[DataFramed Careers Series #3]: Accelerating Data Careers with Writing

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Hello everyone, this is Adele, Data Science educator and Evangelist at datacamp. Welcome to the third day of our four day Data Frame Careers series where we deep dive into the ins and outs of launching and building a career in data. In yesterday's chat with Nick Singh, we discussed the usefulness of content based versus coding based portfolio projects. While Nick is much more bullish on coding based projects, I strongly believe that content based projects are also a great way to showcase your skills and accelerate your career. There is no better testament than that than Queen Tran. Chances are you've probably seen Kuyin's work if you've ever been on LinkedIn or on Twitter. Kuyin is a developer, advocate at Prefect and a prolific data science writer. She's the author of the book Efficient Python Tricks and Tools for Data Scientists and has written hundreds of blog articles and tutorials on key data science topics and amassing thousands of followers across platforms. What I love about Kuyin's experience is that she has been able to build a platform and a brand by sharing knowledge in her work and has been able to accelerate her data career as a consequence. Throughout today's episode we talk about her process creating content, how to combat imposter syndrome when sharing her work, and what makes content useful advice for aspiring data science writers and much more. If you enjoy this episode, make sure to rate the podcast and leave a review. Also, just a friendly reminder to join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data yourself, there is definitely something for you. Seats are limited and registration is free, so secure your spot today on events.datacamp.com radar. The link is in the description. Now on to today's episode. So I'm excited to discuss with you how writing can be a great tool for aspiring data practitioners to break into the data science space, how you've grown in your data career as a writer and more. I'm sure many of our listeners know you already. You create amazing content covering short form snippets of python code to long form articles on sites like Towards Data Science and even Data Camp. But for those who may not be aware of you, can you please give us a background about yourself and what got you into the data science space?

Yeah, thank you for the great introduction. So I'm majored in statistics, so that's my background. So I try to find something that I can apply my math knowledge on and I didn't want to be a professor so I attend a math conference where the speaker is a data engineer and he was a math PhD and he seems very enthusiastic about his current work and I have been wanting to find a work that I will love. So I just asked him more about it after the talk. Get some guidance from him since I already knew Python at that time and have some math knowledge. So I did a lot of self studying and then later I attended a school like a school in Mexico for one semester in data science. So that kind of get me deeper into data science. But my knowledge really grew after I start writing what I learned.

That's really great and I love how serendipitous your entry into data science is like a lot of data practitioners today actually. So as I said and as you mentioned here, you start writing a lot of content so you create really great content that gets hundreds of thousands of views and reactions across different blogs, on LinkedIn, all over social My first question for you is why should any aspiring practitioner invest in their writing and in creating content?

Yeah, so I would answer this question in two ways, right? If you want to get into data science, that's kind of like the first reason why I write because I want to be a portfolio. So to showcase recruiters. I know what I'm talking about. So that's kind of like the first good reason for those who want to get into get a data science job. But for those who already get a data science job, writing really helps me to reinforce my knowledge sometimes there's some knowledge that you thought you know, but not until that you write down that you saw the gap in your knowledge and you are able to understand it better after writing. Also, I think me and a lot of people who really want to know about some tools, about some concepts, but they haven't got the chance to do more research on it because they just get stuck with their daily work. So writing is another way for you to get out of your daily routine and do research on the topic that you are interested in. So it's like an excuse for you to do it because it's not like you're just hanging around doing nothing. You do research and you rely on it so you see the evidence of your working. So I think it gives you a better feeling for research on something that is not related to your work.

That's really great. So kind of in a summary here, it helps you reinforce your knowledge, it gives you the ability and kind of gives you an excuse to kind of learn new things and helps you build a portfolio of content as well. So how do you think writing has changed your career trajectory and what is the success that you've seen because of writing?

So prior to writing I tried to apply for hundreds of data science jobs and I got like no response. If I got response, it's like, hey, we don't accept you. So when after I start, right, I think I start getting messages from recruiters, from hiring managers for if I want to work with them. Because of that I haven't really applied for any jobs, but I got for internship during my school, during my bachelor study and I also had some side contracts like with data camp and because of that it's very easy for me to find a job after graduation.

That's really great. I love this kind of ability to create momentum within your career profile using writing. So do you think in some sense that writing carries the same value as a traditional coding based portfolio project for anyone trying to break into data science? Or if not, do you think there's any difference? And if so, how so?

