Kirill: This is episode number 19, with Business Analyst Ot Ratsaphong.
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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 and welcome to the SuperDataScience podcast. Today I’ve got a very interesting guest joining me for the show. Ot Ratsaphong is a Business Analyst in the Australian Department of Veteran Affairs. And what is interesting about Ot is that he has been working in the space of data science in the Australian government for many, many years. And he’s changed different departments, changed different roles, but throughout his career, he’s some how mostly stayed with the government departments performing business analytics roles.
So in this podcast, Ot will share some insights on how the government uses data and data science to perform and to deliver the best services to its customers, which are the citizens of a country. I personally learned quite a lot about how the government operates and what career opportunities exist in the government, and I think you will too.
And also in this podcast, we discuss something that we’ve touched on previously in episode 11 with Garth Zoller, how right brain people go about data science. And in this podcast, you will actually learn a huge advantage that right brain people have when it comes to data science and thinking about data science problems.
So I can’t wait for you to check all of that out in today’s show. And without further ado, I bring to you Ot, Business Analyst at the Department of Veteran Affairs.
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Hello everybody, and welcome to the SuperDataScience podcast. Today I’ve got a very interesting guest with me here on the show, Ot Rataphong calling in from Canberra, Australia. How are you today, Ot?
Ot: I’m great, thanks Kirill!
Kirill: Thank you very much for coming on the show and how is the weather there in Canberra right now?
Ot: At the moment, very hot and muggy. And thank you for having me on the podcast.
Kirill: Oh, it’s my pleasure. All right yeah, so it’s great to have you. And we actually had a quick chat with Ot before the podcast and Ot’s really interesting, he’s got an interesting background in art and we had a bit of a chat in French and practiced that a little bit. And yeah, so we’ve got quite a lot to talk about. And right away I want to mention a quick disclaimer. So Ot is involved in government work, he’s worked in the Tax Office of Australia, and the Immigration Office, and he currently works in the Department of Veteran Affairs.
And therefore, throughout this podcast, there will be a lot of moments where due to security level clearances, Ot cannot mention some things and go into a lot of detail. So you’ll have to forgive us for these omissions. But at the same time, we’re going to hopefully have a wonderful chat about how government approaches data science and thinks about data science, and then some of Ot’s background, and maybe a couple of other things that Ot has been involved in and has been using data science for.
So are you ready for this Ot?
Ot: Oh yeah!
Kirill: All right. Ok. Let’s get started. So first of all, very interesting is where you currently work. Could you tell our listeners a bit more about your current government facility that you work in?
Ot: Ok, currently I’m working at the Department of Veterans Affairs. What I do, I’m working as a Business Analyst and I’m involved in upgrading one of their systems, which is involved with looking after the psychological Veterans and Family Counselling Services. When I got that position recently, I found it very interesting because I wanted to get into that data science and one of my interests was health, mental health. So I was looking forward to seeing how I could get myself involved in that field.
But at the moment, I’m not currently looking at any client data because there’s a lot of privacy issues that the Department outsources and then studies in relation to mental health and psychology to elite university studies.
Kirill: Interesting. So you’re helping them upgrade a system. So you’ve been through a couple of government departments throughout your career. What has driven this choice? Why have you always chosen to stay inside the government sector rather than go into the industry or into consulting maybe? What attracts you in working for the government?
Ot: Well I started my IT career when I — actually, it’s a funny story. After I graduated from art school, I was looking for a job and a friend from university, I met up with him, and he was working at the Tax Office. And he said to me, “Ah, you know, you should come and join the Tax Office, because they’re looking for graduates, and then they can send you to study further if you want to.” And that was an attraction to me. And this friend, he studied Botany. He did [inaudible] in Botany, but he was studying Law. And so he did a complete shift. I thought, “Ok, Tax Office.” [inaudible] And I thought it was a good opportunity to get a job with the government, and then they pay for a free education. So that was my attraction.
Kirill: Yeah, ok. And why did you stay?
Ot: Well, I wanted to get into IT. And at that time, there was a shortage of IT people, and they were prepared to train. And they said, look, if you come to Canberra, we’ll train you. So that was the attraction, and that’s how I ended up in Canberra. There was a lot of on-the-job training, I could go to university and they’d pay my university fees. And so that was the attraction. And so I moved my family and all that, and we just ended up staying here.
