Kirill: This is episode number 75 with Data Visualisation Consultant at The Information Lab, Rachel Phang.
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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 we’ve got a success story from Rachel Phang. Rachel is and was a student at SuperDataScience on our courses, and a while back, in – I think it was September last year, I spoke with Rachel for the first time, and back then she was still searching for her career. She knew that analytics has value, she was looking to learning SQL, but she didn’t know where everything was going and I was really hopeful that she would find a way, she would find a path for herself.
And today when we spoke, it was so, so exciting because it’s only been 10 months or so since then, and Rachel has completely transformed. She’s got a very exciting job, she’s a consultant in the space of data visualisation at The Information Lab, which some of you may have already heard about. She’s using Tableau and Alteryx to drive significant value to different organisations, she’s learned so much, and she’s super excited about the future, so it was a great, great catch up and I can’t wait to share all of this with you.
And of course, in this podcast, you’ll learn about Rachel’s journey. So, she will tell you about how she went from where she was some time ago, and from her previous career in finance, to where she is now. You will also learn about Tableau and Alteryx. Of course, we will go into more detail in depth on what The Information Lab is, how they are set up, what the Data School at The Information Lab is, and Rachel will give you some tips on how you can apply for the Data School as well.
And finally, I find that this podcast is a great example, a very inspirational example, of somebody who really wanted something, and then they just put in their effort and time into it, and eventually they got it. And they got a great career. And now the world is their oyster. So, I’m very excited to share this with you. And without further ado, I bring to you Rachel Phang, Data Visualisation Consultant at The Information Lab.
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Hello and welcome everybody to the SuperDataScience podcast. Today I’ve got a very dear guest. Today we’ve got Rachel Phang on the podcast. Rachel is a Data Visualisation Consultant at The Information Lab. Rachel, welcome to the podcast. Super happy to have you here.
Rachel: Hi Kirill, thanks for having me.
Kirill: Rachel, we met quite a long time ago. I think it was end of last year, I think September or October, and you were already a student on one of my courses, is that correct?
Rachel: That’s right, yes, I was doing the Data Science A-Z.
Kirill: Yes, that’s correct. And so it was very interesting to chat to you then. You told me about how you were looking for opportunities in the space of finance, as far as I remember, and then you came to understand that data science is a very important element in today’s job marketplace. Is that correct? Tell us a bit more about what you were going through back then.
Rachel: Oh, yeah, absolutely. There seemed to be a lot of opportunities that required data skills and data knowledge, especially in the field of credit risk, so there were a lot of opportunities for people with SQL skills in particular. That was what really piqued my interest. I wasn’t actually working on that more techie side, I was working more as a fund manager, so looking at data quite intensively and taking action on what I could see in the data rather than really manipulating it too much. I was really the end user.
Kirill: Okay. And tell us a bit more about your background. So, you were already working in the space of finance, is that correct?
Rachel: That’s right. I was in fund management.
Kirill: Okay. And how did you come to that career path?
Rachel: I guess it was something that I planned for since university. I did a degree in Economics, Econometrics and Finance and then I did a Masters in Finance and Management, so I was really looking at the space of finance quite keenly. I managed to land a role with Fitch Ratings, which is a credit analysis company in London, and things were going quite well until the financial crisis hit in 2008 and things have been a bit rocky since then.
Kirill: Okay. And that’s what led you, at the end of 2016, to be looking for new opportunities. Is that right?
Rachel: That’s right. I was looking — the senior people that I have spoken to within finance, and particularly the asset management industry, it didn’t seem since 2008 to be such a hot area, such a growth area. There was a lot of regulation and jobs seemed to be definitely not growing as much as they used to be, so I was really looking to move into an industry and a space where there was growth. Obviously I’m not looking to retire just yet, obviously I’m thinking of a long-term career plan. I’m looking at something where there will be opportunities in the future, where I can grow and build up my skills.
Kirill: Gotcha. And that’s how data science came up?
Rachel: That’s right, yes. There’s a lot of hype from the research that I could see about data science, and obviously there’s a lot of hype continuing now. And I thought, given my background in analysis and statistics and having been the end user of all this data, that it would be quite a natural, sort of sideways movement to move into data.
