 This is Think Tech Hawaii, Community Matters here. Okay, we're back. We're live. It's the two o'clock clock. I'm Jay Fiedel. This is like about science. And I'm standing in for Ethan Allen, our chief scientist. And today we're talking to Shamanad. We have two faculty from Shamanad, tech faculty. One is Ryland Chong to my left and to his left, Mark Speck. They're both assistant professors at Shamanad. We like Shamanad a lot because Shamanad is out of the box. Shamanad thinks ahead. Shamanad is trying to be sensitive these days under Lynn Babington and Helen Turner to deal with the marketplace as it exists for jobs and kids and our community. So this is really important. And what they do is really important. So first, I love this question. Ryland, who are you? Well, I'm Dr. Ryland Chong. I'm born and raised here. Actually in Hawaii. And from Maui. And I graduated from Maui High School. I came to Shamanad. I did my computer science degree there. And I specialize in database and learning about data and how to query them and design databases. And from there, I was really interested in learning more about data. And what I learned about it was I got to protect myself. I have to be mindful. We have all this data and what we can do with it and how can I protect myself. So I went off to Purdue to pursue my PhD degree. And I was in the field of cybersecurity or the program I was specifically in is called information security. That way it taught me how to protect data, protect myself, protect if I had to work for a company or an organization. One goes with the other. Yeah. Data, security, all the same now, yeah? Yeah. And from after getting my PhD, I realized, you know, now I learned how to do databaseing and learn about data. I learned how to protect or secure data. The next step was how do you work with large data sets? How do you work with data sets that are messy? And so my next step was looking into data science. And I was looking for programs. And Shamanad, which is my alumni, said, hey, we're interested in doing a data science program. Would you want to pursue it? Sounds perfect. Yeah, it was just a perfect dream. And what's nice about Shamanad is that I get to work with brilliant faculty like Dr. Mark Speck. I get to work with Dean and Provost Helen Turner. And I love the vision and mission and goals of the leadership from President Babington to board of directors to even our faculty. Is this amazing? And what's unique about Shamanad is that we're not siloed in our divisions. We come together. I worked with the education department. And we all come together to move Shamanad forward. So this is a great opportunity. And I'm really grateful and humbled to be here. I've always found Shamanad to be good tone, good relations and all that. People friendly working together. I always found that. So before I get to you, Mark, I want to have two questions. unpack two questions. What is a large data set? What is that? How large is large? Is it 2,000 records, 5,000, 10,000? What is it? How big is big? I'll let Mark also answer because he has a lot of experience in the area. But it ranges. And there's no definitive concrete definition of what large data set could be. It could be terabytes. It could be just maybe a zillion records or some sort. We know that's large. Yeah. But there's different definitions. And so large meaning that it's not going to be your traditional address book. Right. So my dissertation was about 100 something records. Some would say that's really small. So large data, big data sets, there's different definitions. It's also fields, too. If I have a data set with 200 fields, that's beginning a little on the large side. And the other question is messy. What's a messy database? Messy database. So sometimes when you. That's everything out there. Basically. Not having fields. So it's more of a neat database. Right. But it's basically just sometimes you get data where. So I'll start with clean. Clean data sets is basically you have all the fields filled out and everything looks nice. Messy data sets is we're missing fields, missing values. Maybe they put the wrong values in. And that's what's considered messy. Makes it a great challenge to figure out how to put information in where there is no information or correct bad information. Mark, how much of what Rylin said you agree with? Well, if this is on the record, I agree with most of it. But tell us about yourself. I came here by way of New Jersey. I was working at Rutgers University. And then I came to UH to get my master's in microbiology. Microbiology? Yeah, yeah. I'm coming from the Life Science Center. And so that just that ended up turning into a PhD. And the majority of my work was informatics. Bioinformatics is we're working with a lot of genetic information that we're getting from experimentation. And so just always working with data sets. And even at Rutgers, I was working with genetic data sets. And working with those and understanding, OK, all these experiments that we're generating now are just creating loads of data. And so I would, after finishing my PhD, I was getting moved on from one data project to the next to the next, never actually doing experiments myself by getting stuff that other people worked on, helping them clean that up, analyze it, and get something useful out of it, and handing it back to them. So it was more like I became a utility in that sense. You mean you were helping other people? Yeah, and it was kind of fun because it's always working on something