SDS 053: Career transitioning to Data Science: Important lessons from an aspiring Data Scientist

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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.

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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.

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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.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.