Kirill: Okay, makes sense. Now let’s fast forward 8 months—or it’s almost been something about 10 months up until now. Where are you at now? How has your career and search for your career changed and where has it brought you?
Rachel: I’ve actually managed to have a role as a data consultant, and the way I got into this role was quite interesting. After having taken the Data Science A-Z course, which was what really opened my eyes to a lot of different software packages and different tools out there in data science, one of which was Tableau, I was living in Birmingham at the time with my parents and there was a Tableau event that Tableau held in Birmingham—
Kirill: Wow, what a coincidence!
Rachel: Yeah, it worked out well. So I managed to go along for this event and I spoke to the presenters during the networking session and one of them said, “If you’re not currently working, you should actually go check out this Data School down in London. It could be a fantastic opportunity.” What they do is they offer paid training, and after training you for four months in Tableau (you’re learning from Zen Masters). And this was from a Zen Master himself, so I knew it wasn’t a scam or something even though it sounded a bit like a scam. It was almost too good to be true. But they offer paid training, it’s a fixed term contract, and then you go out and work with clients and you help them with their data. You use Tableau every day, you get really good at it, and at the end of that the world is at your feet. So, I thought “That sounds like a good idea.”
Kirill: That’s so cool.
Rachel: Yeah, it was really cool.
Kirill: Just to recap, you took the Data Science A-Z course and there you learned about Tableau, which was one of the tools in the course. And then there happened to be a conference or an event run by Tableau in the city where you met a person during a networking session who told you about this data science school, which you thought was a good idea and you went to check it out. What a chain of events that led to where you are! That’s so crazy.
Rachel: Yeah, it all happened within a couple of weeks really and that Zen Master Rob Radburn actually came to present at the Data School, he came down to visit us, and I said, “Hi! Remember me? I was at this event a month ago and you told me to come here and here I am.” And he said, “Yeah, that’s really cool.” So, in about four weeks, everything just happened really quickly, it just took off.
Kirill: That’s so cool. So tell us a bit more about the Data School. First of all, what is this Data School? As far as I know, it’s by The Information Lab. So tell us about The Information Lab and their Data School.
Rachel: Okay. The Information Lab is a Tableau partner. Actually, there’s two technologies that they partner with. One of them is Tableau and the other one is called Alteryx and they offer sales, but also consultancy and support for both of these softwares, and another thing they offer is training. They realized that at the rate that they were looking to grow and the way the industry was growing, that there just wasn’t enough expertise and there weren’t enough consultants to take on the work. They realized that they were in a position to train people up themselves, so that’s what they started doing.
Basically, they recruit eight people every four months to train. The training program runs for four months, and then after four months you go out as a consultant to work with their clients for six months at a time. You do that for three placements, and at the end of the three placements and the training, that’s a two-year fixed term contract that you’re bound to, and after that there’s a variety of options that you can take part in. You can either join The Information Lab as a consultant or you can join one of the clients, or you can do your own thing. It’s quite a new program so we’re only just starting to see where the alumni are going with their lives and their careers. It’s quite exciting.
Kirill: That’s so interesting. And was it hard to get into? You said they take only eight people every four months?
Rachel: That’s right, yeah. The application process is quite interesting. They’re not interested in your resume; they’re not interested in your CV. I don’t think they even look at your LinkedIn. All they want to know is what you can do in Tableau. People are self-taught—again, I just took all your Tableau courses that I could find, you know, Advanced Tableau and Tableau Q&A—and they ask that you build something with a public dataset that they supply to you in Tableau Public. And then once it’s up there, they’ll give you feedback and speak to you, and you need to be able to present it and that is the basis of the application basically.
Kirill: Oh, that’s so cool. And what you learned from the courses was sufficient to do their challenge?
Rachel: Yes. I would say that what I learned from your courses was really helpful. And I’ve since spoken to somebody who is currently in training now, who’s in the current cohort of Data School, who also said that he used your Tableau and Advanced Tableau courses in Udemy to help him prepare for his Data School application. So thanks for getting us in.