Yeah, so I'm mentoring a person, he's like a PhD in math and he tried to get into data science. What I advise him answer your question is, you know, like because what he have, what he has before was only his code in his jupyter notebook, right? And this seed and is hiding on his GitHub and I think this seeded not good because if you just show the recruiter your code without writing it out, your methodologies, what was your question and what was your approach and how you go from the results to the conclusion and what is the next step if you don't tell about those things form of writing the recruiter will not like. I think a lot of recruiters are non technical, so the fact that you just give them your code, you're not going to stand out from the crowd. So by writing out what you have done is a very good way to build your portfolio.

So in some sense, creating content and creating articles around your portfolio projects is the data storytelling packaging on top of the portfolio project that gets you noticed by a recruiter?

Yes.

That's great. So I want to kind of pivot from just talking about the value of why writing. Right. Understand how you work with writing and how you share your knowledge with the data science community. So I'd love to unpack your process for creating content. What are the different types of content that you create and what got you to create them?

Yeah, so I like to experiment with different kind of content. I would break it down into types and categories. I write three different types. I would say, like, I share daily tips, so as you know, a small snippet every day articles, about once per week videos, short videos, also once per week. And for categories, I like to focus on Python for data scientists. So one day I would probably write back Python building function and another day I will talk about, you know, some interesting Python data science repositories that I found on GitHub.

Yeah, so yeah, it's all about kind of providing. I love how you kind of organize your content between, you know, short form daily tips and long form articles. In terms of kind of, you mentioned here that you produce one per week. Do you kind of intentionally force yourself to create at a schedule?

Yes. You know, like I have a routine where in the morning I wake up, write journal, make coffee, and the second thing I do in a day is to write content Right before checking emails or checking notifications or do any other things that can affect my mood. It's like, okay, I need to brush my teeth in the morning, so I need to pause in the morning kind of thing.

Yeah, that's awesome. And I'm excited to discuss with you kind of the psychology of creating content. But before you mentioned here the different types of content that you create, you know, daily tips, articles, what do you think resonates the most with your audience? What was the process by which you found which content was the most resonant?

You know, it's based on a lot of trial and errors because as you might know, I experiment with a lot of Content and types, so it's hard to generalize it, but I think something that I found consistently or some content I think are popular to readers are something that are short or if it's not short, there should be a lot of images and illustrations so that it's straightforward for them to understand. But if I have some article that are short and have a lot of images and illustrations but not useful, then it also not popular. So I think either short or have a lot of images and illustration, but it must be useful in terms of.

Kind of what determines usefulness is how do you determine whether a piece of content is useful or not? While creating content, how do you establish the quality level?

Basically that's a good question. I think usefulness is different for different levels of expertise. So for example, a lot of time I'm very surprised by some posts that I thought is not useful and it gets very popular. The examples of those like, you know, some Python tips that like I already know a long time ago, but I post about it anyway. Right. So I know about it, but a lot of beginners don't know about it and it seems like it get more popular if it is useful for the beginners because the beginners are. You see more data enthusiasts who are the beginners then you see the experts in data science. I mean the percentage wise on linting or medium.

Yeah, I definitely agree with you there. And in terms of creating useful content for experts, how would you adjust kind of your mentality towards the content that you're creating so that it's also useful for experts?

Yeah, so I mentioned before that I use, I switch between one day is Python building functions and one day is like some interesting Python data science repositories. So my intention was Python building tips will be for the beginners and the Python data science repositories are for those who already know some good Python best practices and they want to just kind of use my content as a way to discover some cool tools that they can use for their data science projects.

That's really cool. So you mentioned here, of course, creating content for experts, finding out new GitHub repositories, new packages. I anticipate writing on a lot of these new packages and tools requires a lot of learning and developing your expertise in these tools. How has writing made you a better learner and helped you grow your technical.

Skills by writing about these tools? I kind of understand because I keep my mind open to so many tools so I start to understand the similarities between them and difference between them and what make one tool better than another. And how can this tool fit to a workflow? So when, for example, when I learn a new tool like Prefet, right. It's like an orchestration tool. At that time, I didn't know a lot about orchestration, so I tried to learn more. What orchestration is about and why is it important for a data science project. So by seeing the tool, I kind of go back and learn some concept that is important for that tool. So from there I understand a big picture of the workflow and help me kind of build my own data science template where I, you know, like for every data science project, I will have this stack of tools to use and I understand what problems are out there and how can I solve them using these tools.

That's really great. So in some sense, kind of even like writing about so many of these tools kind of gave you a good idea about which tools work best with each other. And this helps you become more efficient in your actual data science work.

Yeah, exactly.

That's really awesome. What are some of the tools that you've recently wrote about that you've been particularly excited about?

