end
up in data science. Actually, I am into this passion for
having my own business and never thought that I could use
a tool like data science to help me out. Lately I had this
insight, like three months ago, I’ve been reconsidering to
change my career because I felt very interested in the
course. Mainly there’s a “Data Science A-Z” course that you
offer on Udemy. That was amazing. I was really enjoying it
and most of the time at work thinking of it, nothing else. So I
thought, “Oh, my God. This is interesting. I could have many
insights and if I had my own business it would leverage my
career, my business itself to the proper insights.” I was
thinking about it and I was studying this and during three
months I was really keen for doing this. And inside AT&T I
found this possibility. There were open positions and I just
applied for it.
Kirill: Okay. That’s really cool, that you just applied and it
happened that there were positions that you were interested
in at AT&T at the time.
Virginia: Yeah, it was a series of coincidences. I was feeling super
interested in understanding it deeper and also there were
these positions available at AT&T and I saw them. Usually I
never see and this one really caught my attention, obviously,
because this interests me. So I told them I have to decide if I
will go for it, is this what I find more meaningful for me and
to start this career in this area.
Kirill: Okay. That’s really cool. Tell us a bit more about AT&T. I’ve
encountered AT&T in America. They do mobile phones, they
sell mobile services. Is that the same company that you’re
working for? Do they do the same thing in Europe?
Virginia: Yes, it’s exactly the same company, but here they don’t do
the same. Here we are managing the services or — in this
case, I was into managing the database, the systems that
are actually in there, in the U.S.A. So I was basically taking
care of services in U.S.A., not in here.
Kirill: Oh, okay. So it’s like an outsourced operation from the U.S.
in Europe.
Virginia: Exactly.
Kirill: Interesting. So what do you do currently at AT&T? I see on
your LinkedIn it says ‘data integrity asset analyst’. What
does a data integrity asset analyst do?
Virginia: Well, the moment I changed to this position, it is inside the
asset life-cycle management of AT&T. It takes care of the
assets, all the information of the AT&T assets. So we have to
gather this, we have to collect all the data from software and
hardware altogether from different database. Basically, as
far as I can figure out, they’re being gathered by software
that is called Asset Manager. Through these, we collect and
analyse the data from AT&T systems and then we can
compare and understand the overall data integrity.
Kirill: Okay. Very interesting. It sounds like you’re very involved in
that first, initial part of the data science lifecycle where
you’re kind of data preparation, data collection, data
cleaning maybe. Not necessarily the full suite but that’s kind
of your main focus. Is that correct?
Virginia: Exactly. I see it at the beginning. I see they are cleaning the
data. I can start to imagine all the things to do and I feel
really excited to apply what I’m learning and also to use the
tools like Tableau which we learn in your course.
Kirill: Okay. So does AT&T have Tableau or Power BI?
Virginia: AT&T has a partnership with Microsoft and we obviously
have access to Power BI. But before I was hired, I was
talking to the manager and I was really interested in having
this experience with Power BI and the work, but she said
we’re still not going to work with this. So I cannot wait for
the time to bring it there somehow, find a way to make it
more available.
Kirill: Okay. Wow, that’s very interesting. Sometimes it happens in
life, I’m sure a lot of our listeners have the same situation
where they don’t have access to the tools that they want to
learn. In fact, I had the same situation. I was working at a
company in the industry and I didn’t have access to R,
Tableau, even SQL. So that was very challenging—
Virginia: Frustrating?
Kirill: Frustrating, exactly. I only had Excel. So I had to talk to
managers and I had to ask them to actually bring those tools
in. I had to make business cases why those tools should be
here, why they’re important and so on. But in the meantime,
what do you do? When you don’t have access to the tools
that you want to learn at work, what do you do about
progressing your learning anyway?
Virginia: That’s exactly what I’ve done. I have asked for access to the
ITO service which takes care of this are at AT&T. I have done
these requests and also stated why I need it, and they’re still
like—well, I’ve got to install Tableau because I’ve got to prove
that it’s related, but the others are still on the go. By now,
it’s what interests me, and also Excel. It’s helpful. So that’s
it. That’s what I can use at work.
Kirill: Okay, interesting. And in terms of your role at AT&T, when
you started that role—how many years ago was that again?
Virginia: The role of DBA?