Kirill: Thanks for sharing that. It’s so cool and so exciting. I didn’t know that part. That’s so interesting. So, you’ve been in this Data School for how long now?
Rachel: I’ve been working with The Information Lab for six months, so four months has been with the Data School on the training and I’ve been with the client now for two months on my first placement.
Kirill: That’s really cool. Can you share a bit more details about this client? Is that okay, not too confidential?
Rachel: Yeah, that’s fine. They actually provide services to the shipping industry. They’ve been around for a long time, they’re called ISS, and they’re basically shipping agents and they have a lot of data on ports and just things that get shipped around the world. Again, given that they’ve been around for about I think 150 years, obviously we’re not going to go back in time, but they have really established connections so it’s really interesting to look at some of the movements of goods and ships around the world and seeing that side of things that perhaps a lot of people don’t really think about too much on a day-to-day basis. And being able to create data visualizations in Tableau, especially with the mapping abilities of Tableau, that’s quite cool.
Kirill: That’s so interesting. And it’s such a big shift from a career in finance to a career in data visualization where right now at the moment, you’re doing visualization for a shipping company. How does that make you feel?
Rachel: I’m really excited. That was one of my main pushes to move into data. I thought, “Well, I’ve been working in finance for eight years, I’ve been studying finance for four years.” That shows you one view of the world and that does cross industries, but to cross industries in such a much more hands-on way as a data consultant, that is amazing.
Kirill: Yeah, that’s so cool. So how long does this placement last for and do you already know the next client?
Rachel: This is a 6-month placement and I don’t know yet where I’ll be moving onto after this.
Kirill: Okay. I’m sure it’s going to be something very exciting as well. And is it correct that you’re going to have a total of four or five placements during these couple of years?
Rachel: It’s three placements. They’re quite long, they’re for six months each.
Kirill: Okay. And then after that you get to choose if you want to stay as a consultant or join one of the clients or let your career take you wherever?
Rachel: That’s right, yeah.
Kirill: That’s so cool. Okay, that’s very interesting and very exciting for you. I’m definitely very excited about that. Tell us a bit more about what you learned about Tableau. Well, first of all, for those of our listeners who don’t know what Tableau is, maybe let’s start there. What is Tableau in a nutshell?
Rachel: It’s a software that allows you to see and understand your data.
Kirill: Yeah, that’s their mission. I think you just quoted the mission of Tableau. (Laughs)
Rachel: I guess, yeah. But I see that quite clearly. I can’t say it any better than they say it themselves.
Kirill: Okay. And would you recommend to somebody to learn Tableau or what would you say to somebody who is used to visualizing data in Excel?
Rachel: I would say you really take off with Tableau if you’re from an Excel background. Most of us are from an Excel background and suddenly you feel like you can fly with Tableau. I think a lot of the capability of Tableau and a lot of the speed, even just working with the size of data, I think now Excel has the ability to deal with, like, a million rows of data whereas I think—I don’t know how many rows Tableau can deal with, but I’ve definitely worked on datasets that are much, much bigger than a million, or several million, rows of data.
Kirill: Okay. And I personally also totally agree with that. I think Tableau is a tailored tool to visualization and has much better capabilities in visualizing data than Excel. Okay, so Tableau – tell us what you learned about Tableau in those four months. It’s great to take courses on Tableau, but in four months you’ve probably learned so much more. Can you share a few insights from there?
Rachel: Sure. Aside from Tableau, the other software that we use is Alteryx. You often quote this statistic that I’ve re-quoted, that when you’re doing data analysis, the reality of it is that you spend probably 70% of your time doing the ETL – extract, transform, load. Maybe you’re doing it in Excel, maybe you’re doing it in SQL, and it could be quite tedious, it can be quite challenging. And Alteryx is one tool that tries to address that. It’s drag and drop, it works quite well with Tableau, you’re not expected to know much programming. Maybe if you want to do some IF statements or logic Boolean statements, that works, but basically you drag and drop these icons and create a visual workflow where you can transform your data and you configure each tool as you go along and it gives out your data in a format that you can then put into your visualization tool, in this case Tableau, and it’s in the right shape for you to start visualizing. So that’s quite cool.