new. No, it's like you get kind of anxious if you work on something for like two years, three years, you want to move on. I get to work on something new after each project, right? After I finish that, what's the next thing? I can work on multiple things at once. And a lot of times you start seeing common patterns. People start needing the same kind of thing. It's data, right? And a lot of times it's data from a lot of different sources. And so it's never clean. It's not tidy like these like to say. It's the variables are named improperly. They're not ordered properly. You might have the variables as rows and some columns. Computers like to see things in certain ways and you've got to see, OK, what have you done to this? And how much work is it going to take me to make it usable, right? It's like 70% of your work. So from where I was coming from, it was from the life science field. And it's kind of been pushed in this direction. I mean, I've allowed myself to be pushed. It was a natural progression. In order to do the life sciences, you have to know about the data. Right. And then when you learn about the data, you can apply those rules to other sciences. Exactly. So it's kind of like a second career arc for me. And it's kind of interesting because I get to run into people like Rylan. And Helen Turner is just an amazing mind out there just coming up with these ideas and helping us think ahead. And then our new president, Lynn Bavington, she's very much into being responsive to what the community needs in terms of education and how Shamanad can respond to that. And I really appreciate it because they give us full access to them. It's easy for us to ask questions. And we don't have to go through intermediaries to ask, what's the path we need to take? Or throw an idea to them. They're very responsive to us. And in that way, Shamanad has been really good for this kind of degree path. The jury has a question. The jury's question is, you're only allowed to be judged. You can't be both. Been there, done that. The jury's question is, at your UH experience, you were involved in research, right? Yes. And you were examining data and making algorithms to fix it or get conclusions out of it, whatever. OK, now you're in Shamanad. It sounds to me like you're more in teaching. You're more in dealing with students. Was that part of the reason for the transition? Well, the work I do at Shaman, teaching is only a percentage of it. It's also about how you spend your time. Well, right now, it's been helping to create this curriculum, doing research. And then teaching might be between 15% to 20% of what I do. What I learned, I get to pass on to the students because it's emerging, right? And so it's been useful for us to design certain courses that are going to feed into this data science program. You going to teach them? We're going to teach some of them. We're not going to be the full faculty. It would be exhausting. OK, well, now we're really at the threshold of the new students. Yeah, but we are the first among two other faculty. You're the organizers of the whole degree, sort of. Right, right, because there's definitely things I'm not well suited to teach and other people are better suited. So I'd rather just stick to what I'm better suited teaching and let other people that are more expertise in those areas. Some of the more advanced mathematics and stuff like that, that we're going to require for them to understand. Absolutely. So I'd rather have a mathematician do that rather than myself. Yeah, but given your flexibility, maybe you'll learn that too. So it's not that you guys woke up one morning with this idea about data science and making a program out of it. The administration came up with that, am I right? And they called upon you to help create this whole program. And I guess it's undergraduate rather than graduate, although you do have graduate courses in technology and then a computer science. This is going to be undergraduate then? Specifically undergraduate for now. And then maybe in the future we can look at progressing to a graduate program. But right now it's just undergraduates. But however, we do have an MBA program that will be incorporating some of our curriculum in or courses in there. That's very interesting because the B stands for business, not science. The undergraduate program is a science program, but the B stands for making a science graduate into a business graduate. Correct. And the science aspect of that would be, you know, data science is not just looking at statistics or math. What's the research process? We do it in our everyday life, and that's part of the science aspect of it. We define a problem. Once we define and identify this problem, we do a literature review. And that could be as simple as shopping. We want to buy a shirt. What do we do? We go online and look at all the different types of shirts. And that's kind of like our literature review process. And then we, um... I do that on the internet all the time. My wife does it on Amazon, do you? Never mind. And then you design a study from what you've found in the literature review. You develop your hypotheses or research questions. And then you conduct the study. You collect the data, which is all overlapping into data science as well. So, you know, there's overlaps in what the science term, terminology means, and it can be incorporated in multiple fields, not just business. You can include it in environmental studies. So it's like giving them the tools necessary to be able to