[00:00:05] SY: Welcome to the CodeNewbie Podcast where we talk to people on their coding journey in hopes of helping you on yours. I’m your host, Saron, and today we’re talking about how to use code to build cross-cultural understanding with Laura Gutierrez Funderburk, Data Science Intern at Cybera’s Callysto Project.

[00:00:23] LG: And also I think they’re at a point where they’re trying to motivate more people to learn the art of basket weaving. So we thought this would be a nice way to bring back that spark and that excitement.

[00:00:32] SY: Laura talks about how diving into projects and finding mentors helped push her through her coding journey, using Jupyter Notebooks to create curriculums for teachers with the goal of cross-cultural understanding and why building tech with that mission in mind is important after this.

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[00:02:21] SY: Thank you so much for being here.

[00:02:22] LG: Thank you so much for having me on today.

[00:02:24] SY: So tell us about how you got into coding.

[00:02:27] LG: I was working towards my bachelors of mathematics at Simon Fraser University, and I was initially working as part of a calling center. And this calling center, what we would do is we would raise funds for scholarships and bursaries, and we reach out to the alumni. So one of the questions we normally ask to the alumni was, “What kind of advice would you provide to a student like myself?” And a lot of the times the alumni would say things like, “Well, try co-op, do study abroad, talk to your professors, seek projects.” And I thought these were really good pieces of feedback. So I ended up doing all of those.

[00:03:02] SY: Oh nice!

[00:03:03] LG: So I decided I wanted to go to Germany for study abroad term and figured, “Well, I’ll need some sort of funding to support expenses while I’m abroad.” So I applied for co-op. Through co-op, I worked as a help desk IT support co-op student. The place that I worked was the BC Cancer Research Center. What I did is I supported people via installing software and troubleshooting issues and making sure that they had everything they needed.

[00:03:33] SY: Okay. So when you say co-op, just to make sure I understand, is that when you go to school, but you’re also working at the same time?

[00:03:40] LG: Yeah. So the co-op program allows from 4 to 12 months of paid working experience. So it usually means I can work full time for this time while being remunerated and it counts towards my studies. I’m not obligated and I’m actually not recommended to take classes while I’m doing this co-op program.

[00:04:01] SY: Oh, that’s great.

[00:04:03] LG: And at this place, I learned that a lot of people worked with software and coding to solve problems in biology. And it was the first time I was exposed to the idea of using programming to tackle these kinds of problems. I was very intrigued. And at some point, there was an announcement about a hackathon coming up soon. I was like, “Ooh, a hackathon. I’ve never tried one of these before.” So I went there. And I loved it. I worked with a team that was working on a problem related to building open source software to solve a bioinformatics-related question. I loved it. I absolutely loved the idea that you could use coding to tackle research questions. Following my co-op, I had study abroad. So the next piece of advice that I received was connect to a professor. So he asked one of my professors if he’d be willing to sort of work with me on a project that we could tackle while I was overseas. So the project was working on classifying data related to the Anopheles mosquito. Over a hundred species of this mosquito are responsible for transmitting malaria. So there’s a lot of interest in public health. So we were working on using Python code to clean up and reclassify a dataset of this mosquito family.

[00:05:19] SY: There’s two things I love about your background and how you got into code. Number one, I think we get a lot of advice all the time of how to start getting into code and what we should do. And I think that a lot of times we’re either not able or not interested in doing all of it. I think we kind of pick one or two things and kind of run with that. And I think it’s just incredible that you did everything, like you did all the stuff that people advised. I think it’s really cool. And then the other thing I really love about your story is that it’s very applicable. I think that a lot of times when we’re learning how to code, most of the projects that are available to us, most of the opportunities that we have are making kind of side projects, things that hopefully help people, but ultimately that’s not really the priority, like no one saving lives. You know what I mean? Usually, that’s kind of the way it is. And I think that with what you’re doing with the mosquitoes and malaria and the cancer machines, you’re doing stuff that actually directly impacts the life or death of humans. That’s really incredible. Does that mean something to you? Did the fact that you’re doing something that’s biology based that’s so real life, so to speak? Did that have an impact on how you saw coding, how you view technology?

