[Transcript] From Curiosity to Career: The Hack the Hood Story (feat. Henry Bowe)

Eric Van Dusen  0:07  

Welcome to the UC Berkeley Data Science Education podcast. We're happy you're listening in today. In this space, you'll hear from a variety of distinguished data science educators and professionals. The individuals we'll speak with are diverse in experience and perspective, but share the common goal of shaping the future of data science education. Our idea is to have some informal conversations, with the goal of creating community and let people hear from practitioners in this growing new field.

Lauren Chu  0:37  

My name is Lauren Chu, also from Data Science Undergraduate Studies. I'm working as an intern with the division's external pedagogy team, and I'll be helping to guide the conversation today too.

Eric Van Dusen  0:50  

Hey, everybody, today we have Henry Bowe from Hack the Hood. Excited about this conversation, and to start off, could you give us a brief introduction to yourself and how you got into the space of data science education?

Henry Bowe  1:04  

Yeah, absolutely. Hello, everyone. My name is Henry Bowe. I'm 27 years old, from Washington DC by way of North Carolina. My way of getting into data science and software engineering was actually pretty interesting. Before I got to college, the most that I knew about software engineering, and well, I guess not data science really at that point. But the most I knew about software engineering and programming at that point was that it was somehow related to game development. I've always been a really avid video game player. Oh, so I had always known that there was some relation between those two things. But outside of that, I didn't really have much exposure and much knowledge about things like, what programming was and things like that. But whenever I started to go to college, my freshman year, I went to Morehouse in Atlanta. And I was studying mechanical engineering, because truthfully, I didn't really know what I wanted to study. But I knew that like I was good with numbers, I was good with calculus. And physics was something that was interesting for me. So maybe engineering was a way that I would want to go. So that's why I ended up choosing Mechanical Engineering my freshman year, but there was actually a prerequisite to my first engineering course that I had to take. And it was actually a programming class where we talked a little bit about C++. And I, that was my first like, real exposure to what coding and programming really looked like behind the scenes. And I just almost immediately fell in love with it. Um, I ended up switching my major, I ended up switching schools. And I ended up coming to Dominican University out in California to study computer science. So it was definitely not a traditional route for me to get into it. But once I did, like I did, I was definitely blown away by it. I fell in love with software almost immediately.

Eric Van Dusen  2:42  

Nice. That's a great story. All right. So let's talk about this organization you're working for now. Can you give us a brief introduction to Hack the Hood and what you're currently working on?

Henry Bowe  2:52  

Absolutely. So Hack the Hood unapologetically serves black Latinx, indigenous people, other uses of color, other groups of youth of color as well, providing them with programming skill building programs, and career navigation support, which is always grounded in social justice and being able to support building economic mobility. One of the best things about our programs is that they are free, you don't have to pay to get in and on the other side of that is we often pay out stipends to our learners who complete our programs as well. So it's definitely a nice incentive to draw people in and want to get them to start to think about technology. And actually, we really believe that you can use technology for a lot of things. And one of those things being a tool that can help you increase the economic mobility of your life, but also being able to improve your community, other communities around you. And there, as we were just talking about Ben, before we really got started, there's just so much you can do with technology, there are so many different ways that you can apply it. And we really believe that if you can, if you can give somebody the tools to really feel like they belong in that space to really feel like they can be comfortable in that space and they can thrive in that space, then the sky is really the limit. So we really want to focus on drawing in and giving opportunities to people who don't generally have those opportunities. So a lot of people who come from different situations like me, as I said before, I had no previous exposure to coding and things like that. I would have loved to have had something like a hacker like while I was in high school. So I definitely think that if we can provide that space for people, it'll definitely create a lot of opportunities for folks but also be able to address the diversity in the tech industry right now. And currently, what we're working on is trying to become a pre apprenticeship program and creating different partnerships and programs with other corporations and organizations to be able to result in more direct job placements coming out for our learners and giving them more and other types of career advancement opportunities as well but being able to directly place them in things so that they can And, you know, begin to really walk that make those next steps towards getting to where they want to be in their careers, and then also getting ready to offer the programs that we offer each year.

