Educating a data-literate generation

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0:48Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm really excited to follow up today on a topic that, of course, has come up on the show many times and even in our last episode came up very specifically, which is how well our educational system is doing at kind of training students for this next generation of jobs, especially as it relates to data science, AI, and kind of the latest wave of technologies. And of course, I'm very privileged because I live in a town where there's a great university, Purdue University. And so I'm very privileged today to have with me two representatives from Purdue University and the data mine.

1:44That's mine as in pickaxe, not mind or mime. The data mine over at Purdue got Mark Daniel Ward, who is the executive director of the data mine, and also Katie Sanders, who's the chief operating officer. Welcome. It's great to have you all. Thanks for having us. Yeah, great to have you here with us. Well, I kind of teed up a little bit of that in the sense that there's a lot of us in industry who are, of course, being bombarded by all sorts of changes to our jobs, the way we do our day-to-day tasks, whether that be coding or everything from HR to administrative things to even manufacturing jobs and all sorts of things.

2:33All of these are sort of being transformed by this latest wave of technologies. And I know we'll talk more about what the data mine is here in a bit, but you all are kind of uniquely positioned to really think about this next wave of students that are coming into the workforce and particularly how things like data science, AI, et cetera, are shaping maybe how they need to be trained or how they should be prepared for the workforce. Could you give us a little bit of a sense of your viewpoint on that and maybe how people that aren't in the education space could think about some of your perspective on what is changing or needs to change or what you're hoping to see happen?

3:19You know, one thing we see is higher education is changing just the way industry is changing. And the students, of course, still love coming to college and graduate school and all the fundamental learning that occurs in labs and courses, research centers on campus. But maybe more than ever, I don't know how you quantify that, but certainly there's an appetite among students as a generalization to see how the skills really translate into real world work. I think students and of course their parents and families are really interested in what they learn in college and in graduate school is going to get them.

3:57And when they go into the workplace, how that's going to translate into careers more than just that initial job. What's the long run look like? How are they building a foundation while they're here at Purdue for what they're going to do in the rest of their career? So higher education is also wrestling with those changes as well. I think in previous years, you were good if you had an internship, your junior year, your senior year. But now the way things have changed, that's just too late. Yeah. And I know that your focus at the data mine is related to, of course, data science, you know, maybe the wider perspective of data engineering, you know, AI, machine learning, whatever it is.

4:41Why is that set of skills something that maybe students from across different departments or majors should have exposure to in a real world sense? You might think, oh, well, computer science majors, certainly they need to know about AI. But why is this a concern that should be kind of interdisciplinary? Data is just pervasive in industry. You know, aerospace engineers and people on manufacturing floors and out in the field and so on. Everybody's career is being remade because of the pervasiveness of data. So it's not just the computer scientists by any means. It's people want to do predictions and build tools and have sensors and all kinds of automation and workflows that everybody's leveraging data driven tools and methodologies, you know, in their sector of industry.

5:38One thing I learned, I'm not technical at all, so let's preface with that, is that data looks different to an engineer than it does a data scientist. So being able to have students that are able to look at data differently, then they can come up with the best outcome for whatever project they're working on or whatever work that they're doing. Yeah. And would you find, I guess, you work with a lot of corporate partners. This is certainly something that I've seen in industry is often the team that you're working with, of course, is not just made up of developers or others, but there's people from a variety of backgrounds that hopefully are providing input to a problem.

6:21So is that part of what students need to be ready for is talking about maybe technical topics or data topics in a place where there is a diversity of backgrounds? I don't know. Is that part of the goal there? I would say yes, definitely. Giving students the opportunity to work with data no matter what major they're in. We have over 160 majors in our program. we have some actually some marketing projects where marketing teams are working with data and they need assistance with that so you know we can pull marketing students in on that as well as data science students or engineering students whatever they're looking for in order to get that blend and to get all you know different insights as to what the solution could be.

7:12Yeah I think that that is an excellent perspective and I think it leads naturally into some discussion of the data mine itself, because I was very intrigued to learn, like I said, we've been talking about this on the show, but the sort of need for creative approaches to helping students wrestle with real world problems, but also wrestle with those in the context of AI and advanced technology, which is maybe outside of what you could do in kind of a standard classroom environment. And I was interested in what you all are doing, because it does seem like a very interesting, creative approach. And I see on your kind of description of the data mine, there's maybe a whole lot of words that we need to pick apart here.