go into their interest range. So that's where part of the science comes from. Okay, so here we are on day one when the program opens. When is the program opening this year? Next year? 2019, we're going to start... This year? Yeah, it's fall. September, okay. Our goal is to get the first courses started in fall. Okay, and you should. You can. You must be recruiting people to join this. Right, right. I told you when we spoke on the phone, I really like to be part of this program. Oh, come around and audit or something. You know, stand by at the back. We also, we can talk about it a little bit later. We have a summer program that we'll be running as well. It's going to be like a four week crash course. Is that starting this summer? Yeah, it's a partnership we have with the Texas Advanced Coding Center. Oh, that's great, that's great. So they'll be coming over here and it's a four week program for about 25 students. Okay, those 25 now. That's camera one over there. And those 25 students are just behind, right behind camera one. Right. And it's day one. You know, you're welcome. You're giving him the welcoming speech. Okay, what do you say? I said, welcome to the future. Here's your data science route. You're standing next to him. What do you say? It's camera one. Well, I'm going to add my little twist, which is welcome to the family. And you know, we're going to have a fun time learning about data. And we're going to teach you everything we can possibly teach you to get you not only ready for your careers but also ready, you know, if they want to go to grad school or any type of opportunities. This helps in life sciences, yeah. And any interest they want to do, so. So, okay, you know, there's going to be a bunch of courses. And I'm one of the 25 or whoever, many are sitting in the room. What kind of courses? What are you going to, how are you going to divide up my time for me? Yeah, so it's, so when we developed and designed this curriculum or this program, we did it based on, first off, we have to do it based on our mission and values and goals from our leadership and the Marinus Ways at Shyamala University. And, but to also include the development from, you know, researching and talking to our industry partners, some of what other data science and data analytic schools are doing. And from all these factors that we collected, we divided it into about five different areas or components. The first one is, we're going to teach you and have you master the basics of data science. This includes the introduction of data science, give you kind of a boot camp course. You're assuming no prerequisites and no significant. Right, we're assuming you don't, you're just a blank straight out of high school. Straight out of high school. You're becoming this freshman, yes. I do play games, yeah. Yeah, yeah, you can. So we have the basics. We're going to show you what data sciences is about and get you excited about it. The second step will be the advanced courses. So once we get you excited, we teach you the tools in the basic areas. The goal for us is to get you excited and be able to explore it in the advanced realm. Not just to make it like a challenge, like how we hear in advanced courses is like, wow, it's going to be harder or it's going to be more rigorous or it's going to have those components but we're going to look at it from a perspective. Let's explore it. And then so we have those two areas and then the third area or the third component is the design of this curriculum is basically based on project oriented, meaning that we have a capsule at the end and right throughout the process of getting their degree and taking the basic and advanced courses, they're going to be building their projects from the get-go. Who decides what projects? Well, we will work on it with them because part of this is that we're going to have them in turn, they made during the summer at that time. Oh, with companies. Yeah, yeah, with the stakeholders. And then in that way they can get introduced to what's actually out in the wild. What's the real stuff that's going on? Because we don't want to just come up with some artificial scenario because that's not going to be true to what they're going to see. Don't forget my suggestion about the traffic controls. Oh, right, yeah. This would be a real... We'll talk to City County about that one. The contribution, yeah. Listen, Jason is over here. They'll let you in the door, it's a gold mine. So the question I want to put to you actually, Mark, is what's the hands-on aspect here? Do I walk into a room of lecture? Do I walk into a room of computers? Do I walk into a room of modular discussions? How do you teach this stuff? Do you teach it by single individuals, by a room full of students, or do you teach it by groups? Well, it'll be groups, and it's going to depend on the particular course because it's not just being able to understand and rate certain kinds of code and do mathematics because when you're in this kind of field, you don't have to understand ethics. You don't have to understand what is the full life cycle of a data set. What is important for the people that you're doing the analysis for, right? So you have to learn how to talk to people. You have to learn how to explore ideas and then utilize what you've heard from somebody, be able to go back to them and be iterative in your process. So you can't get too much ownership over what you come up with, right? Because what your people want aren't necessarily what you think that