[00:00:30] Excellent. Uh, so I want to say hello to everyone. I'm so excited to be with your, be with you this, here this evening, and, uh, to this virtual open house for the data science program at Utica College. Um, this is something that, uh, it's very, very exciting, uh, for me to talk to you about. And, uh, please, uh, if you have any questions along the way, uh, please go ahead and enter them into the chat. Uh, we'll try to answer them as quickly and as promptly as we can. And there's always gonna be time at the end to, to hit, uh, all the questions or we'll definitely get back to you. Um, Alex with admissions is definitely monitoring the chat.
[00:01:00] And so, uh, with all that, uh, I just want to show, this is a picture of our library at Utica College. I think it's a great place to start. My office is right across the way. This is a great picture of it, but this is the picture I prefer. Uh, it's a picture of me right up front. And, uh, you know, i- it's a r- Utica College is really a, a wonderful place and this library and, and it's, it's really just sort of, uh, uh, one of those things that I just like to showcase that Utica College is definitely a lovely, lovely college. And we have a wonderful undergraduate population and a wonderful graduate population. Um, it really is founded after, um, uh, world War II to help, uh, uh, the GIs coming back from, from the war. And that was actually its founding was in 1946 and I'm really, really connected to that, uh, because, um, 'cause I'm a veteran, uh, and I'll c- I'll touch on that in a little bit, but here's our agenda for this evening.
[00:01:30] Uh, we're gonna do a quick introduction of the speakers. Uh, we're gonna talk about data science and car- the career outlook. Talk a lot about the program, uh, what makes Utica College unique? And then, uh, we'll talk, we'll talk a lot about what it's like to learn online. Um, real quick about myself. Uh, some of you might've seen my, my bio online, but I, I went to United States Military Academy at West Point. I graduated, uh, before 9/11 and, uh, right, right in the middle of a flight school, uh, that all took place. And I, I spent about seven years in the army.
[00:02:00] And I bring that up because, um, I, I transitioned from this really a high, intense job, uh, of being an army aviator and then transitioned into, uh, academia, and I got my, both my graduate degrees, uh, with the help of the GI bill in North Carolina.
[00:02:30] And then I went to work at a hospital. So I kinda transitioned again from academia and the data-driven work I was doing, uh, into data-driven work in healthcare, which definitely had its own learning curve. So I'm used to these, these transitions. And then many of you are probably gonna experience that once you start down the road of becoming a data scientist, uh, hopefully with us at Utica College. So, and then, then after I was at the hospital, then I, I found this job and I had to tell you that, um, I'm very, very passionate. I love my job. I'm one of the few people in the world that absolutely love my job. I love teaching. I love teaching graduate students and I'm gonna really, really enjoy being part of the journey, uh, with you.
[00:03:00] So with, with that, I want to introduce ... Oh, that's a picture of me real quick (laughs) back in my Blackhawk. Uh, uh, when I was a much, much younger person.
[00:03:30] Uh, I want to introduce real quick, uh, Sean. Sean is ... he's really a w- a wonderful person and he's such a hard worker. And, uh, he took, uh, a data science course as an elective when he was in the MBA program and he got hooked and it was really, really neat to see his transformation, uh, from, uh, this data novice to this data guru. And it happened right before my eyes. And that's the one thing I absolutely love, uh, about teaching. So, uh, with that ... now, with that brief introduction, uh, Sean, I'll let you, uh, turn it, I'll turn it over to you to introduce the rest of yourself.
[00:04:00] Thank you, Dr. McCarthy. Uh, like, like Dr. McCarthy said, um, I s- entered into the MBA program. I basically, at the point of my career where I wanted to ... I knew I wanted to do something else, but I wasn't sure what it was. So I jumped into the MBA program, um, really liked it. And, and like Dr. McCarthy said, I took an elective in the data science program, uh, and ended up getting my, um, concentration for the MBA in business analytics. I loved it, (laughs) and when I finished the MBA, I kept going and I also kind of did a career transition as well.