I’ve mostly used it for data preparation in that way, but it’s also got some really cool capabilities including spatial, so you can look at maps, distances and drive times and those sorts of functionalities. But one thing that I think your listeners would certainly find interesting is that it’s got predictive tools as well, so by ‘predictive’ what I mean—I’m just going to open my predictive toolset now in Alteryx and go through some of the R tools that they have.
Basically it’s built on R and I’ve got an icon for decision tree, I’ve got an icon for linear regression, support vector machines. So you can drag those in, and rather than writing out the R code, you can run your data through them just based on how you’ve configured it within the workflow. So that’s another interesting functionality of Alteryx.
Kirill: Okay. So, you use Alteryx for the ETL process before Tableau and also you can use some algorithms in a drag and drop form. That’s pretty cool. And did you use Alteryx on your placement right now?
Rachel: Yes, I use it hand-in-hand with Tableau. So, as I’m going through Tableau, I think, “Oh, I could really use this in a different format,” or recently what I’ve done is, one client came back and said, “Can we look at this with this other column added in?” And then we can go back into Alteryx and join that back in into the data and then bring it back into Tableau and visualize it again. So that’s quite a good functionality.
Kirill: Okay. I don’t think I’ve used Alteryx before, definitely not in-depth. Is it a tool that you can use without Tableau or do you need Tableau to use Alteryx?
Rachel: No, you can use it completely independently from Tableau.
Kirill: Okay. That’s really interesting. Also, I think you mentioned that you’ve taken the Tableau exam. Is that right?
Rachel: That’s right. So, Tableau has got two levels of exams. It’s got the QA, which is the Qualified Associate exam, and then it’s also got the Certified Professional exam. Certified Professional is quite advanced, actually. There are few people who are very experienced at Tableau at The Information Lab who have taken or are taking that exam, but as part of the Data School, we are all expected to pass the easier exam, which is the Qualified Associate exam. That is a 2-hour long open book exam, so you can google anything which is interesting. It asks you technical questions which are multiple choice, and it also asks you some more theoretical questions about Tableau, and I include Tableau Server. So that’s something that we’re all expected to pass by the end of the first four months. I think one cohort were all expected to do it in their first or second week or something, so that’s interesting.
Kirill: Okay. And those open book exams are usually tricky, right?
Rachel: Very tricky.
Kirill: Yeah. You just don’t have enough time if you get carried away googling things.
Rachel: Yeah. And also the way Tableau words the questions as well. They’re a bit vague sometimes, so that’s something else to watch out for. It’s a pretty interesting experience to go through.
Kirill: So you passed the QA exam?
Rachel: That’s right, yeah.
Kirill: Okay. Congratulations! That’s really cool. So you’re a Qualified Associate of Tableau now?
Rachel: Thank you. Yes, I am.
Kirill: That’s awesome. Okay, so Tableau, Alteryx… And this is very interesting because I’ve personally found this myself as well. There are so many different data science tools out there and data science techniques, methodologies, algorithms, but often if you get good at just a couple of them, that’s enough to solve like 80% of the problems that you come across. Would you say that is the case for you, that with Tableau and Alteryx you feel equipped and confident to take on almost any challenge that comes your way in your current placement?
Rachel: Yeah, definitely. I do think that the clients that we’re working for are also quite aware of the abilities of Tableau and Alteryx, they’ve been using it for some time. Again, it works hand-in-hand to understand the limitations of the software, but also the power of the software, so they will pose these questions that they think can be answered and they know the capabilities of Tableau and Alteryx well enough that they throw some good challenges at us, I would say.
Kirill: Okay, gotcha. I think this is very inspirational for those out there who—a lot of the time, now especially, we hear more and more that machine learning is taking over data science, that you have to know machine learning, it’s very important. And that’s true, machine learning is very important and it’s becoming more and more dominant, but at the same time there are, and always will be, people who are just not comfortable with that level of programming or that level of coding or that level of sophistication of the algorithms, kind of like the technical side behind them that you need to understand and master.