8:01But you say it's an interdisciplinary living learning community, again, open to students from every college, but these students work alongside corporate partners and they solve kind of real world challenges. So maybe if we just start with a little bit of picking apart that definition, we already talked a little bit about the interdisciplinary portion of this, but just to give us a sense of maybe growth and scale across the whole university, could you help us understand like the, I guess, the scope of students that are involved in this. Katie, you mentioned even marketing and other things. Could maybe, Mark, could you help us understand like how this has grown and how, I guess, pervasive or ubiquitous it is across the different departments at Purdue?

8:57You know, Datamine grew out of a grant we had where we had 20 sophomore undergraduates a year living and working across the street here from where we're located in the Convergence building. And those 20 sophomore undergraduates every year produced a ton of research outputs with faculty all over the campus. Well, one of those early career students at the time wanted to go work with the company. The other 99 worked with faculty. That really set something thinking in our mind about how we could also have research with external partners. So when that grant was winding down that last year, our university administration was so supportive and said, well, what if we open this up to anybody on campus?

9:42How quickly would this grow? And we had 100 students that first year and then 600 and then 800. And no one's required to take this program, the state of mind that we offer, students see a lot of value in it. We sense that the employers, the people hiring them for both internships and full-time employment, see a lot of value in the skills the students are learning. And just coming back to that interdisciplinary piece, each student kind of has their role in the larger team, an engineer, someone from Daniel's School of Business, someone from a data science background or communications or liberal arts.

10:20the students kind of find their niche in a larger team within the larger corporate environment that they're working. And it just seems to be a model that's successful. And could you speak to maybe Katie, the general structure of these teams we're talking about? I think if I remember what you said, as we were chatting before recording that there's, you know, potentially over 2000 students now that could participate in this, which is pretty incredible. It's wide reaching. How are those students from different departments, interdisciplinary, how are they formed into teams? And what does a team mean, I guess, is my question.

11:02That's a loaded question. So we have a couple of different options, right? So we have our one credit seminar course where students are learning different skills, our Python, on SQL, Bash, depending on what level they choose. And then we have the three credit hour corporate partner program where students are working eight to 12-ish students per team are working on a data science project with a company. They are meeting weekly. They have a 50-minute meeting with their mentor. And then they have a little less than two-hour lab that's led by a TA where they're working in agile methodology, you know, trying to solve whatever problem the company has presented.

11:51And is that structure, either one of you could let me know, have there been multiple iterations of that to learn? I'm very interested in kind of a little bit of the backstory here because I'm sure there are others, maybe even at other universities, but other corporate maybe folks listening to the podcast that are wondering, you know, what is the work that's going on in universities to really figure out how to adapt to what students need to do? So was there iteration in that, you know, challenges, things that you tried and then changed? You know, any thoughts on that and kind of the history of how that developed into what it is now?

12:34I guess I could speak to that. You know, in the beginning, we had undergraduate students. Katie already mentioned students from 160 some majors on campus now, but we didn't have graduate students involved. We often didn't have very much faculty involvement. Our relationships with many of these external partners were just starting to build. We have really deep partnerships now with some of our friends in industry, and students will stay involved often throughout their undergrad study and sometimes even choose to stay at Purdue for their graduate study to be in data mine because they recognize what an opportunity this is.

13:11We've definitely evolved and continue to evolve. And in addition to the way we've evolved working with companies, I'm sure we'll speak to this more as we get into the podcast, but we're trying to help other universities around the state and also outside of Indiana to adopt external facing types of models like the data mine in whatever sector of their state, whatever sector of the economy that they're interested to partner in. Interesting. And are there other examples of these sorts of external facing programs that are spinning up around the nation or even globally that you're aware of? Yeah. So right now, we have a partnership with Youngstown State University.

13:57They are pretty much operating their own data mine instance. And then we have Data Mine of the Rockies, which is led by another faculty member here on campus who is working with students at Purdue and Colorado on different projects. We also have quite a few schools in Indiana that are working with us in different ways. We have some that are folded into our seminar, some that are folded into projects, and some that are working with faculty on research projects. We also have a grant that we're working with other institutions across the United States. And it's very similar in that some are working on projects, some are folded into our seminar course.

14:46But we are working with, I believe it's over 60 institutions, if not more.