[00:04:30] So when I was within ... when I started the program, I was a supervisor mostly doing administrative tasks. I wanted more of a technical skillset and to actually start doing more work again. Uh, the MBA started that and the data science really finished it. So I've, I've transitioned from being a manager, working on administrative tasks, to working as an economist in a data-driven decision making. And then now, currently, uh, working as manager of quantitative analysis, um, at an investment firm. So it's, it's, it really, same thing did a, did a career path and, and Utica really helped me get there.
[00:05:00] And, and it's really a, like a common, you know, theme where, you know, people are really striving to transform themselves, uh, and, and see data as a pathway for that. And I'm very excited 'cause it's transformed my life and I, I see it happen with each of my students. It's very exciting. And again, I want to acknowledge Alex, uh, with admissions, who's here with us as well. So, uh, the three panelists here today, and I wanna thank, uh, each of you for, for coming. Um, so real quick. The, uh, I want to talk real quick about, uh, the data science environment and this, this slide helps us understand all the kinds of different flavors that are out there because there is a fla- there are different flavors and there's definitely a hierarchy. And I'm so pleased that, uh, Utica College, we offer a master's of science and data science, 'cause I really do see it as this kinda big overarching, uh, science, that kind of all the other ones kind of fit into.
[00:05:30] And, and that's the neat thing about the data science degree from Utica College is that it can be an almost anything. A lot of people come in here, they want to do in cyber. They want to do in business. Those are all very wonderful and applied, but we have a social science aspect to it. We also ... and if you want to do something totally on your own or healthcare administration or, um, financial crime, those are all wonderful pathways that you can do with the data science program that we have at Utica College.
[00:06:00] Uh, and there are a lo- a lot of those other flavors are, are powerful and you'll do some of those things too, but it's one of those things that, um, if you learn the science side of data science, 'cause you're, like the first class you're gonna come in as a, and become a data scientist, you know, and that's very, very important to us, uh, you're gonna start modeling in the first class.
[00:06:30] And then, and then we're gonna move on and you're just gonna get deeper and stronger in that, and as you progress along your journey to become a data scientist. So, um, so just to talk about the career, I'll look, a lot of people really like to look at the dollars and cents of it. And, and that makes a lot of, you know, a lot ... it makes, that make sense because, um, you know, there's definitely an investment you're making in your yourself to take on a graduate degree. Uh, but the one thing I tell you about this is that, uh, especially in this last year, a lot of people have seen that the data science space, uh, hasn't shrunk. Some people, I do know some data scientists who've lost their jobs as a result of the COVID recession, but the ones I know, uh, lost it specifically in, uh, industries that were very, very hard hit.
[00:07:00] Uh, the one in particular that I'm thinking of was in the hospitality industry. Uh, but he, he was hired pretty quickly thereafter. Uh, there's also, this is one of those great jobs that you're part of a team, doing a lot of investigative work, and you're doing a lot of collaborative work and like creative work, which is what I really like about it is the creativity, the collaboration, a- and, and really just finding something new, uh, to help your organization, you know, either, uh, helping the bottom line, make things more efficient, uh, or, or a new insight that can really transform things. I really, really is a lot of fun.
[00:07:30] Uh, and that's the one thing that keeps me, you know, seeing my students grow, but also, you know, just, just digging in, in, in and finding things. Uh, that's really, really exciting for me.
[00:08:00] Um, so there's a lot going on in this slide, but I wanna point to the, the three circles on the right, the Venn diagram on the right. We all come in to data science with what we've, what we have, you know, from our, all of our life experiences. That's the one thing that's so wonderful about graduate school. Um, most of our graduate students, all of our graduate students come in with so much rich experience. They have all bunch of domain knowledge. They might even sh- they might wanna shift domain, but that's still gonna be some very rich knowledge that come in, some rich experiences. And some come in s- with a lot of math skills, some a little bit less, and somewhat some computer science skills, maybe it's a little bit less, but our goal is to push you towards the, the middle of there, where that unicorn is, which I always think is funny. Uh, but it's, it's one of those things that we wanna strive to develop ourselves as much as we can, uh, to, and to build those spaces to where we can become the strongest data scientists we can.