Kirill: Ok, all right.
Ot: Things like school, and this and that. Yeah.
Kirill: Yeah. And in Canberra, just for those of our listeners who might not be totally familiar with the Australian geography, it’s a common misconception that Sydney is the capital of Australia. Sydney is actually just the biggest city, and probably the financial capital. But actually the capital of Australia is Canberra. And so from your story, I’m gathering that there’s a lot of government departments located in Canberra; it’s quite easy to find a government job in that city. Is that correct?
Ot: Yeah. So most of the work initially was government work. But now, Canberra has grown and is changing. So there’s a 50-50 split. There’s a lot of work that’s outsourced now, and so there’s consultants and oil, and there’s lots of construction and commercial, and lots of other work involved in Canberra. There’s still a lot of work in IT, there’s a lot of work. It’s mainly government work.
Kirill: Yeah, that’s quite a big split, 50:50. Even if it’s quite approximate, that’s a lot of government jobs. Okay, but obviously, even though at the time it was probably easy or more common to get a government job in Canberra, is there something that specifically attracts you about government work? Is it some benefits or is it the approach, the culture in the workplace? Is there anything that you can pinpoint?
Ot: In government work there’s so much work to do that you can never get bored. (laughs) It’s like working on a big farm. There’s lots of work to do and there are lots of projects. And from an IT perspective — I guess, listening to your podcast, when you worked in Deloitte’s, you were saying that there is lots of work, you know, a variety of work. And in the same way, in the government work, there’s so much variety as well. So you could move from one project to another depending on whatever you’re interested in. I’ve worked in my early days in distributed computing, so I got to travel a lot. I visited a lot of Australia because I had to go and set up computers and set up systems for different government departments.
I moved to Defence and worked there and I got to set up lots of systems all around all of the bases around Australia. I did a lot of travel, so it’s very nice.
Kirill: Okay. One thing that we’re definitely not going to talk about on this podcast is the systems you set up for the Department of Defence. That will get this podcast shut down. (Laughs) So we will stick to the Department of Veteran Affairs and just your overall experience with government. My question, not that I wonder about it every single day, but whenever it comes to government and data science, the question that I normally have is, when you’re a company and you’re out there competing with other companies in your same space, you’re forced to use data science.
If you think about it, there’s two companies – A and B – they do exactly the same thing and they have the same products and services, but one of them uses data science to its advantage in operations and marketing and so on, and the other one doesn’t. Of course, company A, the one that uses data science will overcome company B and have this huge competitive edge.
On the other hand, government doesn’t really compete with anybody. Government creates the rules. Government has to exist. You cannot outcompete government because it just has to be there. So they don’t have this pressing need to be on the cutting edge of data science, have the latest technology, implement the latest systems and so on. And yet here you are telling us that you are helping the government implement systems to improve their data analytics capabilities.
And I’ve also heard that Australia’s Bureau of Meteorology bought a supercomputer for their predictions, and so on. So there are signals indicating that the government is investing into data science. It does recognize the value. So having you on the podcast here is very valuable because you can provide your impression, without going into any additional details that you cannot disclose, but just your impression of does the government use data science, does the government think of data science as this new technology or new area or field that it needs to implement?
Ot: Yeah, okay. I think what came to mind when you were talking about this is there’s a government department that is charged with ensuring that all government departments provide online access. And there’s an on-going work in all departments to migrate a lot of their systems to provide online access to government services. And even though the government departments don’t have anyone to compete with, there is this overarching government policy, I guess, to do more with less. (Laughs)
Kirill: OK.
Ot: Yeah, there is a lot of work being done to make the most out of taxpayers’ money. Governments are always looking to be efficient. In terms of data science, they’re looking at—I better not talk about the other departments that I’ve worked with before, but there are two things: how to make government services more accessible, easier and, for example, in Veterans’ Affairs, many of the veterans are aging, they’re retired and they want to travel. You know, like what they call the “grey nomads”. They don’t want to come into the office to make a claim so we provide online access to them. They could be anywhere. So they can just log in through their laptops or their iPhones, iPads or the mobile devices and just to provide any inquiries they want, they can just do it online. That’s a big saving for the department and it’s a really good service for the clients, for the veterans.