Eric Van Dusen  5:12  

Fantastic. It's such a great mission statement. So one thing that we're interested in is, you know, you do have this technical curriculum, there's like job building and empowerment and stuff. But there is this technical curriculum, including the build program, which we saw was sort of based on like Fundamentals of Data Science. Can you tell us a little bit about the technical curriculum?

Henry Bowe  5:33  

Yeah, absolutely. So we have three programs at Hack the Hood, they're called hustled build, and Laney, which is our drive program, so our Laney Drive Program is a little different from the other two. So I'll start by talking about that one. So our Lani Drive Program is actually in partnership with one of the local community colleges there in that area Laney Community College, and what we do is while our learners go through our hustle and build programs as well, they also take other software engineering and data science related courses through Laney, which then it's ultimately for them to receive a, I think there are four different options, I could be forgetting the number right off the top of my head. But there are four different options that they have in Laney for different data science certificates, that they can get through Laney By completing these different courses and going through our program and whatnot. And so that's a little bit different, because the curriculum that we give those particular learners is more so in conjunction with the learners that they have at Laney, but looking at just specifically our curriculum at Hack the Hood through our hustle and build program. So hustling builds are related, but separate in a way. So hustle is more of an introductory program. And it's designed for anybody who feels like they have curiosity about technology, but may not really have any previous experience to be able to really know if it's something that they can prosper in, if it's something that they really want to do for a career. So the aim of the hustle program is really to just expose people to the different programming ideas, the different socio technical issues that come along with different introductions of different technology, but really just getting their foot in the door so as to get them to understand, like, what are programming languages? What does it mean to code like, what is debugging, like, from a surface level, really breaking down what the fundamentals of software engineering and development are, and being able to get them more used to those sorts of things. And the four main components of hustle is really teaching them the Python coding skills and the basics of Python coding skills, relating that to social justice. So we always want to make sure that our learners know how technology can have an impact on various communities. So that's always at the forefront of what we're trying to teach. And then being able to, as we said, making sure that we provide them with resources, whether that be a different, like online resources about different skills that they can practice or whether that's mentorship that we offer, what the the panels that we host to give them different information about career navigation and such, but being able to give them different resources and opportunities so that they really feel like they are building a sense of identity within that tech space and feeling like they can prosper in that career. So that's really hustle. And then build, as you said, you can see they're more focused on data science fundamentals. So our build program is definitely a bit more geared for people who have a bit more exposure to technology, maybe they have basic Python skills, or they have basic JavaScript skills, but they essentially understand development to, at a basic level, at least. And so what we then do is, then we take those skills that they already have, because I I very much believe that if you know one coding language, it's not going to be too too difficult for you to learn another because the building blocks of all programming languages essentially are the same where you have like your variables, different data structures, etc. And it's just really the syntax and how those things talk to each other. That's a bit different across the different languages. So we believe that if you really know one language, then we can put you in, whether you know the language that we focus on or not, which is Python, but we feel like we can put you in and give you those skills and build, build that up. So when they come into build, we focus primarily on learning the basics of data science, data literacy, and just understanding what it means to be a computational thinker. And so specifically with the software engineering tools that we use a lot and build, so as I said, we focus on teaching them things from the Python language. But we do talk about our SQL as well. We don't really do too much with those languages, because those can kind of be a bit much for people who don't have a lot of experience with technology. But definitely we do talk about art SQL and how those things relate to the industry. But for the most part, they're practicing most of their skills in Python, using different packages like NumPy pandas, do you Bigger notebook matplotlib. And just doing something very simple, like data manipulation, data analysis, we talked a little bit about creating machine learning algorithms and things like that.

Eric Van Dusen  10:11  

Right? I mean, yeah, it really lines up with what we're thinking about as well. I, I'd love to move to this next question that you know, you guys talk about bringing social justice perspectives into the teaching of data science and to the empowerment of the youth? Could you just give some examples about how you bring social justice into the data science curriculum?