Kirill: No, at AT&T.
Virginia: It was 2015. January 2015.
Kirill: Okay. So you’ve been in that role for some time. I just want
to understand for the benefit of our listeners, how do you
think ahead in terms of a career at a company? Did you just
jump at a role because you liked it or did you take
something into consideration and you thought, “Okay, this
role will take me to this type of data science work, which I
want to do.” Did that happen in real life? What’s the
situation there?
Virginia: Well, before taking this role, I was really studying the
options I had. AT&T has an amazing software tool, let’s say
web service, that offers us the possibility to see the career
progression. So, in the area of data science, graphics are
also showing how many are getting into this job, how many
are getting out and even what you have to know. And a
portal to get this knowledge is also there. So a specific
subject into the data science area and how to get there and
the progression also, where you end up.
Like in this case, data analyst in the asset life-cycle
management, I would be in the quality management. It
would be the end of my career in this area in AT&T. (Laughs)
So AT&T gives this—let’s call it Career Intelligence and
iCareer too. They are two websites from AT&T that give us a
background. Like we can have a clear idea where do we go if
we choose this role. That’s what I was doing. I was really
concerned where I would end up if I choose this. This was
from the choice that I had, the most interesting, the most
similar to what I would like to have, at least start an
experience hands-on with data.
Kirill: Okay. That’s very interesting. And what would your
recommendation be to listeners of the podcast who are
considering a career in data science? How would you advise
them to think about their career? What things should they
take into consideration? Because making the first move in
your career is a big thing. Like applying for a job, getting the
job, and agreeing to a job is a huge step. So what would you
recommend to them to consider when making these
decisions?
Virginia: First of all, I think this should be based on knowing yourself,
what you like doing the most, so that you won’t regret your
decision later on. Because you’re totally sure about who you
are, your values, and what you like most. Once you are
aware of that, you can make a clear decision. Not because
this is the big fashion of the moment and the area that will
earn you lots of money if you go successfully, but because
you really find it meaningful in your life, you really find this
is a tool that you can go totally into without regret. So if
you’re feeling that, I totally support you on choosing, on
deciding. If not, I support you in studying exactly where you
want to go, what you feel most attracted to before taking this
decision.
Kirill: Okay. That’s very good advice because I agree with you that
there’s so many areas of data science that a person could go
into. There are so many different tools that you can study,
so many different types of data science, so many different
applications, methodologies and so on. It’s probably
impossible to learn everything and be very good at
everything and have a career in everything. You’re going to
have to choose inevitably. So I totally second that opinion.
You have to understand what you like, what’s the best thing,
what’s the best fit for you personally regardless of what the
hype is about. If everybody is talking about machine
learning, but you don’t like Python programming or R
programming, maybe you should be doing something else.
Virginia: For example, you can see people are excited by Java
programming. Since I entered IT, it was like the boom, all my
colleagues were going for that, but I said, “No. I don’t feel
like programming. I admire if you’re doing it with all of your
passion, but if not, it doesn’t make any sense.” It’s 8 hours –
if for example you’re working at a company, 8 hours of your
life daily for frustrating tasks. I think we should really
consider the paths we take based on what we most like or
who we think we are and who we really are.
Kirill: Exactly. I agree with that. For all students listening to this
out there, I often get asked the question, “Which course
should I start with?” You know, Kirill, you have like 20
courses on data science. Which should I take? Where should
I go?” And the thing is, the answer for everybody is different.
It depends on what you feel like, what is the best thing for
you. Don’t get distracted by the fact that just now we
released the course on deep learning or we released a course
on something else. And that’s like the hype of the situation.
You really have to be honest with yourself what is the best
thing for you. If you like visualization, if you like Tableau,
just do Tableau, do visualization and get really good at it.
This field is booming so fast that there’s going to be job
opportunities pretty much in any space of data science,
wherever you decide to go.
Virginia: Yeah, I would consider to look at yourself, what you feel
more interested. Like, when you experience curiosity you are
trying out. What made you to want to try that out? It’s an
interest. How far does interest go? How many times you’ve
been into this? How much are you really interested? So
that’s what you like doing the most. That’s how I figured it
out.
Kirill: I was about to ask, what did you figure out for yourself?
What is your most interesting thing in data science?