[00:06:39] LG: Yes. So being able to work towards helping issues in public health and issues that affect people’s lives absolutely was one of the factors that drove me to continue working on coding.

[00:06:53] SY: So of all the different things that you did, the advice that you followed, what worked best for you in terms of learning how to code?

[00:07:01] LG: I think what worked best for me was definitely the hands-on experience. I think part of what I found so appealing about the advice I received was that it was hands-on, it was real life, it was outside of the classroom. I think that was a huge factor in my process. I think I’m a very hands-on type of learner. I don’t necessarily do so well reading material and working in theoretical aspect, but I’m better at working in a hands-on environment. So I think having the job experience, like that was very applicable to real life, going through the process of applying for a job and being rejected and then applying again until you find something. And then within that process, sort of this messy, organic process of finding someone to collaborate with and work together to find a question that we’re interested and then solving problems together, I think, it’s a style of learning that I really, really like.

[00:08:00] SY: So what was it about coding that resonated with you? We talked about the fact that it’s real-world application, the fact that it’s biology, that it’s science definitely helped your perspective on it. But when it comes to sitting in front of a computer, typing things in, getting something out, what was it about that process that really connected with you?

[00:08:20] LG: I think when I spent a lot of time working on a problem, I would feel frustrated, especially if I ran into an error or a bug and I didn’t know how to solve it. I had the experience of sitting through this horrible, painful experience of not knowing what to do and seeking on the internet what other people tried and talking to others about my process. And then it’s suddenly clicking, eventually finding an answer and it’s like, “Oh, it works. It works.” So that eureka moment where you see your code running and it works and it’s doing what you wanted to do. It’s great. It’s a great feeling.

[00:08:58] SY: So tell us about some of the challenges you had while learning how to code. I can imagine working in all these different contexts and all these different situations, I’m sure you hit a couple of speed bumps along the way. What were some of the hard parts of that journey?

[00:09:13] LG: I would say I wasn’t familiar with the process of troubleshooting and debugging at the beginning. I also had taken very few classes at the time I started coding. I did most of my learning online, which means I don’t necessarily have the in-depth understanding. So roadblocks sometimes involved not knowing how to handle or navigate via errors or bugs. So my first language or the first language I learned was Python. So other languages, I usually find very daunting and intimidating. And I find that I oftentimes, when I try a new language, one of the roadblocks I find is I need to feel that I have others to work with. Otherwise, I find it very intimidating.

[00:09:59] SY: So tell me about the emotional journey of learning how to code. What kept you motivated during those frustrating times? What made you determined to continue? Tell me about that.

[00:10:09] LG: I think what really helped me in first steps of my coding journey was having a mentor. So what this meant is we had this project together and we’d have a meeting once every week, once every two weeks where I shared my progress. And I think this was huge in helping me stay connected and engaged. He was facilitating that experience of, “Oh, this is a roadblock. Let’s work on it together.” So I think that was huge in terms of the emotional process, having that structure available and having that opportunity to discuss with someone as I move forward.

[00:10:45] SY: So you continued working at Cybera Callysto Project after graduating college. Can you talk about what Callysto and what Cybera are?

[00:10:55] LG: So Cybera is a non-for-profit organization that is one of Alberta’s technology accelerator. The Callysto Project is done as part of a partnership between Cybera and the Pacific Institute for the Mathematical Sciences. Through the Callysto Project, we develop educational material and deliver workshops for teachers and students in K through 12. And we share concepts like computational thinking, programming with Python within the context of the curriculum. So we’ll have exercises on mathematics and social sciences and a little bit of biology, but with a computational thinking twist. So what this means is we’ll have a number of Jupyter Notebooks. This is an application that allows me to interact with Python code life. So we explored different concepts in the curriculum and that an interactive twist by a Python and Jupyter Notebooks.

[00:11:53] SY: Interesting. So working with kids, people that are so young, what is that like? The topics that you mentioned, they sound really hardcore and very intimidating for anyone, let alone a 15-year-old. What is it like working with kids on such it feels like pretty cool complex topics?