Kirill: Okay. I see what you’re saying. So basically it’s not the competition that drives governments to use and implement data science and new systems and so on, but it’s actually the consumers, it’s actually the expectations that people now have because all the other companies that we deal with, whether it’s banks or shops and so on, they have these online systems, and we’re used to, as consumers, to be able to access and make claims online. And therefore, the government needs to keep up with our expectations and therefore it provides a great service and also it significantly reduces its own operational cost. Is that a good summary of what we just talked about?
Ot: That’s right.
Kirill: Okay.
Ot: I think Australia is in the forefront in the world in terms of government providing online services. Even in America, a lot of government services are very disjointed because of the government structure. Even in England as well, a lot of people — I heard people being in long queues just to pay their bills, to get government access and all that. Things might be changing now, but the government works with a lot of corporations, joint venture type things to develop services and they can then export these services to other countries as well.
Kirill: That’s very interesting. Yeah, I will kind of agree and disagree on the online stuff. Like, I’ve personally had experiences in Australia where I had to go and I couldn’t do something online, like make a health claim or something. I had to actually physically go. But hopefully, that will improve with time. It’s not everywhere. You can submit your tax online and things like that, so that’s good. Compared to other countries, I don’t know about the experience, because I’m not a U.S. citizen and I can’t comment on that, but I’ve seen a couple of websites that some of the U.S. departments have for their customers. And the new website that the Australian government has rolled out, which is mygov.com.au, that is like miles ahead of everybody else. I really like the versatility and usefulness of that website. So I will agree with you on that point.
Ot: I did work on myGov at some point but, you know, in terms of making it easy for business to do business, I think the tax office has done a really good job on that.
Kirill: Yeah, I agree with you on that. That’s also true. Okay, so that’s been a good kind of summary of government and how they use data science. One thing I was curious about, and probably a lot of our listeners will have the same question, is very often in government, we come across bureaucracy. And a lot of people have this negative impression, or there’s a negative connotation, around this whole concept and the word “bureaucracy” has this whole negative connotation to it, that whenever you need something done, or even inside the government, the government wants to change something, and going through a change, there’s a lot of paperwork that it has to go through. So being somebody who works in the government, and obviously you see this from a different lens. What would your comment be on that? Like, why does it exist, and is it really necessary? Is there a justification to all of that lengthy paperwork and processing that sometimes has to be put in place?
Ot: Yeah. Whenever the government needs to implement a new system or undertake a new project, a submission has to be made for funding through the department. Department then makes a submission through Parliament to get funding. So as the public servants could work in the government, we need to be really aware that we need to report how we’re spending the money. Because if we are not spending it properly and it gets out that the proper checks and balances haven’t been done, then it’s very bad publicity for the company. So we have to be really careful. We’re trying to make business balance so we’re trying to get work done, but also trying to make sure that at the end of the project, that we can say that we’ve done it properly, that we haven’t done anything wrong. Because everything needs to be documented and archived, and there’s a 99-year time chain for all documentation. At some point in time, you can go to the government archives and do any research you want. You could submit freedom of information and find out how things have been done.
Kirill: Yeah, that’s interesting.
Ot: Everything is public, so we’re making sure we look after public money really. It’s not our money, it’s public money.
Kirill: And it’s not just money. It’s also the data, right? The government deals with all this sensitive information from the public.
Ot: Yeah. The data is even more important. I can say that when I was working in the tax office, there was what we call an “air gap” between outside systems and internal systems just to make sure that there is no possibility of having [inaudible 20:38] lots of privacy issues.
Kirill: Yeah. I can imagine that. Because this is a careers podcast, I would like to ask you a question about—for those of our listeners who are looking to start a career, or maybe make a move in their career into data science specifically in government-related work, what would you say the number one thing is that they should look out for? What would you say they should be prepared for, or research or get themselves ready for going into this step in their career of working for the government in the space of data science?