they want, right? So you have to be able to interview people and say, okay, what's your problem? Show me what you have, right? And they have to learn how to do this. And then it's like, well, let me see the data that you have because it might not be appropriate for what you're asking. And if you might say, okay, but it sounds like you might actually want this and let me show you why. So you have to have some form of soft skills to be able to talk to people. So it's not going to just be about this technical heavy type training, which is important and what we have built into the curriculum, but some of the soft skills stuff, it's going to be no discussion. You have to work with people. And most of the time they're going to be working groups because you're never often working on your own. Right, okay, very valuable. I get three words out of that that I'd like to discuss right after the break. One is the life cycle of data. Hmm, not to know about that. And the interactive quality. I like to know how that works. And I like to know about the practicum aspect, design thinking of talking to your clients, so to speak, even if you're a student, talking to your client and trying to come up with the real problem instead of the artificial theoretical one. Right after this break, we're really going to drill down. You'll see this Mark Speck and to his left, Rylan O'Chang, yeah. Yeah, we're coming right. From Chaminade, we'll be right back and we'll hear more about the data science program. So if you have somebody in high school who's going to college, if you yourself, you're in high school, this is something you really have to hear. We'll be right back. Aloha, my name is Mark Shklav. I am the host of Think Tech Hawaii's Law Across the Sea. Law Across the Sea is on Think Tech Hawaii every other Monday at 11 a.m. Please join me where my guests talk about law topics and ideas and music and Hawaii Anna all across the sea from Hawaii and back again. Aloha. Hi, I'm Dave Stevens, the host of Cyber Underground every Friday here at 1 p.m. on ThinkTechHawaii.com And then every episode is uploaded to the Cyber Underground, that library of shows that you can see of mine on YouTube.com. And I hope you'll join us here every Friday. We have some topical discussions about why security matters and what could scare the absolute bejesus out of you. If you just try to watch my show all the way through. Hope to see you next time on Cyber Underground. Stay safe. I told you we came back and we came back, right? That's Rylan O'Chang and Mark Speck from Shamanad developing a data science program which sounds very interesting. And so you pointed out to me in the break there were certain things I missed already. Tell me what we need to cover right now before we go any further. Oh, so we thought it would be useful to show some of our brochure work on the different degree paths that we have available. The first would be the BS in data science. And this will be available on our, we have a website called data science.shamanad.edu. Data science.shamanad.edu. Yes, okay. And then this, I maintain the website so this will be published on that website so all this information will be made available there. And what this shows is what is our philosophy for going and following this coursework and where we think it will take you. And what are the aspects that we're gonna be touching on? We're not trying to make computer scientists, we're trying to make data savvy people, right? And these people can be- This is actually much more useful in the 21st century. Yeah, so we want people to be comfortable taking the data science courses but also be aware that you can apply this to any kind of career, right? So we want data competent people who can understand how to use data, ask the right questions or ask people who know how to do certain things and understand what they're getting back, right? There's also other programs that we're gonna be offering that we're working on is, so this is our BS curriculum. Curriculum. Yeah, and so you see it's a 58 credit load. And just like I mentioned earlier, we have the basics, the advanced and then we have our capstone project that students are gonna be taking and throughout the whole process they're gonna be building their capstone and also gaining experience. Capstone, what's a capstone? Capstone is a research project at the end of their... This was in, what, last year? Yeah, it was in their last year. So it's like a culmination of everything you've learned. But as Ryan said, has talked about in the past is that we almost want them to start working on it as they start, so it's like their baby, right? They're working on it. They're adding parts to it. Actually, that's a great, doesn't it? Yeah, so they're adding parts to it as they learn about things. When they get introduced to the different data sets they might already have an idea of what they wanna do. We want them to be able to work on it as they go, and the capstone's kind of the culmination of all that. And I think they'll give them something almost like a portfolio of work that they've done when they leave the work. Sure, for the resume, don't you say? Yeah, they'll turn chips and they'll say, okay, this is something that produced. It's like writing down your dissertation. It defines you. Yeah, and so look, this is what I've discovered. This is the best way to sequence our traffic lights at the intersection so we don't waste fuel and get people upset because they're sending traffic for too long. Yeah, it's