And I think your example is very inspirational to people like that who want to be in data science but don’t necessarily want to do machine learning per se or want to explore other areas of data science. You doing Alteryx and Tableau and feeling confident about taking on data science challenges, it’s a good motivation and a good testament to the fact that if you want to be in data science you can always find a path for yourself, a career for yourself which doesn’t have to conform to what the majority of the data science positions expect you to have or to know or what the majority of the people are doing – for instance, learning machine learning or other things. There’s always a way to structure your own career. What would you say to people out there who want to be in data science, but are a bit thrown off by the claims of machine learning or even more sophisticated things like deep learning or AI? Would you say there’s still room for them to have a very successful and inspiring and exciting career in data science?
Rachel: Oh, absolutely. One of the strengths, I guess, of the Data School is the diversity of people’s backgrounds. I know one person who has a degree in computer science and I think that’s it. I think out of the 48 people that they’ve taken on so far, they took people from really diverse backgrounds. A very interesting use case of data analytics actually is in sports analytics, and there are a few people who have come through Data School who have come up from a sports background, sports analytics backgrounds, I think one person right now who’s in training has a sports trading background.
So, yeah, there’s definitely a lot of room for a lot of different backgrounds in data visualization. And I would say for me one of the challenging aspects was not so much the technical side, but I think my creative skills are quite rusty. So having to design things and think about usability and the end user and how things look and how people who don’t have a lot of time need to look at something and be able to understand what you are trying to tell them and designing for that and just thinking around all these different questions, these were the fresh challenges for me.
Kirill: Interesting. And how did you go about upskilling on those challenges?
Rachel: Practice. At the Data School we got a lot of real client projects that come our way. Before we even get out to the six months of sitting at the client office, we have a shorter term client projects. So, the client comes in on a Monday, gives you a brief, and on Friday you need to deliver an answer to that question.
Again, every week we have presentations. We practice our presentation skills, so that was quite new for some people as well, but by the end of the four months – as one of my classmates said – you just get bored of being anxious every week. Yeah, you just get up there and you just present. They’re not long presentations, but again, it’s just getting used to that experience. Being able to gather requirements from the client on Mondays when they come in and present, making sure you’re asking them the right questions so that you understand what the deliverables are on Friday, being able to communicate well with the client and generally client management, expectations management, basically more of these consultancy soft skills I would say are also a very key part of Data School, of the training that we get.
After doing that for four months, by the time you come out of that and the other side projects that are always being thrown our way, by the end of that, you get a feel for things a bit better. It’s something I still need to practice, definitely, and I’m still trying to look for datasets and think about the best way to communicate insights and just looking at other people’s visualizations as well, looking at things and critiquing them for yourself, just thinking through the process of what you like, what you don’t like and why, what works, what doesn’t work.
Reading other people’s blogs about their own or other people’s visualizations as well – that’s a good thing to look out for. The Head Coach of the Data School, Andy Kriebel, he actually has a community project that he calls “Makeover Monday,” and he runs that every week along with a lady from a company called EXASOL called Eva Murray. And every Sunday afternoon, they publish a dataset to the website and it’s just open to people all over the world and they download it and over the week everybody creates something and they post it on Twitter with the hashtag #MakeoverMonday, and then at the end of the week either Andy or Eva will do an analysis. Again, everyone got the same dataset and everyone’s come up with a completely different take on it: “This is what we like, what we really like, and this is what we want to see more of.” I don’t think they get too much negative feedback because it’s really targeted at encouraging people to participate and it’s quite a good way to learn.
Kirill: So anybody can participate in that?
Rachel: That’s right, yeah. Just go over to the Makeover Monday website, download the data and then make something in Tableau Public.
Kirill: That’s fantastic. I highly encourage all the listeners to check it out. And it’s very interesting how you described this training that you go through in Data School. It sounds like you’ve really got a robust approach to training with real clients coming in and actually talking to them at the start and then presenting at the end, feeling anxious and stuff like that.
That’s a very good training, like training with fire. It’s not just a textbook training, but actual live work training. So that’s really cool. I wish there was more of that. It’s a shame they only take eight people every three or four months, as you mentioned. It would be really cool if a lot more people could have the opportunity to go through a training like that.