Ot: As I mentioned before, I started my career as a developer, and my main focus – a lot of my main focus was in data transfers, being able to analyse data and understanding data. You need to be good at data analysis. That’s your starting point, and then obviously with data science, how to manipulate data. I was talking to a couple of colleagues early on who are doing data science, and a lot of the work is data collection. You know, as we’ve heard before, in data science, data collection, data manipulation, cleaning data, a lot of work is searching for data. Because there is a lot of data, but it’s sort of in all the different government departments. And I was thinking about the talk today, I think the big thing that I think will be the main thing for data scientists going forward is unsupervised learning, having that ability to get understanding of how unsupervised learning works and being able to set up a system that can collect data, categorize it and then analyse it, and then go forward. They’re the skills that you need.
In Canberra, we have a couple of data science meet-ups that I’m in. Different people are doing different things. A lot of work will be done in text analysis. There’s a lot of legislation. Different departments do different things. For example, in health, we have a lot of health-related data: how government departments – how the health department — how much spending is done in terms of what medicine, what treatments, what drugs, what illnesses. There’s a heap of data to be analysed to come up with how to efficiently make use of government funding. Government is about how to best make use of government spending, make the most out of the tax dollars that they collect. I did a project in health and we had to analyse data related to spending. When you go to a hospital, there is a treatment package that includes say, surgery, drugs, etc. The government collects all that because that all goes to MediCare. There is work being done there. So for somebody who is interested in government work as a data scientist, you need to be flexible about what work is there and to understand what the government department is interested in doing.
And teamwork is a big thing. That’s a key thing. If you go for an interview, they’re always looking for people who can work with other people. (laughs) And that is not related to data science, but in a data science it’s a multi-discipline, isn’t it? You have to work programmers, data analysts and everything like that. People skills are important as much as your technical skills.
Kirill: All right, thank you. I like that comment that unsupervised learning is going to take a major role in government’s data science work going forward because indeed, when you think about it, there are so many different departments in any government, and they’re all interlinked, and sometimes information needs to be gathered from all different locations. So unsupervised learning, where the algorithm can come with predictions without having some input set of rules, input learning structure, that can be a very valuable addition to government analysis in the space of data.
But what I did also like is your comment about teamwork. Indeed, it’s not directly related to data science, but when you think about it, data scientists can be different. And personally, I like to work in teams. But oftentimes, I find myself secluded, I find myself just working on my own project and driving the project. I’m very passionate about taking one thing and going forward with it. Still, people skills are important. You do need to get information from different places, and from different parts of the organization, so you need to know how to speak with people anyway. But sometimes there are projects where you don’t need to work in a team of data scientists to get to a final result when it’s like a one-man job.
Whereas from your comments it made me think that probably in government, that is a rarer case. Where in government it’s more, because of all of these regulatory frameworks, because of the compliance procedures, because of all the Q and A that has to happen, because of all the sensitive information, like one person cannot have access to everything and cannot do the whole project on their own, I’m kind of thinking that probably is the case in government, that you would find yourself more commonly working in teams of analysts or data scientists, rather than being able to take a project and just spearhead it on your own and get the final result. Is that fair?
Ot: This is a good point because this is what will give you insight into government work. I work with some senior managers who have specialized knowledge of their domain. And then as well as that, we have policy people who have specialized knowledge about the government policy involved. And as well as that, we have then the subject matter experts who are at the coal face, who would meet and talk to people, or provide support to end users. And that’s another level of teamwork.
Then there’s the IT people you have to deal with. If your system needs support, or you need to interface with a different government department, then you need to be able to talk to those people and be on good terms and have an understanding of what kind of data you need to transfer between your department and the other government department. You need to understand security issues and make sure that all those checks and balances are in place. So you need to be a bit of a jack of all trades in terms of being able to talk to people, as well as knowing what you can offer in terms of your skillset.
Kirill: Yeah, that was a great summary. I think, for a lot of our listeners out there, and I hope that for a lot of our listeners out there, this little conversation about government debunks some of those myths that you might have preconceived about working in government, that maybe it’s boring, that a lot of the stuff is just mundane or routine. From what Ot has described, it doesn’t sound like that at all. In fact, on the contrary, when you have to work with lots of different people, you have to constantly be ready to work inside a team. You’re servicing this whole population, so it’s a very responsible type of work.