called the J Theorem. And this is our workable theorem and this is how it's gonna work. Yeah, and it's gonna give the students their experience that sometimes jobs ask for, that one year experience, two year experience, they're all gonna have that experience. And for instance, if you decide to hire one of our students, you're gonna be familiar with the student. You're gonna be able to understand what to ask for from the student and they're gonna be up and running into either your organization, your company, or even if they want to go grad school. So it's, like Mark says, their portfolio, their direct access and opportunity to be interviewed right on the spot, which is working with our mentors or experts. What I get out of this is it's not just doing the fine work, the embroidery, dealing with small issues or at least code as something that's on your test that you drill down and focus on. You're dealing with big issues. Big issues. Society and general data sets. And in this program, I'm guessing you can correct me, but you learn to have confidence in dealing with the large stuff. So you're walking in the doors of a hospital, for example, and they have thousands of patients and thousands of beds and thousands of medical reports and diagnostic laboratory, whatnot. And you're confident. You can wrap your mind around all of that. You know how to put that in rows and columns. You know how to search. You know how to find. You know how to analyze. This is much bigger than just writing code. Yeah, well, that's an interesting segue because one of our focus areas is healthcare analytics. And we also will be offering MBA, working through the business school, that will help, you know, if you want to get into business analytics or healthcare analytics, they'll be offering an MBA in just healthcare management, but also we want to be able to offer them an MBA in science, technology, and innovation with a healthcare concentration, right? So, and other, there could be other fields, but you know, as an example. That's a good example. Yeah, so, you know, 20% of the most domestic product is in healthcare. Yeah, so we're not just limited to our department. You know, in terms of this research field, we want to be able to reach out to other departments and businesses as a really good partner for that kind of thing. So are you training kids to, I shouldn't say kids. Young adults. Literally, learners. Young adults. Students. Are you training them to work here, to stay here? Or are you training them for the world? Well, where do you like to point them on this program? Probably where they find where their mission's gonna be, but we hope that a lot of them will stay because we know that there's a small pool of people that can actually do this kind of work. Will you help get jobs? Well, that's where we're hoping that our internships will lead to. Right. That's an automatic connection. Right, and our mentorships and through the whole process of them working with expert in the field that this is the direct line. This is a hard one, but you can give me a range if you want. So I finished the program. I have my BA. I'll ask you about the MBA in a minute. My BS, so sorry. And I walk out into the community, whether it's here or the mainland. What kind of salary can I expect to make? Oh boy. Give me a range. It's okay. From 80,000 to six figures. I mean, it's a very enticing area. It depends on where you get into, you know? But yeah, he's right. I've seen anywhere between 80 to 120 within that range. If you go to GlassStores, look up data scientists, you're gonna see some things that are comparable. And that's the starting, yeah, yeah. Wow, maybe I'll actually matriculate that. So what about cybersecurity? Where does that fit in on us? Is this one of those pieces on the curriculum, or could this be a significant feature for a given graduate? So basically it's a compliment. That's how we are looking at it. It's in the development work stage. So the idea with this is to, so since they learn about data analytics and how to use the tools, but we also wanted them to be mindful about how to protect themselves. So cyber attacks is growing on an incredible scale to protect their own work effectively. And the data they're... And the organization they're gonna be working for. So it's taking them beyond the technical aspect, but also looking at it from a very, like the social, the economic, and political perspective. And from a micro level protecting themselves to the macro, to other macro levels of the business that they're working on. And also maybe the state of Hawaii or the states that they're working on, or even the country if they would decide to work for a government agency. If I were an employer or government acting as an employer, I would want nothing less. I would want somebody to protect my data. And it's more and more important to me. To me, this whole thing, if I had to pick one word out of this whole discussion with you both before the show and now, I would say it's relevance to the job market, relevance to the world in which we live, relevance to the economy, the technology. It's creating relevant graduates who are in touch. But I haven't forgotten the question. I wanted to, yeah. One more thing. We are offering scholarships, which is probably pretty important to mention. We have funding through an SFI use grant and that will offer a certain level of scholarship for taking the BS degree in certain fields. And also there's a, and that will fund up to 10,000 a year in tuition