[00:12:11] LG: So prior to COVID-19, what we would do is we would sometimes visit high schools and we’d organize student hackathons. And we’d prepare tutorials with Jupyter Notebooks and Python, on how to interact with a notebook, how to interact with Python, how to print your first hello world statement, how to do additions and for loops. And eventually, we would build on top of the tutorials and introduce them to a bit more complex topics like Pandas DataFrames or data structures or visualizing. So towards the end of the hackathon, the students would have a challenge that they could work on together. And usually the challenge involved a question and a data set. One of the data sets that we share with them is an artificial data set with data about pets. So they learn about how many different pets are there, what are the different kinds of species in the data set, how many legs do the pets have, what are the ages and how heavy are they. During the hackathon, a number of Callysto team members would walk around and support the students and provide assistance as they needed and then the students would share with us their progress. And usually we have a few winners based on usually the insight. So one, we looked at a code completion, perseverance, and three, the quality of the insights. So can you look at a picture or a plot and can you tell me what you understand? It means related to our original question. Once COVID-19 happened and we couldn’t host in-person hackathons anymore, we started hosting online hackathons. I think because we realized we had access to a lot of open data, we started adding themes to the hackathons. So the last three hackathons we explored involved colonizing Mars or sustainability in Mars. The idea was the same. We started off with a tutorials and exercises and we would give the students a dataset or a question. And eventually, we made it fairly open where the students could even go and get their own data set and then share with us what they found. It’s been a really, really interesting journey and it was great seeing some of the same students join with the online hackathons. You start seeing the same names and it’s great to see people developing their skills.

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[00:15:47] SY: So I know that one of the goals of the work that’s being done at Callysto, the workshops that are happening there is the goal of cross-cultural understanding. Can you talk a little more about that?

[00:15:58] LG: One of the projects that we worked on as part of Callysto include the Coast Salish baskets, notebooks. So this is done as part of a partnership and collaboration along with Tla’amin Nation in British Columbia. And so two professors from Simon Fraser University, Professor Veselin Jungic and Professor Cedric Chauve, visited Tla’amin Nation and discussed the possibility of developing notebooks on a topic that they were interested in. They agreed on modeling the mathematics behind some of their baskets and the patterns that they use. Cedar weaving is an art that forms part of the Tla’amin Nation. So apparently, the process of designing and creating these baskets is extremely complex. There’s a ceremony around requesting use from the cedar trees. They have to cure and prepare the cedar material and soak it for several days so that it’s soft and you can start bending and weaving with it. And then the process of actually weaving takes weeks. And so what we found very interesting was that there’s a lot of mathematics and a lot of complexity involved in designing the patterns. So what we explored there was geometry-related properties of the designs were. So what we had available is we had two more Callysto developers in addition to myself. So the professors said Tla’amin Nation provided a couple of pictures of the baskets and our goal was to develop a Jupyter Notebook that would allow us to explore the math behind these patterns. So we designed two Jupyter Notebooks that had the notion of the anatomic motif, which is the smallest possible unit, such that when I apply operations like flipping or reflecting or stacking, I can recreate an original pattern. So we did. We worked on those during the summer, and towards the end of summer, a couple years ago, we had these two notebooks that, one, let us play with geometrical operations until we could recreate some of the patterns. And then once we had a pattern, we could visualize it in 3D. So that’s one project that we worked on.

[00:18:14] SY: Very nice.

[00:18:16] LG: It was great. It was great sharing this with the Tla’amin Nation. A year later, I had the chance to go visit them and share with them our progress and get some feedback from them. I think part of the challenges they experienced right now is there’s a kind of a loss in a cultural understanding and appreciation for this art. And also I think they’re at a point where they’re trying to motivate more people to learn the art of basket weaving. So we thought this would be a nice way to bring back that spark and that excitement.

[00:18:44] SY: So you mentioned Jupyter Notebooks a few times, and I know you gave a brief definition of what it was a little bit earlier. Can you go into a little bit more detail of what it is and what it’s like to use it?

[00:18:54] LG: So you can launch a Jupyter Notebook into a web browser. What it is, is an application that lets me interact with code and the code is not limited to Python. I can explore code such as R or JavaScript or even HTML and CSS. So what it is, is you open up a Jupyter Notebook and what you’re seeing is you’re seeing a website that has these blocks and there’s two kinds of blocks within the page. There’s text-based blocks, and these blocks are normally called “cells”. And then there’s code-based cells. So the text-based cells allow me to enter formatted text, like a nicely formatted title or a subtitle or a link or even a table. And then the code-based cells allow me to play with Python or R or any kind of code. So whenever I have one of these code-based cells, I enter a Python command, for example, print and then quotation marks, “Hello world”. And then the Jupyter Notebook has these buttons that allow me to run my code. So that means that if I enter some code in one cell, I can press run and immediately get feedback and check that my code is doing what I wanted to do.