Rachel: I would say yes, it is, but I would say there are also ways that listeners can benefit, obviously not from the entire experience of the clients and everything and the presentations, but I think that at least with the Tableau and the community there are other benefits that anyone can gain.
Kirill: Okay, tell us more about that.
Rachel: One of the things, as I mentioned, was Makeover Monday. We participate in Makeover Monday and participate in the community. By the community I mean keep up with Twitter. There are people posting things on Twitter, posting things on Tableau Public. And I think now on Tableau Public, what’s quite cool, they put in a social aspect so you can follow people whose work you like and see what they’re publishing on Tableau Public and reach out to them on Twitter and engage with them.
One thing that’s really cool is that the Tableau conference is obviously quite expensive to actually attend in person, but all the videos are available online for free, so you can just tune in at your own leisure and try and learn something, maybe set aside some time on a weekly basis to watch and implement what you’ve learned from the video.
Andy has a YouTube channel where he publishes Tableau Tip Tuesdays and all sorts of quirky things that you can follow and learn from. And getting involved with your local Tableau User Group—I know a previous podcast guest was running her own Tableau User Group, and that’s actually a thing and Tableau encourages it. Basically everyone in a local area who uses Tableau gets together and talks Tableau and talks about what they’re working on. In London it’s quite big and there are a lot of large corporate clients who have really adopted Tableau and obviously there’s a massive Tableau office here so there’s a huge community. But I think even in places where there isn’t such a huge community, I think just building that up would be quite cool.
Another thing I would say is to follow blogs. Tableau is something that people like to blog about, whether it’s their most recent viz or something new and cool that they learned. Even the Data School has a blog. When I was part of the Data School I also had to blog. Part of our homework was to blog regularly, so you blog about the things you’ve learned, something new you’ve learned today that you didn’t know before or you blog about the experience of being at the data school. That’s just a few different topics that we’re given guidance to on blog posts. So that’s something that you can follow as well.
Kirill: Okay, that’s really cool. You mentioned quite a few things so far. For instance, the community, the blogs, the meetup groups, the Makeover Mondays. That’s really valuable sources. But out of curiosity, what has been the most valuable source for you personally? You’ve undergone such a huge transformation from when we spoke back in September till now and you’ve really gotten your career on track. What has been the biggest supplementary source? Of course, probably the biggest change came through Data School and The Information Lab, but in addition to that, what has been the biggest second source for you in terms of information, inspiration and learning?
Rachel: I would say probably Twitter, following other users on Twitter, watching what people are creating and posting. Again, that kind of ties into Tableau Public as well. I follow loads of people on Tableau Public and a lot of them are quite regular, they post something quite regularly or they participate in Makeover Monday, just looking at how so many people approach the same dataset so differently and learning from that.
Sometimes somebody tweaks something and you just think, “I never thought of doing it like that before.” Or they do something that you didn’t even know Tableau could do and then you reach out to them and say, “Hey, how did you do that? I didn’t know you could do that,” and they might write a blog post about it because a few people have asked them the same question. So that whole interactivity—again, I keep using the word ‘community’ but that’s what it comes back.
Kirill: Okay, that’s really cool, very interesting breakdown of what’s going on in the space of Tableau and especially in The Information Lab and Data School. Yeah, it looks like everything is sorted out. What are you excited most about for your career going forward?
Rachel: What am I excited the most about? The fact that with a background in data visualization and understanding the key concepts, whether that’s in Tableau or maybe next year some other software will be even more amazing, whatever that is, but being able to work across different industries and being able to see the world from different lenses.
I guess I’m quite nosy like that, so I get to poke my nose into “Oh, this is what they do in shipping and this is what they do in a fast-moving consumer goods company and this is how they do things at a supermarket,” really the whole consultancy angle and being able to add value by helping people understand the data they’ve got.
And that’s not only from a commercial aspect. Another community initiative that I feel like I should highlight is a hashtag, I guess, #DataVizforsocialgood, which is a bit of a mouthful, but somebody out there is publishing a dataset of social issues that they feel that they need more attention and they’re calling on a community to help them present this data in a way that really captures people’s attention. Obviously, through engaging with the dataset yourself, you learn about some social issues whether that’s education in India, or women’s rights. I think one of the most recent ones was unemployment in Latin America or U.N. agencies and the work that they do and how they collaborate. So I think there are a lot of social aspects as well. And I think that’s something that’s very exciting about data, that it’s not confined to any one particular use, but you use it for, as the name says, data viz for social good.