[00:08:30] Now, the domain comes from the, uh, the specialization that either you're already in, or you'll take on it as part of your electives. Computer science and IT is baked into a lot of the classes and the math. I mean, we have one specific class, but again, it's all, it's baked into a lot of the curriculum where you're gonna get a lot of math along the way, and you might not even really realize that you're getting math. Uh, and then we have six courses. Uh, they run pretty sequential although s- you'll get your electives along the way.
[00:09:00] And then we have the, the social ... we have the specializations at the bottom. Again, social science analytics, which is very special at Utica College, ver- pretty unique. Business analytics, cybersecurity, and, uh, financial crime. Those are award-winning, uh, programs, uh, acknowledged by the DOD, the NSA, very, very strong.
[00:09:30] But the last one, I want everyone to strongly consider, a lot of people come in to Utica College's data science program, and they, they've assigned themselves to one of those. But the general one that I think is actually perhaps the most strong, because it allows each student to pick the four electives that best serve them. And it can be some in healthcare administration, it could be one cyber and two business, or two business to cyber and healthcare analytics or some- and something along those lines, uh, really allows you to tailor what you're doing. Uh, so Sean, w- what do you ... you had some stuff that you wanted to mention about th- this curriculum.
[00:10:00] Yeah, definitely. So I think just, just first off [inaudible 00:09:54] and I was a, uh, undergrad for, in economics, but I, I was, I would not say that I was strong in, in math or, or anything like that. I had zero coding experience and I honestly, didn't, wasn't a wiz at, at, at, uh, Excel. Like I, I went and do that and I still, you know, open eyes and attempt to learn and, and advance. And I think, um, I've found a love for math, even within the data science realm. Uh, Excel has (laughs) obviously gotten a lot better, uh, and coding now, too. So it was all skills that I either didn't have at all, or I wasn't comfortable with. And, and by the end of that, I felt great. So that's just one aside.
[00:10:30] And then all of these courses are, were great, um, the core courses there on the left. So I've, I've used pieces of these courses in two j- so, yeah. So when I was in the program, I ended up getting a new job that was focused on analytics, uh, before I graduated. And since then I've gotten another job. Um, that's even much more focused in data science and in quantitative analysis.
[00:11:00] So, um, I've, in both of those positions, I use pieces of each of these classes, uh, and, and I really lean on a lot of this. I still have some printouts from some of the coursework in this that I, that I lean to. Um, obviously, you know, there's a lot, you're gonna learn a lot and, and, you know, no one can remember everything. So it's always nice to have these, uh, the courses, the materials to lean back on so you can look at.
[00:11:30] And then just job-wise, and I was able to get (laughs) two jobs just from this program, um, employers are looking for this. Almost every listing that I've seen has some sort of data component. Um, you know, obviously it's, most important thing is to learn all of this, but these are a lot of buzzwords. You know, employers are looking for data mining, they're looking for machine learning, data visualization, uh, and data science. So all, everything you see here is gonna help you when you're in the program, and then when you get out.
[00:12:00] Thanks Sean, and the one thing I wanna, I wanna, I wanna, I me- I meet with every student right at the very beginning, and this is the point I was gonna make a little bit later. I meet with every student at the very beginning, we talk ... we don't talk about data science. We talk about your life goals and how we can make this program fit. And I'm gonna steal a little bit of my own thunder here, and I'll tell you that, uh, most of the time I advise students to start looking for a data job after the first semester, while they're in data mining and machine learning. That is when you become a very powerful, uh, you're, you're very marketable at that point in time. You've had, by the time you've finished machine learning, you've had four, six to the core. Uh, and then after that, it's almost all downhill. You know, you're alr- you're already almost over the hump.
[00:12:30] Uh, you've learned a lot. And then through the electives, through the capstone and the data visualization class, uh, those are really, uh, meant to help you home.