Henry Bowe  10:34  

Yeah, absolutely. So whenever we, I think that I believe that you can really break data science into five different pieces, right, and you've got like the data collection, the data cleaning, data analysis, data modeling, and then just the overall management piece. And I think that in each of those pieces, there's definitely something that can be talked about that relates to the social justice aspect of what we like to focus on with our learners. So thinking about the data collection piece, one of the things that we really like to focus on is, whenever we talk about data collection with our learners, we always try to emphasize the fact that there's always a lot of talk around how numbers are automated, and these, we have all these algorithms and things that automatically collect these different data points, and etc, etc. But there's still a human point in these processes, where there's the human who is picking out the data that's being fed into these algorithms, or it's the human that's designing the algorithm itself. So there's always going to be some unconscious bias in these processes. So that's the first thing that we try to bring to their attention. And a lot of that we relate to different things that can be looked at, specifically in communities of color. So one thing being like, the way of what's the exact phrase that I want to use, I guess I'll just say crime data, the way that crime data is often skewed, because there's a lot more monitoring happening in communities of color. And so with that being the case, that is a bias, and thus, that can affect what the predictions and the outcomes of this crime data algorithm is. And so we definitely try to keep those sorts of focuses with each layer of the data science process. So then when we move into data cleaning, you can talk about how we depending on like, if you're working with big data, or you know, if you're working across multiple different databases, it's very easy to miss manage things Miss Miss entry, Miss enter things, or it's very easy to have things go awry within that process, if there isn't, you know, accountability, and multiple eyes being head on and etc, etc. And we like to really point to a lot of like, hard technology examples, one of the ones that we really liked to talk about is facial recognition. There's, I forget what the name of the company was. But there's this one video that we always show because it really stands out to me. And I forget what the name of the group was that was put on the video. But when the video starts, I forget the years well, but the video starts and it's like the early 2000s. And there's a guy of color, and then a white man in this bathroom together. And they're friends and they're talking and then the white guy goes to use the sink. And you know, this was around the time where they were first introducing automatic water being dispensed from the sinks. So the guy went to use the sink, and it worked for him. But when the man of color went to use the sink, the water didn't come out. And then it transitions to another sort of skin tone recognition software, where they were in. They were in some sort of factory somewhere. And it was a black guy and a white guy. And they were sitting in front of the camera, and the camera was supposed to follow them as they moved. And then when the white guy moves, the camera follows him. But when the black guy moves, it does not. And so we talk about different things like that and show how different pieces of real life relaxed puppies. Let's talk about how different pieces of the algorithm can be influenced because there are different human processes that can have that influence. And it's not just all, it's not all computerized. It's not all automated. And there are definitely still biases that exist within these processes.

Lauren Chu  14:34  

Yeah, definitely. So when you talk about all of these, like complex technical concepts like AI and facial recognition and things like that, what methodologies or what teaching strategies you use to make those concepts accessible to students with varying levels of experience.

Henry Bowe  14:53  

Yeah, this is actually something that's key for us at Hack the Hood because we always want to be the way that we say it is. We all always want to make sure that we're being mindful of meeting our learners where they are. And what that means is not because we tend to try to focus on people who don't have the most exposure to technology, there's always that sense of intimidation, when thinking about different technology concepts, thinking about different data science things. And so one of the two of the biggest things for me, actually, whenever I'm trying to teach people is one, trying to demystify the verbiage around computer science and demystifying the verbiage around data science, getting people to understand that like, we can, we can talk about things in a way that makes more sense to you by using words that you're more familiar with, versus me talking about things. And then we're like, Yeah, let's talk about how we build these neural networks and talk about how these different layers of the neural networks relate to each other and how the different neurons are interacting. When we're using all these words that people aren't familiar with, that's automatically going to get people to like retreat into a shell. So that's the first thing that I try to be mindful of is understanding that we have to demystify the way that we talk about technology for people to feel like it's something that can actually be understood. And then the second piece is being able to make these different technology concepts and things relate back to either things that these people are seeing in their day to day lives, or things that are really, really important and passionate to them. So what I think about in relation to that is whenever I was going through school, like even though I said that I had my first programming class, I completely fell in love with it. I barely passed that class with a C minus. So I definitely struggled coming into software engineering, and being able to understand the different things, and what helped me really gain comfort and familiarity was being able to relate it to things that I was passionate about. So I was able to take my passion for software engineering and data science and relate it to how I'm very passionate about sports. And I was able to put those two things together. And I was able to learn more about data science, through doing different sports data analytics, little mini projects for myself. And so that's something that I always try to keep in mind is being able to find out like, what is it that really speaks to that person that you're trying to teach? Or that group that you're trying to teach? And then finding a way to relate the topics and things that you want to teach them to that.