Virginia: Well, first of all, it’s business. My idea is finally having
business, mostly in the area of social development, like
social organizations. And I would really like to create my
own business in this area or at least make part of this. I
cannot find any other tool other than data science to help
me out with these to gather the best of it, like to see the
trends and instead of just making profits—like, you can take
the tools and make profit with your personal business, but
you can also do it with a good social purpose. We are here to
add up in the society, and why not? That’s super powerful to
boost my intention to have my own business in this area.
Kirill: Interesting. Let’s talk a bit more about that. I noticed in your
LinkedIn you actually—(Laughs) It sounds like I’m stalking
you on your LinkedIn, that’s all I’ve been talking about! I’ve
just got it open right now in front of me. You have been
involved in quite a few volunteer opportunities. For instance,
you were involved in the Africa Centre. Can you tell us a bit
more about that just briefly so that we can get a feel for
what kind of person you are? And then we’ll talk more about
business opportunities in data science.
Virginia: Yeah. In Ireland I had to study and I could work also for a
limited time. I decided to go for things I really was interested
in. Like, I was already into business and I was very close to
the idea of how could I influence society, which are the
NGOs here that are doing something about society? So the
ones that I have noted that I am more keen to help are about
the refugees and also these excluded societies like black,
poor — just excluded societies, people that are just left
without much options. So I went for that. I went to these
institutions to check what they were doing and to see how I
could be helpful for them. And that’s basically it.
Africa Centre is gathering the youths, the teenagers mostly
to bring the consciousness about the service provided,
cultural services for them to keep their roots alive, to feel
that they are not alone, that they have the support. It’s
amazing job, what they do. I was directly involved with the
director of the company and I had nice ideas with him to
improve the business. I was mostly managing the projects
that he had, like altogether. We had other people from his
staff to promote this information to the African community
inside Ireland.
Kirill: Okay. That’s very interesting. Thanks for that breakdown.
Very noble thing to do, to participate in volunteer
opportunities. Now moving on to your idea of using data
science in business for good, can you tell us a bit more
about that? I know before the podcast you mentioned a
company called DataKind and how they use data for social
good. Maybe let’s start there. What is DataKind, what do you
know about them, and how do they use data science for
good?
Virginia: Well, first of all, I know Jake Porway, he is the founder and
executive director of DataKind. I actually found him because
I was checking which companies working into data science
are interested in working with social good. So I found this
DataKind company which does work that I really admire.
Basically, they’re helping to bring together data scientists to
promote high impact in social organizations, to better
collect, to better analyse, to better visualize the data in the
service of humanity to decrease poverty, to decrease violence
and all issues that we have in the society.
Kirill: Very interesting. It sounds like a very passionate person and
somebody that I would love probably to invite to the podcast
as well.
Virginia: Yeah, that would be very interesting, to have this person.
And there is also a woman — because I’m kind of a feminist
— there is also a woman in this area. I don’t know if you
heard about her, but—
Kirill: What’s her name?
Virginia: Claudia Perlich. She’s the Chief Scientist in Dstillery. That is
also a company. In her case, she is providing market
intelligence. She has a brilliant mind and is a really great
data scientist in the area. I get inspired with her and her
data mining knowledge. She has some presentations on
YouTube that inspire me, too. So, basically, both of them
together is my view of a good data scientist.
Kirill: Okay, that’s pretty interesting. Very inspiring people and
very inspiring initiative, sounds like it. But what about your
idea? What are you thinking of using data science in your
business to help use analytics for good?
Virginia: Well, using the prediction models like Claudia does to figure
out the trends into social issues basically regarding
refugees, which is a critical issue here in the European
Union. So I’m really interested in gathering the data to offer
them support, shelter specifically, mainly care of children.
But I am aware that I need to be in touch with many, many
other companies and NGOs that do this same service to have
a broader experience, not just what I had in Ireland but
much more contact. And in this reality that I ended up in
Slovakia, I’m a bit far from it, but it is still my biggest desire.
We are talking about the end line, my biggest desire to use
and develop great knowledge about data science on helping
me to furthermore invest on this business idea.
Kirill: Okay, that’s pretty cool. So you’re still searching? Still
developing your network and contacts to start a business?
Virginia: Yes.
Kirill: If you don’t mind me asking, why are you so confident about
starting a business? There’s so many different areas you
could apply data science on. Why are you so set on starting
a business and what helps you keep that passion alive?