The only thing you have to be prepared for is some specifics, I guess, that are a bit different from working in the industry. And one of them we outlined here in the podcast, is that you have to really be a team player. Some people are more of a team player, some people are less of a team player. Probably think about that, and maybe there’s some other specifics which we probably won’t go into detail right now, but it gives you a good kind of perception of which way to keep looking to understand if government is a good choice for you as a career.
Ot: Yeah, and if you’re interested in a particular area, you know, there are opportunities. You have to be more proactive about pushing your perspective on what you can do and what you can offer to the government department. It takes a bit of time, I suppose. I mean, I’m personally still new to data science, but I’ve sort of been able to manoeuvre myself to areas which may require data science type work. At this current project, not so much, but you sort of build up your case in terms of what you can do, and eventually people find out what you can do and then you get to the jobs where there’s more data science work.
Kirill: Oh, yeah. That’s a great advantage that you just outlined. That because of the vast size of government, and the amount of different departments—like, you’ve moved through three different departments.
Ot: Oh, lots more.
Kirill: Lots more. There you go. So would you say it was—not easy, but it wasn’t as complex as going on and finding a new job?
Ot: No. No.
Kirill: Exactly. Once you get into government, and if you find that you have a valuable skillset to offer, but it’s not exactly right for the position where you’re currently in, or you start exploring new skills, you’ve grown, you’ve developed, it’s very easy, from what I understand from Ot’s comments, to jump around to find the right place for you. And the government knows — they want to keep talented people inside this massive customer-facing organization which is the government. So there you go, that’s a huge advantage, that you will find the right place for your skills.
Ot: There’s definitely some work in Canberra in the data science area. You just need to be patient and keep networking, so that you have meet-ups, you talk to people, learn from each other. And we’ve got some really good field data scientists I can learn from. I’ve been to a couple of meet-ups and I’m just amazed by the talent that we have in Canberra.
Kirill: Yeah. Okay, thank you very much. That has been a great overview of government, and hopefully, our listeners have picked up quite a lot of stuff from there. Now we’re going to move on to a different topic. I’m going to do something that I haven’t done before. We’ve got two very interesting topics outlined, and I’ll give Ot a chance to choose which one he wants to talk about more because we probably won’t have enough time to cover both. Topic number one is that Ot has a very interesting background. He’s actually got two arts degrees, so let’s just look into LinkedIn here. So you’ve completed a Bachelor of Arts in French, Economics and Environmental Studies, and you’ve also got a Bachelor of Visual Arts in Sculpture, Photography and Painting. So a very right-brained person going into data science, and we can talk about that and your experience. Or, as you mentioned before we started the podcast, you have a passion for trading. So you use data science in trading options and that is also a very interesting field. We can also talk about that. What would you prefer to talk about, Ot?
Ot: They’re both related, so I’ll try and combine them.
Kirill: Okay.
Ot: You want me to start now?
Kirill: Yes, yes, please.
Ot: Okay. So when I was studying art, what happened was the question as an art student was how does the audience, the viewer, look at a painting and the interaction between this piece of canvas with a bit of pigment and painting. And where’s this meaning – how do you get meaning and joy from it, especially when you are talking about abstract art. So that was the question for me at the time. And then computers came along, and then we became interested in user interfaces. And then that was another question, “Okay, so we’re moving from canvas to the screen.” And then computers became intelligent, and they can play chess. So we’re not dealing with inanimate objects anymore. We’re dealing with something that has a semblance of intelligence.
And so that drew me to the computer and artificial intelligence field. So as an art student and also as an economist, I was interested in how society works, especially how the financial—you know, how does society hang together? And that led me to the stock market because that’s where the rubber meets the road. (laughs) At that time, I happened to be working in the tax office and one of my colleagues had done a Masters in artificial intelligence with regards to weather prediction, and he was doing neural networks and all that. That was about in the year 2000. The neural networks are fairly new.
Kirill: Very new back then.
Ot: Very new. And I asked to see his notes, and the notes were – I just couldn’t make sense, head or tail of it. And there wasn’t a lot of work. You know, at that time, what he was trying to pitch was to use neural networks and artificial intelligence to detect fraud.
Kirill: Which is a huge industry right now, yes?