I need to look at my list of articles because I write a lot.

Yeah, you're definitely writing too much.

I'm sure I write about something that is quite popular, but. Oh, I write about PY graphic Tree recently. So the title of the article is to analyze and visualize URLs with network graph. So the motivation for this is I think a lot of data scientists like when in the data there's URLs, then you basically just skip it, right? Because it's kind of hard to deal with URLs. Like how do you get some filters app URLs? So this article I use a combination of Yar y a RL is make it very easy for you to extract some components of a URLs. And then I visualize it with py graphic tree, which create like an interactive network graph of URLs and how the URLs relate to the topic.

That's really great. And we'll definitely link it in the episode show notes as well for people to check it out. And when looking at kind of future projects that you want to take on, how do you prioritize these projects? What makes you want to write about a particular topic?

So as I said, I switch between tools and concepts, beginners and advanced topics. And for each of the categories, I have like a list of things to write about and how I narrow down to one is I will see which one I'm the most excited in and I want to write about it. And it also based on the time constraint. So for example, today if I need to, you know, like I have an appointment at 9am and I have like one hour left to publish on LinkedIn, then I'll choose the one that is very easy to write. If I have more time, then I'll take on something that take me one hour to write. So it really depends on the time and what I'm excited about.

That's really awesome. So this kind of segues really perfectly into kind of the psychology of being a content creator. Kind of to preface this entire kind of set of questions, I think there's a great way to kind of unlock the ability of people to be able to start posting their content and to develop the confidence to share their work is kind of the psychology and potentially battling imposter syndrome. So of course when you started out you didn't necessarily have the same traction as you have today. So how did you approach kind of any feeling of demotivation and kept yourself.

Committed at the beginning? When I first writing, I check my notifications pretty frequently to see like, you know, how many claps do I have, how many views do I have. And like, you know, sometimes my article doing great, right? But some articles, they just don't do well. It made me feel very disappointed. But then later some people message me about the article that was not popular and they say thank you to me. So I feel like for a piece of content it doesn't need to be popular. Right. As a writer you should try to aim to be useful for even if it's only useful for 10 people, it's already good. So what motivates me to keep going is I know that my content would be helpful for even a small subset of people is great. And I also don't check my notifications on medium ever anymore because I don't want to feel demotivated when one of my article not get popular. So I accumulated to 35,000 notifications.

Wow.

And I for LinkedIn I try to check it after I already post the next one. So I check the notification of the previous post after already post about the next one. And when I first started I just make things small and easy to write so I can keep do it consistently to a point where writing long posts are not a problem anymore because become a habit.

That's really awesome. I love how you break this down. And do you still deal with imposter syndrome today when sharing your work?

Yeah, I do. But so that's why I still adopt the same strategies like for notification wise, because otherwise it's hard for me to get going. When I saw some criticism before I.

Post 100% and how do you deal with imposter syndrome when sharing your work?

Yeah, so I got a lot of fear at the beginning when posting on LinkedIn because, you know, I thought people would say like, oh, this is so basic or you know, like you're wrong. Right. But I just push myself through the fear because I know that in a long term this will pay off. So in a way I like I have a vision in my mind that if I do something consistently enough, it will contribute to something bigger. So that pushed me going. And also in a way, if my knowledge is wrong, then is a great opportunity for me to fix it from the feedback.

That's really great. I absolutely love that and I love how can you discuss your relationship with this imposter syndrome today? What are some resources that you have helped you kind of managing the psychology of being a creator.

The book that actually motivates me to post on LinkedIn, like regularly is share your work by Austin Claire. And in the book he said no matter how bad your work is, show the progress of how you get in there. Show people the best stuff and the good stuff so that like it's fine to just share and eventually you're gonna get some reward from that. So yeah, that's why like at the beginning I think my work is not that good. Like I post another good but I post anyway. And another book that I really, really enjoy. And I read it twice already and it's called Mastery by Robert Greene. And it talk about why mastering a subject is important and how to mastering one. So it motivates me to if I'm interested in something, I'm going to go all for it. And if I happen to be interested in something else, I'm going to spend days to do research on it. And when I publish my work, I'll go extra miles to make sure my work is good. So it's kind of like a motivation for me to produce very good work.

That's really awesome. And you mentioned here Cal Newport's deep work as well. He has a great podcast, I think it's called Deep Questions with Cal Newport. Great podcast as well, based on the author. So given your experience, how have you been able, you know, to improve as a writer and what are kind of some of the lessons and resources that you can share here?