Virginia: Well, that’s a great question. Honestly, I was always troubled
in my life with the idea of starting up my own business. I say
‘troubled’ because the society’s conventional ways don’t
usually convey to supporting our creativity. Most people are
training to be part of an already existing working idea.
Therefore, pursuing my own way to do my own business, it’s
something that would really give me satisfaction. I don’t
have to follow the rules of any other person that created any
other idea.
I have these values that brought me to a specific idea and I
would like to put it in practice. Of course, with the best
skills that I have, so I would build it strongly. And why not?
We are usually raised to go with the flow and just work there
to have money and keep ourselves fine, but I think life is
much more than that. We are here and we are very valuable
so we can add with our own ideas and create our own ways.
Kirill: That’s very inspiring. Thank you for sharing that. What
would you say to somebody listening to this podcast who
maybe thinks similar to you? I think many people — maybe
everybody even – has some sort of passion, some sort of
ideas of how the world could be better. What would you say
to those people about how they can get excited about that?
And what kind of first steps can they take in the direction of
starting their own business, in the direction of becoming
more independent in their thinking and not just performing
the work that they’re doing at their jobs – even though they
might like it – but also creating that opportunity for
themselves to implement these ideas into something
material and make them come to life?
Virginia: I think it’s all about passion. When you have been honest
with yourself in choosing that career that you really like, this
might be a natural result because you are so excited in this
area that you start to have your own ideas. And why not put
them in practice? So I would advise to who is listening and
is into this same perspective of mine, the same objective, I
would advise you to just pursue your passion. Just because
it’s comfortable, don’t let it be like that. Go for the challenge.
Your passions are going to hold you tight there and will
make you succeed if you are really into it. So don’t be afraid
and really try if you find this is your passion. That’s what I
would advise.
Kirill: Fantastic. Thank you for sharing that, it’s great. And you
mentioned challenges. What is the biggest challenge that
you’re facing right now in terms of making all of this come to
life?
Virginia: The biggest challenge right now is how to apply the
knowledge into my work because I want to make it real and I
know that data science is about real problems, it’s about
reality. We can solve it. So I want to have this knowledge I’m
gathering, apply it into my work – this is the starting point.
So I will be more and more familiar with it and then have a
broader experience. And this is the biggest challenge
because the biggest experience would be what will guide me
to my dream basically.
Kirill: Okay, gotcha. That’s very interesting. Yeah, it’s very
interesting how you think about it. You want to first start by
getting the experience and then move onto your dream. It’s
interesting you mentioned that because I was actually
thinking about that myself just recently. What I do in my
business and what I’m working with, a lot of that would have
not been possible if I have not spent enough time at
university, at my career in Deloitte where I was doing
consulting, at my career in the industry where I was
building a data science team. And at the time for me it felt
like maybe it was just exciting projects.
At Deloitte there was lots and lots of exciting, fun things, I
was flying all over Australia doing really cool data science
projects and then maybe it was just overcoming challenges.
Also there was a sense of obligation that you have to have a
job to pay the bills and so on and so forth. And slowly my
dream was growing and growing and growing into something
bigger with time.
But now looking back, I see that if I had not done the job at
the industry, if I had not done the career at Deloitte, I
wouldn’t have had the right experience, expertise and
background in order to do what I’m doing now. And I’m
really thankful to my past self for spending that time, those
three years or more if you include university and other jobs,
in doing what I did because it helped me build all this vision
and especially the expertise to do the things that I want to
do now to actually make my dream come true. So that’s
some great advice, I think.
Virginia: Yeah, you are building yourself. You are finding out what
you like most and now you are receiving the product of that.
I believe the result is this.
Kirill: Yes. So, for those listening out there, I think it’s a great tip
that if you have a dream of doing something, then probably
when you’re young, it’s harder to understand the pathway to
your dream. You just go with the flow or you do what you’re
passionate about. But even doing what you’re passionate
about is a great compass in life. It helps you because it will
guide you to your dream anyway. But if you’re already a bit
experienced, you know what life is all about – it’s kind of
hard to know, but you know a thing or two – it’s a bit easier
to sit down and think, “Okay, what is my dream? And how
do I build my path towards my dream?”