Ot: Yeah, yeah, especially tax fraud. And he was going to submit a sort of publication or project proposal. But I was interested in how to use artificial intelligence in the stock market, the direction of the stock market. But at that time, I knew virtually nothing about the stock market. I had done a couple courses on options trading. I chose options trading because I didn’t have a lot of money, and options trading was the only thing that made sense for someone who didn’t have a lot of money.
Kirill: Why is that?
Ot: Because of the leverage factor. So, I had to drop the artificial intelligence and concentrate on learning the business of trading. And then fast-forward to now, suddenly machine learning is around, and it piqued my interest again. I have been interested in Python and all that, and I started digging around algorithmic trading, and there’s a couple of websites that talk about that. And that’s how I moved from art to artificial intelligence to trading. So they’re related in a way. (Laughs)
Kirill: Very interesting. So even though they’re very different, the way you think about them, they’re actually very related.
Ot: Yeah. They are related. And coming from the right brain perspective, the way I look at the market is to try and find relationships between different things that drive the market. For example, people look at the open high, low close, and all that, but things have moved on from that. Like, you can’t beat that anymore. And now people are looking at satellite pictures and going back to the original source of the business to see how the businesses might be working based on the number of cars in the car park.
Kirill: I haven’t heard that one before. That is amazing.
Ot: You haven’t heard that? Well, there is a company that takes photos from satellites, and there is this group that analyses the number of cars in the car park of Wal-Mart. And then they can work out how well or how not so well Wal-Mart prices will be based on the daily number of cars in the car park. Or something like that!
Kirill: That is on the verge of insider trading.
Ot: It’s like you have to be—you have to really look naturally. You can’t just dig deeper. You have to dig different holes. So, to be competitive we have to look at different relationships that may not seem related, and that’s where the human mind is better. Machines can do that, but you’ve got to point them in certain directions. You know, I do a lot of thinking about this and I’m sure other data scientists do that. But coming from humanities or arts point of view — you were talking about it in your podcast: Which one is better, Python or R, in a different podcast. I started looking at Python, and I knew nothing about R, but my curiosity made me think “I need to look at this.” Having learned R, I think “Gee, this is much better for quick data analysis, just to get the initial picture of things.” And so I’m doing a lot of exploration with R at the moment, learning a lot of R, because I think a lot of the initial data scientists are tending to use R to just get the initial insight, and then if they need to dig deeper, then they might go into Python which is more general purpose language.
Kirill: Yeah. Okay, that’s an interesting comment about R/Python. And I really liked your example of thinking laterally when doing data science. And I agree, machines probably can’t do that – not just yet.
Ot: Not yet.
Kirill: Not yet. With time probably, but right now that is an advantage of humans. It’s funny, because this conversation has for the first time ever, I think, made me think in the reverse way. Usually you think, “Okay, data science. So people with a left brain perspective, with a left brain way of thinking, they have an advantage because they have probably studied something related to that, they’re statistically better at math, they like stats and enjoy that kind of stuff more.” And usually my question to a right brain person is, “How do you catch up on that? What do you do to kind of make up for that—?”
Ot: That lack of a technical—
Kirill: Yeah, exactly. But with you, it made me think the other way. It made me think that somebody with a right brain has an advantage over somebody with a left brain in the sense that you’re better at thinking laterally. You’ve studied arts, you’ve studied photography, sculpture, painting and all these other things that have helped you develop those skills of thinking non-traditionally about problems. And so now my question to you is: How does somebody with a left brain make up for that? How does somebody with a left brain improve themselves to think laterally better?
Ot: My facetious answer would be to work with somebody with a right brain. You know, there’s this thing called STEM, right?
Kirill: Yeah, yeah. Science, Technology, Engineering and Mathematics.
Ot: Yeah. Now there’s this new one called STEAM.
Kirill: STEAM? Arts? They put Arts in it?
Ot: A is the Arts, yeah.
Kirill: Yay! Good. Finally!