Lauren Chu  17:25  

Yeah, and one of my favorite parts of talking to educators is learning about any, like success stories that you have. So could you share any success stories or anecdotes from your experience? Teaching data science that really highlights the impact of your curriculum? Or, like maybe where your students went after Hack the Hood?

Henry Bowe  17:45  

Yeah, absolutely. I really love it, the first thing that comes to mind is after every program, we always do a round of feedback surveys and things for our participants. And it's always really heartwarming to see the different responses that they give us about how they really connected with different mentors that they had in the program, or how they really feel like they got a lot out of the program and that we really feel supported. But there's one specific learner that comes to mind for me. And he was actually one of the first learners that I taught when I came in. Good. I believe I joined Hack the Hood in 2020. And I started instruction for them the following year in 2021. And I believe it was either the first program or the second program that I had taught for them in that 2021 year, but we had a student who came through and participated in hustle and his name was Aaron Chen. And the reason he sticks in my mind is because he was great in the program, like he was really engaged, always asking questions. You could really tell that he definitely had this curiosity for wanting to learn more about software engineering and data science. But outside of that, once he did our first program, hustle came back the next season and did build. And then I think it may have been two seasons after that. So I think it was the summer of 2022, where he came back and he was like, I would love to work with you guys in the programs. And so he actually joined our organization as a contracted teaching assistant, and he helped me teach our build program. And that was actually really great because he was able to relate with the learners from the standpoint of Oh yeah, I've gone through this program already. I've gone through this too. I know what you're experiencing. So that was a really great thing to see. But the next step is shortly after he came and worked with me on that program. He got an offer to go and work with some research lead. I forget what school it was at, but he got an offer to go work for a research investigation team and they accepted him. He asked me to write his recommendation letter and he talked about how he told them about how hackers had such an influence on him. So that's definitely the biggest one that comes to my mind for sure.

Lauren Chu  20:05  

Yeah, it's great. And so as we reached the end of the interview, something that we ask all of our interviewees is do you have any parting thoughts or words of wisdom for data science educators around the world listening in?

Henry Bowe  20:20  

Yeah, absolutely. The most important thing to me whenever I'm trying to teach people, or even myself is that it's got to be fun. Like it. I think we when it comes to education, and when it comes to academia, we really lose sight of it needs to be fun, because like we were always so homed in on we need to learn this, we got to know XY and Z, we got to find ways to maximize how well people memorize this, this, this and this. But I think we really lose sight of enjoying that. Enjoying that thirst for knowledge, enjoying that process of learning things. I think we lose sight of that. And I think that that's really important to really have people truly understand these topics that can get pretty complex whenever you're talking about technology and data science.

Lauren Chu  21:13  

Great. Thank you so much.

Eric Van Dusen  21:15  

Great. Thanks, Henry. Super great to meet you. And I just want to add a little footnote here for the conversation that Henry had with his two dogs in the interview.

Henry Bowe  21:25  

I did, I apologize. Y'all probably heard them a few times.

Eric Van Dusen  21:28  

What are their names?

Henry Bowe  21:29  

I've got three dogs. One of them is a Belgian Malinois and his name is chopper ima, so I'll preface this. I am a big anime fan. So two of my three dogs actually have anime names. So chopper his name comes from One Piece Tony Tony chopper, the little reindeer. My oldest dog. She is a chocolate lab mixed with a pit bull. Her name is Kilala Kilala is an anime name that comes from an anime called eater. Yahshua. And then I have another dog. She is a pit bull mixed with something I am unsure of. And her name is Ivory.

Eric Van Dusen  22:04  

Oh, all right. Well, thanks for bringing them into the story too.

Henry Bowe  22:08  

Thank you all for having them.

Eric Van Dusen  22:17  

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Transcribed by https://otter.ai