This is interesting. I was saving this up for a Five Minute
Friday episode, but I might say it here. There’s a difference
between dreams and goals. This was told to me by my
mentor who was taught this by his mentor, so it’s trickled
down quite a long way. Dreams are things that you want to
accomplish in life full stop. It’s just something that you
would want to accomplish one day, whereas goals are
dreams which have a timeline. So once you say, “I want to
do this by this date, that’s a goal.” But if you don’t have a
date and it’s just something you want to do, something that
you’re passionate about, something that you’re working
towards, it’s a dream.
Virginia: Makes sense.
Kirill: Yeah, thank you. So it’s important to understand not just
what your goal is for the next year, next three years, next
five years, but to understand what your dream is. Because
the thing with dreams and goals is if you get them wrong, if
you set your dreams as goals, if you say, “I want to start a
business in three years,” then what will happen is you will
get frustrated if you’re not coming closer to your goal, if two
years pass and you’re not even one step closer. But if it’s a
dream, then you will still be doing what you’re passionate
about and your passion will guide you towards your dream
and one day it will become a goal.
Virginia: I believe that it is very productive if you set your dreams as a
background to your goals. They are essential for you to
reach your goal. Without them, maybe you’ll never realize.
And some people just like to dream about it and not really
put in practice. So the goal will make you realize what you
really want. Sometimes in a point of your life you have such
a specific dream and through your goals you maybe are
reaching there but on the way, you find something else.
So in this pursuit of your dreams, you are finding who you
are, what you really want. Dreams can change too, and we
should put them in the background of our goals and bear in
mind that it can change. And we should try, many times, to
really figure out how to see it and think about it, but we
have to have hands on to understand where we really want
to go, not just what appears to be.
Kirill: Fantastic. I love it. I love the concept of having your dream
as the background for your goals and for things you’re
doing. That’s a great idea, so that everything you’re doing
and learning and working on is with that in mind so you
always think, “How is that in the direction of my dream and
how is that going to help my dream?” That’s a great idea, I
think. Okay, I have just a couple of quick questions towards
wrapping up. In your learning of data science, what is a
recent win that you had that you can share with us,
something that you’re proud of that you’ve accomplished,
some breakthrough that you had? Is there something that
you can share with us?
Virginia: Learning and—
Kirill: Or maybe in your role. Either/or.
Virginia: The fact itself that I got to change my career to this data
analyst position is just a start, it’s just a beginning, it’s
really something I’m really happy about, really excited. But I
have less than one month in this position so what I could
have done, it’s hard to say it is solid by now. I would like to
apply what I’m learning, I would like to visualize the data
and the trends, where it is going. There are many things that
I would like to do, but I need to have more experience to
understand the process behind it and finally to see the
trends and the insights about it. It’s too early to say
something.
Kirill: No, that’s a good answer. You just got into this new position.
And I just realized that you were a tech specialist in
database administration at AT&T and it just only happened
a month ago. I thought it was actually two and a half years
ago, but it just happened a month ago that you moved into
data science. Very exciting. Congratulations on that.
Virginia: Thank you. This achievement itself is really exciting for me.
Kirill: And it happened inside AT&T, right?
Virginia: Yes.
Kirill: I’ve heard a lot about that and I’ve seen that happen. To
keep talented people, companies open up opportunities to
move around within the company. Do you have any advice
on that? How would somebody who is in the company that
they love, how would they approach the question of, “I
actually want to change my role to be more focused on data
science?”
Virginia: First of all, your manager should be keen with this idea. I
always had great communication with my manager and in
AT&T, we have to go directly to the manager to talk about
this idea of changing position. And also, when we go to apply
on the website to [indecipherable 49:44] change, it
automatically goes through the permission of the manager.
So if you have a good understanding with your manager,
and mostly if your manager is aware of your skills, where
they are going to, what are your passions, and is willing to
support you.
In AT&T it happens like that. Your manager is going to help
you. Fortunately, in my case, that’s what happened. My
manager said he would support me in this passion that he
has seen, that he acknowledged. I have more focus on data
than anything else, so he advised me and told me not to be
afraid. So, first of all, analyse how is the situation. If your
manager knows you well enough to believe that you can
really change to this new position. If not, I think you should
work on this relationship, if the politics are similar to AT&T.