Ot: So, people are moving from STEM to STEAM. They’re recognizing the value of arts education. I’m a big proponent of arts education. My first degree was to learn how to learn. Yeah, what I learned from my first degree was how to learn. So I can learn anything if I put my mind to it. And then my second degree was learning how to survive and how to make use of what you’ve got. As an art student, we learned just to make do with found objects. You know, there’s artists who used to make art with found objects. So putting these two together and looking at say, the stock market, I said, “Okay, what do I want to do and what do I need to get to where I want to be?” And using my skill of learning to learn, I just learned what I needed to learn. So using this tool I believe I can apply myself to just about anything. For example, one of my interests that we talked about is vegetarian food. And my latest interest is in brewing and microbes and—
Kirill: Brewing beer?
Ot: Brewing beer, brewing cider.
Kirill: At home? Illegally?
Ot: At home. Yeah, just brewing—
Kirill: Not illegally, right? It’s just for yourself?
Ot: Just for yourself. You’re allowed to brew alcohol in Australia for self-consumption. If you want to sell it, you need a license.
Kirill: Okay, gotcha.
Ot: But as well as brewing alcohol, there’s brewing – not brewing – preserving and using the bacteria, good bacteria, probiotic bacteria. So one of my other areas is researching that kind of thing.
Kirill: No, I completely agree with that. It’s a very interesting notion of learning how to learn. I think my degree largely was about that as well. It wasn’t about studying physics – it was about studying physics. But at the end of the day, right now I don’t use the quantum mechanics or the laser physics that I learned. But it really helped me understand how to learn very difficult concepts when the need arises. That kind of made us steer away a bit from the original question, which was what does a left brain person that is good at mathematics, that has been programming all their life, or is very into programming and statistics and all these other things where people with a left brain world perception are more dominant normally, what does that person do to develop their right brain, to develop their lateral thinking, to develop their creative skills now in their current position? Because now they can’t go back to university and start studying these things. Is there any suggestions that you can make, or any recommendations for people with a left brain to develop these right brain-specific skills? I don’t know, like start doing pottery, get a hobby or—
Ot: Yeah, yeah. I was just thinking – just get out there and enjoy a life a bit more! (Laughs) No, no. I mean, do different things. For example, when the market crashed in 2008, I took time off from a lot of that analytical stuff, which was very much linear, linear analytical stuff. I took time off because the market was just all over the place. I took a small loss – it wasn’t a big loss – and I just needed a break.
And so I took up photography. And I thought “I need to get back to my arts interests,” and then I took up photography and really just didn’t think about that. I wouldn’t advise people to break away, but to incorporate a bit more art into your life. Or even just going to the movies and just appreciating how a movie is made, for example, and just looking at movies more from an intellectual appreciation type of thing. Or read books. A lot of the technical people that I know read a lot of books – for example, science/fantasy books. So I’m not sure if I can advise people that much, because even technical, left brain people have right brain elements within. It’s just searching for them. One of my best friends who introduced me to the tax office, he was a real science geek. He used to read five novels at a time. He introduced me to the idea of reading multiple books at the same time. But then he switched to law. And he became—you know, he made it to the Bar, and he totally switched from science to law. I think people can do it. It’s just – one thing I think helps is to understand society a bit more, if they’re coming from a pure technical background, is to understand history. I was watching the “Cosmos” series recently, you know, the one by Neil deGrasse Tyson. It’s a more recent series. And I learned about Edmond Halley—you know Halley’s Comet? You’ve heard of Halley’s Comet?
Kirill: Yes.
Ot: Yeah, he was reknown for predicting Halley’s Comet 50 years before it arrived. And even after he died, [indecipherable 49:52]. But the thing was what I learned here is understanding history gives you an appreciation of the bigger picture, of how we fit in. The connection of Haley is really interesting, because he was the one who sponsored and published Isaac Newton’s work “Principia Mathematica,” which today is the foundation for space exploration. During that time, Isaac Newton was a nobody, an unknown, he was a recluse, but Halley went and sought him out and asked him to develop what he knew about gravity. And it’s so interesting!
Kirill: Yeah, very interesting.
Ot: History is really interesting, and I think it just gives you a bigger picture about the whole how things fit together.
Kirill: Thank you. That’s a wonderful comment. And yeah, there’s so much to explore: history, movies, documentaries, hobbies, all these different areas in addition to your profession, your data science, your career, you can add into your life that will help you develop a different way of thinking. So thank you very much for that. That’s been a great overview and I think we’ve had a fantastic chat. And for those of our listeners who want to contact you or follow you or somehow follow your career, what would you say the best way to do that is?