Kirill: Okay, that’s very inspiring. And the reason why I ask this question was—for this follow-up question that comes now, compare how you felt back then when we spoke the first time when you were unsure where your career was going to go and what’s going to happen and how you feel now. Can you tell us what the difference is? Because a lot of our listeners are currently in your shoes where you were 8, 9 or 10 months ago. So, tell us what the difference in feeling is?
Rachel: It feels great. I feel much more confident that I know where my career is going, where my profession is going, and I feel like there are so many more opportunities. In a way, compared to working in finance, this is something where I feel also—maybe this is a bit of a millennial throwaway, but I feel like it’s something where I personally feel that I can add value.
Kirill: Okay. And what I wanted to underline here for everybody listening is that this only took less than a year. This transformation for Rachel took, like, 8 or however many months, but it all boils down to you really need to want it. And as long as you send that energy out there, as long as you’re actively looking and working hard on your skills, like Rachel was without a doubt working hard on the courses and learning Tableau and doing practical exercises, and then these opportunities will come up.
Going back to that chain of events that led you to Data School, that’s a big random chain of coincidences that happened, but it happened because you wanted it so much. You really wanted to have that certainty, and you wanted to experience this amazing feeling of a happy career and knowing where your life is going, and that’s what you have now.
To all those listening out there, it’s not that hard, it doesn’t have to take 10 years or 3 years to become a data scientist. A lot of recruiters would say or a lot of job descriptions say that you need 5 years of experience in data science to get this job or whatever. It really doesn’t. It’s not the case. As you can see with Rachel’s example, in less than a year you could be in the same position as Rachel and have an amazing, fulfilling and exciting career in the field that you love and the field that you’re pursuing. So that’s a very inspirational example. Thank you so much for sharing, Rachel.
Rachel: That’s all right. And to add to that, again, definitely keep an ear out for events, keep an ear out for your network. You’ve been speaking about conferences lately and I know you’ve been attending various conferences, and I would say definitely approach the speaker, approach other people, network to people, get to know who else is working in the field and build those connections. I would say it is that important people know who you are, what you’re able to do, and that they think of you when they think “I need somebody who knows how to do this,” whether it’s data visualization or programming in R or anything in data science so they know, “Oh, I know somebody who is very keen and I know that they’ve been doing these online courses and they’ve been improving themselves in these ways and they’ve been to these conferences. Maybe I should give them a call.”
Kirill: Yeah, definitely expand your network and make these connections for sure. And I’ve got an interesting question for you which I like to ask guests. I’d like to get your opinion on this because you are just starting out into the space of data science and you still are so excited and inspired about everything that’s going on. It’s a great time to be and you can take on any challenge. I’m sure you have a feeling that you want to take on as many challenges as you can. From what you’ve seen so far in data science and what you’ve learned about data science, where do you think this whole field of data science is going and what are you personally going to be preparing to anticipate what’s coming in the future?
Rachel: I suppose I would keep up, make sure I keep up with what’s going on in the field. I definitely find your podcast really useful in that respect. I’m able to keep up with the latest developments. Again, it’s such a fast-moving field. Recently we’ve been talking about AI and how that’s changing really quickly. I think that’s really interesting. And just not getting left behind.
Kirill: Yeah, I totally agree. Well, thank you so much for coming on the show, Rachel. I’m very excited that you shared all your experiences. How can our listeners contact you or follow you or find you if they would like to see where your career takes you from here?
Rachel: Sure. I’ve mentioned Twitter a few times, so they can follow me on Twitter, my handle is @data_rachel. And I would encourage anybody to join Tableau Public as well and you can find me on Tableau Public and follow my visualizations, my dashboards that I’m creating for public consumption, obviously, not the client ones. Obviously I’m on LinkedIn as well. Go ahead and follow me on LinkedIn and I’ll keep posting updates to my feed there as well.