and other services. And also the four year guarantee that Shyamada gives for degree. And we also have a whole scholarship, which is granted to us through commandment of schools, which is another fully funded tuition. And they can use that not just for data science, but it is one of the applicable fields that they can go into for their scholarships. This is great. And that, and another NSF grant that we have is called NSF Includes, which is helping to fund our summer institute program, which is four weeks. So the students that come to take that is also fully funded. So how many people are gonna be in this program when you open it up? Well, we would be happy to just start with like 15. That would be a good number for us to start with. That way, because it's new, and so we're gonna have to adjust. But it's also not so small where it's underwhelming. Because you know, diversity of minds, yeah, yeah, and we want it to be successful. So if we get a high level of interest, I mean, we're heading in the right path. We're giving students what they want. We have to serve the students as well as the community. How can I find out more? How could I apply? Where do I go? Okay, so if you go to shamana.edu, the different courses that you can take are available there. The data science program has a hub page that we use, data science.shamana.edu, and that covers most of our data science information, and that will also include information about the degree pass in the summer institutes. So if I come into the program like this, I will stay with my cohort, so to speak, throughout my time at Shamana, right? Yeah, it's like a family. I'll meet the people, and I'll be with them, and we'll study together, and hold on. But it's not necessarily, we haven't necessarily designed it as a cohort experience, not like a nursing program, but if you're all coming in the first time at the same time, then yes, it's gonna be that kind of experience by default, yeah. Yeah, that's great. So back to my question, and we have to end after this as we're out of time, is you spoke about databases that have a life databases that are interactive and databases, I forget the third point, but you gave characterizations databases, you made them sound like they were living beings. Yeah, so what's nice about this program is that it's not just looking at statistics and analytics and visualizations, there's other realms, and these realms are like machine learning, artificial intelligence, right? But I do wanna note, and with our students that it's not just learning the technical side of how to build these great products and solutions, but to be mindful about what they can do, not only the benefits that it can bring to an organization or to themselves, but also the harms and the groups that it might affect. So this is, I mean, this is, there's a lot of things happening with this program, but we're both excited to be the faculty for it. Well, I mean, it strikes me that, it's sorta like when you came over from the life sciences, you brought with you a sense of computer science and data science, but it strikes me that if I wrap my arms around a database, usually of people, but it could be straight scientific things too, if I wrap my arms around a given database of human activity, okay, and then I move on to another enterprise, and there's another phenomenon of human activity there. I will be much better prepared because in understanding this living, breathing, organic database concept, which I'm gonna learn in your program, then I will be able to deal with the living, breathing, organic databases in any aspect of human conduct, and that's really relevant to the 21st century, you know? Our last question is what's the connection with this and design thinking out of Stanford? You spoke before about how the students are trained to find out what the real problem is. Right, so you- They don't accept the superficial analysis, they engage with the client about what the client really needs. Well, that's where the critical thinking comes in, right? So you have to listen to what people are telling you, but you also have to see what it is that they're looking at, and try to understand where's the disconnect, or where's the connect, and that can take time, you have to be patient, and so that the design part comes in iteration. You're not gonna nail it in the first round. I mean, if you do, that's great. I mean, iteration was the other word, yeah. Yeah, so it's iterative, it's patience, it's communicating, and being able to look at what they have and see what the limitations are of what they have. What they might get access to is also important, right? So okay, you have this, but can you give us something like this? And also, no, okay, it's never gonna be perfect, the data is never gonna be perfect, it's never gonna be tidy, it's never gonna be- Humanity is never gonna be tidy, perfect. And so, no, this is the part that we want people to learn, which is just as important as actually crunching the numbers. Wow, it's an expander, a mind expander, yeah. So I mean, what I get from what you say is that this helps you think, and that goes beyond any particular discipline just help you think. Aware and be mindful, and yeah, it's an incredible stuff. Well, good luck on the program. Thank you. I'd like you to see it, I think it's very important to our community. Thank you, Roland. Thank you, Jay. Appreciate it coming down. Thank you, Jay. Well, I really appreciate it coming down. Good work for some of us. Thank you. Aloha.