Ot: The best way is through LinkedIn. That’s my main professional work channel. In terms of photography, I have a photography Facebook page on which I occasionally publish some of my photos. It’s Ot Ratsaphong Photography on Facebook. Look it up.
Kirill: Yeah, we’ll definitely include the links to these resources in the show notes.
Ot: Yeah, I’m happy to help anyone with any questions relating to photography.
Kirill: Yeah, for sure. And if you have any questions relating to careers, what it’s like working in the government, make sure to hit up Ot and he’ll share what he can with you. Don’t expect that he can share everything because of certain restrictions. And one final question for you, Ot. What is your one favourite book that can help our listeners become better data scientists or just better themselves?
Ot: Better data scientists? I think, as I’m a beginner in data science, my book that I would go to, which is one interesting really—there’s a couple of books that I recommend. One is “The Data Science Handbook” written by Carl Shen, Henry Wang and William Chen. They interviewed a couple of the pioneers in data science. I learned from people [indecipherable 53:05] things together. There’s a bit of history involved as well in it and that’s a good book.
Another one is called “R for Data Science” by Hadley Wickham – Hadley Wickham is a key developer in R – as well as “Advanced R,” another one by Hadley Wickham. Other than that, another really good book I like has to do with habits. I forgot the name—
Kirill: “The Power of Habit”?
Ot: “The Power of Habit,” yeah.
Kirill: By Charles Duhigg.
Ot: Yeah. The thing with that book is developing with habits and then you can let your subconscious brain do the boring bits and then you free your conscious mind to think about other things, more creative things.
Kirill: There’s some fantastic advice in that book. I agree with you. I should read it. I’ve read bits and pieces. I should sit down and read the whole thing.
Ot: It’s not always easy, but you just keep reading a little bit at a time. Don’t read it in one sitting.
Kirill: Yeah, let it sink in. For sure. So I’m just going to recap those: “The Data Science Handbook”, “R for Data Science”, “Advanced R” and “The Power of Habits”. There you go. All of those will be included in the show notes. Thank you very much Ot, for joining me today on the show. We had a fantastic chat and you shared some very valuable insights. It was a great pleasure having you on the show.
Ot: Thank you, Kirill. Thank you for the opportunity to share and I hope I’ve been able to help somebody, especially somebody with a right brain perspective, and helped them, encouraged them, to explore data science.
Kirill: Oh, I’m sure you have. Have a great day, thank you and I’ll talk to you some time soon. Bye.
Ot: Okay, bye.
Kirill: Well, there you have it. I hope you enjoyed today’s podcast and learned a thing or two from Ot who so kindly shared his insights into two things. We talked about two main things today. We talked about government and the way government thinks about data and the way the government, despite it not being under competitive pressure, still seeks to implement the latest technologies and methodologies and ways to use data to serve its customers, the citizens better. And also we talked about how Ot uses his right brain mentality to his advantage when it comes to data science.
Personally, for me the main takeaway was the lateral thinking. That people who study arts and who are more creative have this advantage that they can think laterally about business problems. And that example about taking photos of cars in the car parks of Wal-Mart to understand how the business is doing is a great illustration of that concept. So if you are not really a creative person, you don’t consider yourself to be that creative, well, maybe you’re wrong, maybe there is a part of you that is creative and you just need to find it and explore it further. As Ot suggested, maybe sign up for some hobby or start learning something about history or pick up a new craft that you haven’t looked into before and maybe that will help you explore your hidden talents and that might even help you in your career in data science.
I hope you enjoyed today’s podcast and as always, you can find the show notes, including the episode transcript, at www.www.superdatascience.com/19. There we will also share all of the resources that Ot mentioned, including the books and the link to his profile and Facebook page with photography which you totally should go and check out.
And finally, if I can ask you for one thing at the end of the show, is if you know somebody who is, in your view, more of a right brain person and might be interested, or is interested, in getting into the field of data science, then forward them this podcast to encourage them to get started. And on that note, I look forward to seeing you next time. Until then, happy analysing.