Kirill: That’s really cool. And you mentioned that during your Data School, you wrote some blog posts. Can people find them on the Data School website?
Rachel: Yeah, sure. You go to the Data School website which is—well, we’ll provide link in the show notes because it’s quite long. Or even if you just google the Data School or The Information Lab, there’ll be a link to the Data School and on that page there will be a link to the blog and that’s got the blog of all the Data School participants right from the beginning and I guess there’s a page where you can find individual people and you can click on me and all my blog posts will show up.
Kirill: Okay, fantastic. Thank you so much for that. And one final question: What is your one favourite book that you can recommend to our listeners to help them become better data scientists?
Rachel: I’ve mentioned that some of the interesting aspects for me or parts that I haven’t really anticipated in data visualization have been more about things like thinking about usability and user experience. And there’s actually a very broad library on data visualization available and some well-known names in the field are people like Alberto Cairo, Stephen Few, and David McCandless. I’d not heard of any of them before getting into data viz, so I’ve got quite a long reading list actually, but there are two books that I would say I would recommend and they’re not even by any of those authors!
The first one is called “Storytelling with Data” by Cole Nussbaumer Knaflic. I’m just looking at it now, it’s on my desk, and this book is actually given out to prospective applicants at the Data School interview, so that’s quite an interesting guide, I would say. The subtitle of the book is “A Data Visualization Guide for Business Professionals” and it just talks about best practices in data visualization, why a bar chart is clearer than a pie chart, what are some common mistakes that people make, and why they’re mistakes basically, why they might not make your chart or your visualization as clear as it could be. So I think that’s a really interesting read.
And the other book is actually a very new book, it only just came out this year, it has three authors and one of the authors was a speaker at that event that I attended in Birmingham, and he was there speaking about his new book, or forthcoming book (that’s now out!) It called “The Big Book of Dashboards” and there are three very experienced authors just looking at a bunch of dashboards, talking about what they liked and what they didn’t like. Obviously, they’ve all got different opinions and I think it’s a good book to get your ideas flowing when you have a dataset and you’re thinking about a creative way to present your insights.
Kirill: That’s really cool. So, “Storytelling with Data” and “The Big Book of Dashboards.” That second one especially sounds like a very interesting idea so I would like to chat that out, too, and encourage those listening to check out those books. Thank you so much, Rachel, for coming on the show, I’m really excited about everything you shared. And good luck with wherever your career takes you from here.
Rachel: Thank you, Kirill.
Kirill: So there you have it. That was Rachel Phang from The Information Lab. I hope you enjoyed this podcast. I found it very inspiring to witness this transformation that Rachel underwent. My personal favourite was the fact that Rachel has targeted her career in a specific way. She’s not worried about learning the things that she isn’t that excited about. She’s just doing the things she’s excited about, she’s just learning Tableau, Alteryx, she’s just applying those things.
And it’s a great testament, too, that data science is so broad, that you can find a niche for yourself and you can decide what you want to do whether you want to do Tableau or SQL or machine learning or data storytelling or use other tools. There are so many tools you can do. Because I noticed that there are lots of people out there who fear that machine learning is very important, as we discussed it in this podcast, but they’re excited about something else. Well, in that case just do what you’re excited about. You don’t have to always just follow the hype. You can always find, even in this space of data science, you can always find something that you are personally excited about, passionate about. And as long as you do it really well, like Rachel, you will find opportunities for yourself.
So I think that was a very inspiring part of the podcast and of course, needless to say, the rest of the podcast was filled with lots of inspiration. And make sure to follow Rachel’s advice and head on over to The Information Lab website, check out their blog, learn a bit more about Data School and perhaps participate in the Makeover Monday, check the Tableau Public website, and sign up for a data science conference. That could be very helpful. As you could see, one event led to a whole chain of coincidences in Rachel’s life and that brought her to where she is now which is very, very exciting.
So we’re very happy for Rachel and hopefully she’ll have an amazing career and lots of success in the coming months and years, and hopefully you will too. And on that note, as always, you can get the show notes for this episode at www.www.superdatascience.com/75. And thank you so much for being here, I look forward to seeing you next time. Until then, happy analyzing.