[00:20:12] SY: So let’s get a little bit more technical with the basket weaving example. How do you translate something like that into code? What’s the process? What are the steps for that?

[00:20:22] LG: So we did a lot of brainstorming at first. We kind of thought we have a picture of a basket and it’s 3D. How do we represent this using code? So we kind of had to use the mathematical modeling to do this. So we have a 3D object and the 3D object in this case is a basket. So you can think of it as a cube, for instance. So each of the faces of the basket, we can think of it as a face in the cube. So that’s step one. Step two is we noticed that the basket has these coils or beads. These are kind of the smallest units that the weaver has to work with. So the third step is, okay, so we have our 3D object and we can think of each of the faces as a 2D object, like a square. And then within that 2D object, we have these little coils and within the coils, the pattern is embedded. So the third step is how do we work with the pattern, how do we encode the pattern. So this is where the notion of geometrical operations came in, how can we break down a pattern into simpler pieces so that when we do mathematical operations that reflecting or stacking or flipping we can recreate the original one. So that’s the mathematical piece. Now the coding piece, we have to find a way to encode the colors and the sort of a basic color of a basket. So what we did is we, for each of the phases, the basket, we encoded each color as a letter. So the basic color of the basket, which is a natural color of cedar, we encoded it with a letter C. So to recreate the whole thing, then we would use for loops and we would iterate and we would go from rows to columns. So if we have a 2D square that has beads, then we think of each of those beads as a square. So with a couple of nested for loops, we can create each row and the square. And then within that, we can stack those rows of squares to get the rest of the basket. And then we repeat a similar process to create the 3D model. We add one more for loop to make it three dimensional.

[00:22:36] SY: So when you create a model like this, can you easily create models of other baskets with other different patterns?

[00:22:44] LG: Yeah, absolutely. One of the modalities we implemented was a blank canvas where the students can choose how big the pattern they want to create. So they have this N by M grit, so N rows and M columns, and so they can pick a color and just click on the squares to create a pattern. They can save that pattern and then apply operations to create more complex patterns. That’s one way to do it. As part of the basic patterns we provide, we have a triangle, a chevron, and a broken line, which were based or inspired on the pictures of the baskets that we first obtained. Having said that, it is worth noting that some of the baskets that Tla’amin Nation created sometimes contained signatures for the families. So not all patterns can be recreated. Some of them are not necessarily created using geometry or mathematics, but rather there’s that unique element that our model is limited in.

[00:23:46] SY: So it sounds like you’re using Python a bunch for a lot of the workshops and the things that you were creating. Would you say Python is the best language for this kind of thing?

[00:23:56] LG: Python is a very user-friendly language. There’s lots of resources to learn. Python is relatively intuitive and it doesn’t need the user to do memory allocation, like other languages might. And so in that sense, it doesn’t require the user to think about a lot of things when they’re coding. And especially when you’re being introduced to a completely new concept is good that you don’t have to think about everything that is right in coding from memory allocation, to the type of variable that you’re working with, to what you want the variable to do. So in that sense, I think, one, because Python is relatively user-friendly and there’s a lot of resources out there and it’s open source. I think this is why we picked it for our projects.

[00:24:44] SY: So are there other projects that Callysto is working on in the future that have to deal with cross-cultural understanding?

[00:24:51] LG: One project we worked on during the summer involved modeling fish traps notebooks, and this one also involved a tradition from First Nations, was also led by Veselin Jungic and Professor Cedric Chauve. So this particular notebook goes back to studying clam gardens and fish traps, which these are intertitle modifications of the beaches that allow harvesting clams and fish more easily. There’s some debate on the origin of the fish traps, which is a complicated issue on its own. But what’s fascinating about these systems is that, one, usually they cover hundreds of kilometers, which means they’re very complex, but, two, they’re built in such a way that they maintain sustainability for a long period of time. Meaning, whichever nation was modifying the beach to either harvest clamps or trap fish was doing it in a way that one provided enough food for the group while also preserving ecosystem and lifecycle of the species that they were harvesting. Once we start thinking about the life cycle of salmon and the long-term impact of tempering with the environment, it’s really fascinating to learn that some of these traps have been there for thousands of years and the ecosystem was not affected in a way that we’re seeing in today’s level.