I don’t how it works in other companies, but that’s how it
works for me. Fortunately I saw this position available so I
could apply for it and all went fine.
Kirill: Okay. And how would you compare an interview – I’m
assuming that was an interview – an interview when you’re
already inside the company versus an interview when you’re
joining a company fresh? Is it different?
Virginia: It’s much more comfortable. You feel much better then. You
are at home, you’re just changing rooms. You know
everybody. I have a good relationship with my former
colleagues and the actual colleagues are also amazing so
they welcomed me and everything was — I know how the
process works in AT&T, so it’s easier to go for it and it’s
easier to change inside, I believe.
Logically, they feel more interested in their own people than
in the external ones because they already know the process
so we will skip the part of teaching how it works, going
through trainings. Because when I was at AT&T for the first
three months, I was in too many trainings to understand the
process and to understand how it works there. So when they
skip this, it’s an advantage. I felt like this.
Kirill: Okay, that’s great. Thanks a lot for sharing it. I think that
can be useful inspiration to a lot of our listeners who might
be considering other roles, like, being in data science but
then thinking, “Oh, this whole interview process is
challenging.” But maybe there are roles for data science in
your organization already that you could consider for
yourself. And that brings us up to the end. We’re running
out of time already. Thank you so much, Virginia, for coming
on the podcast. I just have one last question for you. What is
a book that you can recommend to our listeners that could
help them become better data scientists?
Virginia: Well, I would actually recommend an audio book that I’ve
lately been into and has been giving me insights about the
main tool that you have to have before everything – that is
statistics. “Naked Statistics” is the name of the audio book
you can find on audible by Charles Wheelan.
Kirill: Okay, great. Thank you. What did you like about the book?
Virginia: It is inspiring. It’s telling in practical terms how statistics is
not boring at all when you find meaningful data through it.
It’s just simplifying the terms. Most people are taught about
how bad statistics is because of the hard terms to
understand. But when you find meaningful data behind it,
you will just think the opposite. You’ll just find it amazing.
That’s what I’m feeling. I’m still listening and this has been
inspiring me lately, altogether with your courses which are
really, really good.
Kirill: Thank you. So, “Naked Statistics” – guys, check out that
audio book. Virginia, how can our listeners follow you or
contact you if they’d like to know more about how your
career progresses and what you achieve and maybe one day
how you use data for good?
Virginia: I have a website in Jimdo. I don’t know if you are aware, but
it’s a platform to create websites. I have created my
homepage there so you can know more about me there
altogether with LinkedIn. At Jimdo, it’s just
virginiammg.jimdo.com.
Kirill: Okay. virginiammg.jimdo.com. I will definitely include the
links in the show notes, and also LinkedIn. Once again,
thank you so much for coming on the show and sharing
your experiences and most importantly, your experiences in
learning and vision for your future and how you think about
your vision. I think it’s very inspirational, what you’ve
shared.
Virginia: I thank you very much for bringing me here and sharing this
with your listeners. Thank you very much, Kirill.
Kirill: Thank you. Have a great day. Bye.
Virginia: You too. Bye-bye.
Kirill: So there you have it. I hope you enjoyed today’s podcast. We
definitely talked a lot about career-related things and all
these different aspects to selecting your career, selecting
how you want to progress towards your future,
understanding what you’re good at and what you’re actually
passionate about.
My favourite part of this episode was when we spoke about
how you line yourself up for success in the future. You
might have a dream, but maybe it’s not the best idea to just
jump at your dream right away. Instead, get some
experience, create a name for yourself or get the right skills
and tools in place in order to be successful in your dream.
Virginia definitely showed a great example of that where she
has a dream of doing good for the world through data, but
she also understands that she needs to develop those data
science skills first before she can jump into that and that’s
exactly what she’s doing.
I think that was a very inspiring message that was delivered
there and definitely something for you to consider in your
career. Where do you want to end up? Where do you want
your career, your life to take you? And what skills or
expertise or experience do you think you need to line up in
order to be successful at that? So something to consider
and, as always, you can find the show notes for this episode
at www.superdatascience.com/53. There you can also get
the links to Virginia’s LinkedIn – don’t forget to connect with
her there – and her website. And on that note, I wish you a
pleasant rest of the week and I look forward to seeing you
next time. Until then, happy analyzing.