I'll give advice based on steps here So I think when you write, try to write about something that you are interested in instead of writing about something that you think people might be interested in. Because when you do that audience will find you enthusiastic and your writing is also better. And a lot of time you want to be surprised by how something that you thought people are not interested in, but they actually are. And another thing is kind of I see a lot of writers, they write about something that already out there. So I read something similar, I'm not gonna read it or it not gonna pop up on the Google search, right? Because there are already a lot of content about it. So just do some short research on what is out there and what about these pixels that you think you can make it better or make it different. So for example, if you're interested in Knowledge graph and you see there's a lot of articles out there about that topic, but they don't cover how to use Knowledge graph for like, for example to visualize URLs, then you write an article about it, right? Also ask yourself who's your audience and what the knowledge that you assume that they have. If they are beginner, if they haven't had any knowledge about a concept, then you want to make sure you say it in a way that people who have no knowledge in that concept, for example knowledge Graph, that they can understand and try to aim for clarity instead of complexity. Because if people don't understand you as you know, like no matter how great the tool is or how great your methodology is, if people don't understand, they're not going to read it and try to go extra miles to make sure the illustrations are good and there's no extra wordings. I know there's a thing called like a rudder block. So when I'm stuck on something, I don't know how to explain this concept well, then I just kind of do something else. And when I go back, I always find a solution for it. And try not to publish your work. After you finish your draft, wait a few hours or even like overnight so that you can look at your draft as like a fresh perspective. And then once you do that, you're going to look at your article with your audience eyes and you will be able to see something that you couldn't see before. And if you have somebody that fits your target audience, then have them read your article and see which part they don't understand.

That's really awesome. I love these set of advices and kind of on improving as a writer, like in terms of your ability, your writing Style your conciseness. Has there been any resource that has helped you in kind of crafting better copy and better written content?

Yeah, I think I cannot narrow down to a resource. I think it just based. I developed this style of writing because a lot of my trials and errors and also of how I would enjoy reading an article. Like for me, I really enjoy reading something that is not lengthy in words and a lot of examples and illustrations. So I just wanted to create an article that I will read.

That's awesome. So now a lot of people here listening to the show are probably thinking about how to get into content, how to start working on different types of topics, or what would be your advice for any other aspiring writer looking to find their topic specialty and finding their niche in the data science community?

I think to answer this, first of all, the question is good because I think every writer should find their niche. And when you first started, I think it could be hard to find a niche, but you could start with asking yourself which kind of content you wish to see more, but you don't see a lot. So. And then start with that. Right? It might be a bad idea when you get started with it because you realize that, oh, people don't really pay attention to what I'm doing right now. But don't get disappointed. Try to refine your approach over time based on the reactions of the community and go a little bit outside of what you find is your niche. Right? So experiment with one niche and experiment with another way to give your work out there. And then you might stumble on something that might be very interesting to your audience.

That's really awesome. I absolutely agree here, especially kind of experimenting with different types of content, pursuing what you're interested in and this will kind of help you on your own navigate towards that space where you're creating high quality content for your audience. Now, Kuyin, as you wrap up, I'd love to know what are exciting topics that you're looking to cover next in your writing and how are you looking to kind of grow to the next level as a writer as well?

The one I'm writing right now is about GitHub Actions and I think it would be an interesting article for audience because I think a lot of data scientists don't know how GitHub Actions can be very helpful to automate the workflow. One of the GitHub action that I will cover is to act like a Google Colab batch when you publish your Jupyter notebook on GitHub. Right? So people can run your code from Google Colab and like how to automate the process from you have a model that is deployed to production and then somebody else, another data scientist wanna push there the change of the code to the main branch. Then how do you automatically test those code to make sure it's good before being merged to the main branch. So I think it would be interesting to data scientists and my new experiment is the YouTube videos. I have just started them recently and it's kind of like I try to apply the principles that I have just told you and I have some new ideas about how to make the YouTube because you short YouTube videos that people seem to really like and some that people really don't care about. So I think I might know what will make the YouTube videos better. So I'm going to try with those and I'm very excited for them.

That's really awesome. I'm super excited to watch all of these videos and really excited to read all the content that you're working on. So Kuyin, before we wrap up, do you have any final words before we end on today's episode?

Yeah, I think like if you want to start something but you hesitant to do it because you try to think when is a good time to get started. Just don't wait when you're ready, just start on like whatever you you want to start anyway. And you will be surprised by how much easier it is is when you actually get started. Because if you think about it, it seems difficult but when you start on it it's. It's not that difficult to keep moving anymore.

That's really awesome. Thank you so much Queen for coming on the podcast.

Yeah, thank you.

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