[00:26:19] SY: Coming up next, Laura talks about her biggest piece of advice for those who are looking for the best path for them on their own coding journey after this.

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[00:28:22] SY: So tell me more about the purpose of these workshops. I know that part of it is cross-cultural exposure, part of it is exposure to different coding techniques and different concepts. Why are these workshops important? What impact do you hope to have in the future?

[00:28:37] LG: Now that we are handling a lot of data, I think data has become a central part of our real lives, whether we are studying the COVID-19 outbreak and how the pandemic is having an impact on our own country to socioeconomic status, to how even students do in class. I think having the tools necessary to handle data-related questions is huge. So what we’re hoping to do is we’re hoping to help students gain comfort with some of these tools that later on, they might be able to use to answer big data-related questions. Other things that we’re hoping to do is we’re hoping to help the teachers gain confidence in using think these tools because I know a lot of the times the curriculum is heavy enough as it is. And as there is incorporation of computational thinking and data science into the curriculum, we are hoping that by providing these ready-to-use notebooks the teachers can access the material and use it as part of their classes without necessarily needing to learn everything from scratch.

[00:29:39] SY: So this idea of promoting cross-cultural understanding is very interesting and doing that with code is very fascinating. And of course, a lot of listeners are figuring out what side projects to do, what they can do for their portfolio, how they can contribute to the community. So I’m wondering for folks who might be interested in creating projects of their own to promote cross-cultural understanding, what advice do you have for them?

[00:30:01] LG: The biggest piece of advice I’d have is finding someone to collaborate with. This can be in the form of seeking a meetup groups that are working on coding and proposing or pitching an idea and finding people to work with because you should be tackling these problems. One, there’s the coding aspect that can be complex in its own way. But then the second piece is when you’re trying to share an idea with others, it’s often really helpful to have multiple perspectives involved. So pitching an idea to a diverse group of people and then having involvement or inviting people to collaborate along with you goes a long way in terms of having that perspective. So yeah, seeking, one, groups to collaborate with, to do the coding, and two, seeking groups to collaborate regarding the social or cross-cultural understanding topic that you’re interested in.

[00:30:56] SY: Outside of doing these Jupyter Notebook workshops, what are some other ways you think we can use to tackle or we can use coding to build cross-cultural understanding?

[00:31:05] SY: One way to do this is I think by having meetups or hackathons or conferences. I’m thinking outside of Callysto world, other conferences or groups that I’ve formed part of include the Vancouver DataJam. So this particular group, we’re a group of women and some of us come from the PyLadies world and others from the R-Ladies world. These are two groups of women in STEM that do coding. So I think outside of Jupyter Notebooks and coding, reaching out to these groups is one way to sort of continue fostering this discussion. And I think a lot of these groups are volunteer based. So lending a hand or learning who was seeking input is a great way to make your voice heard.

[00:31:50] SY: So going back to how you learned to code and all the different methods you use, the different processes you went through, what advice do you have for people who are trying to figure out what’s the best path for them?

[00:32:03] LG: One thing to keep you motivated is finding a project or a problem that you’re interested in. And then two, and I think this one is huge, is finding people to do it with. And if you’re a student going to university or high school, finding professors or teachers who are interested in the problem that you’re working with is huge. If you’re outside university or school and you’re wondering, “Oh, I want to get into coding, but I don’t have the infrastructure that I had,” in that case, seeking again meetups or online communities that do Python. I think Python and R are really nice languages to do this because they are open source. Meaning there’s a developer community out there constantly pushing forward new updates and new libraries. You can find a lot of these projects by going through GitHub. So you don’t have to find one of the bigger ones, but I think if you go digging R open source libraries or Python work in progress libraries, there’s all these repositories that you can collaborate in. For those of you who haven’t heard the word GitHub before, again, this is a tool that allows you to store your code and work with others and code, and you keep version control. Meaning if you make a new change, you can always go back to previous changes. But the really nice thing about GitHub is you have features that allow you to sort of get someone’s attention and say, “Hey, I see your project. I see what you’re doing. I have this suggestion. I would love to hear your words.” These tools are called “pull requests”. So you can create a pull request on someone’s repository and say, “Hey, I think this is an improvement.” Because it’s online, usually you might have to try a few times before someone responds because a lot of the time the dev community does it on their spare time, but definitely seeking people to do it with. So one, find a project or a question that you’re interested in, and two, find someone to collaborate with. And this can be in the form of a meetup or a hackathon or an online community or a GitHub repository.