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16:36Well, Mark, Katie, great introduction to kind of a little bit of the structure of this as it relates to kind of this interdisciplinary and scale component. I'm interested in this next component of the description, this sort of living, learning community. Help me understand what that means and why it's important. You know, early when we had our first grant before there was a data mine, I wasn't aware of how many resources the university puts towards students having a great first undergraduate year at Purdue. And then similarly, as students get ready to graduate the end of their junior year and into their senior year, wow, we have a ton of resources for students making that transition into the workforce.

17:21But there's this murky middle, especially one sophomore year where students are taking their hardest courses in their major, kind of finding their footing. You know, they've gotten through their 100 level courses and they're thinking about how their major is going to transition into a career. And it's not just Purdue, but by no means, it's well documented that that's a time when students start to question and struggle and really wrestle with what's their career going to turn out to be. So we love that many of our students choose to live together and they work together. And while Ms. Sanders and I are at home, you know, with our respective families where all of the staff members close up shop and we have dinner and go to sleep, the students are still working, you know, at 1030 in the evening, they're bonding and they're learning from each other.

18:10And that's when the real work in some sense happens. So by having many of the students live together in a residence hall and study together and faculty often have office hours there and we have our meetings there and the university has been so supportive, we're even building a second residence hall completely devoted to these students' experience. We think we've kind of been able to reshape some of those struggles students would have in an intensive science and engineering environment that is often corporate facing. And they end up feeling at home and finding their friends often who become lifelong friends and such.

18:48It really enriches the experience. Really interesting. Prior to this conversation, if you were to ask me, hey, like what? All right. You're going to transform sort of data science and AI across a university campus. Probably the first thing that would come to my mind is not like build a residence hall. But it's interesting to hear like how this has actually kind of organically, it seems like somewhat organically come up. And maybe that's, is it part of the intentionality that you have of like, there's interdisciplinary students, maybe they're in their own buildings and different classes, but there's this kind of cross interdisciplinary space where they are, but you're also having, you know, like you say, a lot of the meetings there and that sort of thing.

19:43So have you found that the students associate that space? Yes, with their living, but also kind of these projects and what they're a part of kind of in an impact sort of way, like we're together, living together, working on these projects that have a wider impact. What is your, I don't know, any experience of how that sort of sense of place influences the students' view of the projects? Any thoughts? I could say one thing that comes to mind, I think very fondly to our first year working with our friends at Cummins, because they would bring literally sometimes two van loads of Cummins employees all the way up from Columbus, Indiana to West Lafayette, a substantial drive of a couple hours, and they would meet with the students in a room in the residence hall where many of the students were living.

20:35And I could just imagine if I were a student, wow, I have people from one of the largest companies here in Indiana who are coming here to see me. Of course, it puts some pressure on them. But on the other hand, it shows that Cummins trust them. And they see them as the future workforce at a major corporation like Cummins. And moreover, our Cummins friends are some of the nicest people that we work with. I spent a large portion of the morning now, six years later, talking to my friends at Cummins about what we're going to do next year. That model where we are fortunate to meet very good mentors in industry who care a ton about our student success has tended to provide another layer that maybe a regular faculty member might not have in the research team.

21:19Students are getting feedback from people who aren't faculty, and it's really well-informed because it's informed by the industry experience. So I think that's a really key part of why the model's been successful is not something Katie and I or our team is doing, but rather we are surrounded by good friends who seem to believe in this model and even more believe in our students and their potential to contribute and make a difference. I would likely say, I would say two things. One being the students get more sense of a community, right? When you're seeing the same people every day, you're walking down the hall and you're thinking, oh, that's so-and-so, or you like walk down campus.

21:59And I know there's thousands of people on campus, but it's sometimes nice to see a familiar face. And I think that sense of community really helps with the projects because they're able to dive deeper into those personal relationships. That being said, one thing is we have our Indianapolis, data mine of Indianapolis, and the mentors are meeting with students on site because of the close proximity to many of the companies, which has been like transformational like down there, like it's been transformative. The students love it. The companies are getting that one-on-one time. We can't always do that here in West Lafayette because of location.

22:42So that's been really great. And I forgot to mention that earlier, that Indianapolis, we started that January of 24 with Seminar and then kicked off the Corporate Partners program this last fall. And it was very successful and a great experience for students, especially students that are coming in their freshman year. They get that experience firsthand. Yeah, it's been really interesting to hear, you know, just anecdotally similar sort of things on the industry side when we've been engaging customers. And when we're able to like meet them on site, go to their conference room. Right. It's like that sort of interaction has such a meaningful contribution to building that relationship and rapport and the trust that can be built there that is really, really difficult in a in a virtual environment.