[00:34:15] SY: Now at the end of every episode, we ask our guests to fill in the blanks of some very important questions. Laura, are you ready to fill in the blanks?

[00:34:22] LG: I am.

[00:34:23] SY: Number one. Worst advice I’ve ever received is?

[00:34:26] LG: For a while, I thought that I could only demonstrate my programming skills if I could answer theoretic questions about coding correctly.

[00:34:34] SY: Interesting.

[00:34:35] LG: Yeah. One of my first coding classes had nothing to do with working in a computer. We discussed things in class and we wrote things on paper. And I think I failed one of these classes and I had to retake the exam. I think after retaking it, I passed and it was fine, but I thought I couldn’t code. After that, I thought I just couldn’t get into it. If I couldn’t answer these questions on paper, then I probably couldn’t code. I think it’s the worst advice I received because it led me to think that I really couldn’t code and it was years later once I started getting into hands-on projects that I realized, “Oh, no, there’s a lot more than being able to answer coding question on paper.”

[00:35:16] SY: Number two, best advice I’ve ever received is?

[00:35:19] LG: Talk to people about your interest in coding, whether in meetups and online events, hackathon or in school. Sharing your interest in coding often lets others know that they can involve you in programming-related activities. And again, this is huge because it means you’re putting yourself out there and saying, “Hey, I want to code. I want to learn. I’m interested.” So that means that when people are putting together activities, they can think of you, “Oh, I remember they said they were interested. I wonder if they want to join us.” So put yourself out there as much as possible. I think this is the best advice I received when I was in school. Go to co-op, do study abroad, talk to professors, and it paid off. It definitely paid off. And if you’re not in school, the same applies, go to meetups network, talk to people that you think are doing cool work and ask them if they’d be willing to take you under their wing or find a group that’s seeking volunteers, either for hackathons or coding related project

[00:36:13] SY: Number three, my first coding project was about?

[00:36:16] LG: So my first coding project was about classifying data from the Anopheles mosquito genus.

[00:36:22] SY: Number four, one thing I wish I knew when I first started to code is?

[00:36:27] LG: One thing I wish I knew when I first started the code is how important cleaning up code as I develop is. When I first started to code, I would write a function and I’d say, “Oh, it’s done. I’m so happy. It works.” And then leave it alone and move on to the next thing. And usually when I write a function, chances are I’ll use it later again or I’ll have to modify it or someone else will have to modify it, if I’m not around. So it’s so important to clean up the code as part of the development. Because once I feel my function is down, chances are I’ll put it aside and I’ll work on the next one. So making sure that the function that I’m working with right now is well commented. It does what I expected to do. I account for user input error. I account for other unforeseen issues. And if all that fails, I at least add some kind of mechanism that tells me something went wrong so I can go back and troubleshoot.

[00:37:20] SY: Love it.

[00:37:21] LG: Like dedicate time to your function. Don’t leave it alone.

[00:37:24] SY: Yes. I love that. Well, thank you again so much Laura for joining us.

[00:37:27] LG: Happy to. Thank you so much for having me.

[00:37:36] SY: This show is produced and mixed by Levi Sharpe. You can reach out to us on Twitter at CodeNewbies or send me an email, hello@codenewbie.org. Join us for our weekly Twitter chats. We’ve got our Wednesday chats at 9 P.M. Eastern Time and our weekly coding check-in every Sunday at 2 P.M. Eastern Time. For more info on the podcast, check out www.codenewbie.org/podcast. Thanks for listening. See you next week.