23:35And I know many people are good at that. And sometimes there's not the chance to be in the same room. But I definitely think that instilling that in students' minds of the kind of continued importance of some face-to-face interaction and building that relational component is really key. So that's very encouraging to me. This kind of, I guess, naturally leads into the discussion of this next bit of the definition of the data mine that I've been using, which is this corporate partnership. We've talked about the interdisciplinary student teams. We've talked about even how those students are living and working together in these interesting living, learning communities that sounds like are expanding.

24:27What about the corporate partnership side of things? Maybe if you could just help us understand the mechanics of how that works. Because I know some capstone projects at different universities or student projects at some universities, I've heard from colleagues in industry, it's like, sometimes they can be viewed as, oh, we're going to, it's good that we can help these students, but we're not really getting, You know, there's no benefit to the company, but maybe it's good that we can help the students. But this is definitely a different view, or at least what I've seen from what's going on at the data mine, a different view of what student projects could be.

25:10So maybe in a general sense, how do you think that universities and corporate partners can actually engage together in a way that is mutually beneficial, yes, to the students and their future career, but also to the corporate partners? I would say that a lot of the projects that we get, they're not mission critical projects. The experiential learning piece is not mission critical, but they are projects that you'd really like to get to, but you don't have the time to get to. You don't have the manpower, things that have been on the back burner, things that need revived a little bit or something that you've always wanted to do, but just haven't had the time to do.

25:56Those are the type of projects that these students work on. And the students are so smart and innovative. It's crazy to me, honestly, because when I was that age, I was not doing that. And like, so it's just been, you know, every company has a different reason why they are coming to the data mine. Some of it's a talent pipeline. Some of it is like, look, I just want to hire some interns. And this is a great way to get connected with students. And you already know their work ethic because you're working with them for the nine months prior to maybe them starting an internship. Some people, you know, some companies are, well, we have this project.

26:35Let's just try this and see if we get it, see if it works out. And it's awesome when it does work out, right? So there are different reasons why companies will join. And, you know, our retention is pretty good as well. So I think that speaks to the work that the students do. I was just going to say, I feel like we're growing with some of our companies, you know, like before I came into the podcast, I went down the whole office suite for Bex Hybrids, which is just on the catty corner from our office. And, you know, when we first started talking with Bax, I think they had on the order of 300 employees and they just hired their 1000th employee this year.

27:14We're just a very, very small part, but we feel part of that growth. We really feel that in our team and they always make us feel like we're part of the family at Bax. When you were first broaching this question, kind of what hole are we basically filling with some of these partners? You know, yesterday I was in Chicago and not naming any names, but I met with a company for the very first time who has 75 employees. It's not a huge company, but the challenge, the gap is they don't have any data scientists yet. They have one intern and the intern is just amazing. But if they had full-time folks on staff who could leverage AI and ML and do some of the predictive analytics and so on, they recognize that they could go from being one of the players in that industry to becoming a leader.

28:01And if they take a chance on us at data mine, they can try the students on for size and see what kind of research and development they can do. And it's not just there being nice to the students or helping them with the capstone, like you said, Daniel, it's more like, okay, we actually sense there's a real opportunity to advance our business here with these folks at data mine. So when we're able to make impacts like that, and the students work is really valued by the company, I think that's when we've hit our sweet spot. Yeah, that's great. And just to give a sense, I mean, I know there's probably some corporate partners that can't be mentioned for confidentiality reasons or other things.

28:37But either of you, I don't know if you can give a sense of kind of how many corporate partners you either are or have worked with and kind of the range of sizes and just a sense of industries maybe. because it is interesting that similar to what you said, Katie, I remember being an undergrad and doing projects. I was not exposed to working with actual real companies outside of if I did pursue an internship or something like that formally. But as part of the actual university side, I wasn't exposed to that. So I really, just from a personal standpoint, had no idea how a lot of industry even operated or worked or that sort of thing.

29:25So I don't know. Could either of you give us a sense of that, the types of companies and numbers, that sort of thing? Last year, we had 88 projects with about 60 different companies. And then, I don't know, Mark, if you want to maybe take the rest of that. I know that part. I know the numbers part. You know the data at the data mine. Yeah. Yeah. I don't know how to work with it, but I know the numbers. We try to be really affordable. I mean, we have not doubled our number of companies in the last year or two, but what we've done is we've gone a lot deeper in our relationships with the companies.