[00:13:00] So again, I, I tell students, you know, maybe not start looking for a job with the very first semester, although I've had students be successful in that as well. (laughs) Actually a- amazingly successful, uh, some, some of my students, uh, but at the same time, the best ti- you know, don't, don't wait till the end. That's, that's the key point is actually to, to help market yourself early. And you're gonna have a lot, a lot of skills right away. Um, you're gonna be a data scientist. You're, and the key thing for each one of these is to recognize, um, that, you know, data science is, um, you know, well, we can cra- cram into all these different courses like this much. We really try to maximize what we can teach, but data science is this huge. I mean, it's just, and just getting bigger.
[00:13:30] And, and how can we, uh, give you the strongest foundation to be able to, for you to land anywhere and to go, okay, now, you know, you know, I have this foundation I can do anything based on that within data.
[00:14:00] Uh, and that's, what's really exciting is that there really is, um, this foundation is really, really strong. I have to tell you that the programming language that I learned in graduate school, um, wherever, you know, at the VA, they didn't have it. A- and so, but I had a strong enough programming understanding with SaaS to pick up exactly what they were using. Uh, the same thing, uh, you know, program languages come and program languages go. So, uh, the key is understanding what's going on, uh, and how, and how to apply it. So I'm gonna go into this next slide.
[00:14:30] Um, so the o- there's one ... we've talked about this a lot because this is actually a very important, uh, component is the thesis or the capstone, which is we, under the umbrella of culminating, culminating academic experience. And you have to have one as the thesis or the capstone. Another thing that makes you different at college is data science program, pretty unique in, in the nation, or perhaps in the world, is that we offer a thesis option. Uh, that's not for everyone. There, it is a little bit more robust. It does require a committee. It does require some extra work along the way. Uh, but it is for those students who wanna go on and get a doctorate degree or working in, or want to work in a research space like Think Tank, something along those lines, or organization that's really, really more research-driven.
[00:15:00] Uh, most students don't offer that, but i- it's really exciting when they do. Um, there's some really amazing things that have happened and are happening. I have two thesis students that are gonna finish the semester and they're gonna defend just like I defended my thesis, uh, with the committee that's jus- just like the community I had. And we really, uh, strive to support those students, just like we strive to support all of our students. The second option which is sort of the default, uh, you have to, you have to really want the thesis to do it. It's one of those things you really want it, but most students opt for the capstone. And we're really beginning to ... I don't wanna say we're beginning. It's, it's well-established that we have these wonderful industry mentors, these companies that, uh, do three things for us.
[00:15:30] They provide, uh, data, they provide a second readers, that domain experience, and they also provide the problem statement.
[00:16:00] So all of a sudden, uh, w- we have Fortune 50 companies that are partnered with us, we have a major, uh, bank, uh, and, and I would tell you more specifically about them, but, uh, they do ask to re- uh, because the data is so sensitive, uh, that they're sharing with us that, uh, that we, we hold on to it. But we have banks, we have, uh, Fortune 50 company that's partnering with us and they give us these applied, uh, capstones. And the students, uh, are able to take this foundation that we, I just talked about and really help these organizations through some really neat iterations, provide a new lens. Uh, and these companies are a hundred for that. And it's a great exposure to not only, uh, this really complex, uh, problem in this really rich data, uh, but also, uh, the second readers who are just phenomenal and, and mentoring and guiding, uh, these capstone students. So it's a really, really fun, uh, activity.
[00:16:30] And we, those start, uh, we have those every, every semester. Uh, and I, I have a class that starts at two weeks. I'm just so excited about this next research, uh, capstone experience. So, but we ha- I, these are just some examples and there's one there that, uh, I really particularly like. It was the El Nino's effects on commodities markets. And it was really, really fun, uh, working on that because, uh, again, I'm not a commodity guy, but the person who did it, uh, was, and I think Sea- Sean, you have some more information about this, right?
Sean Regan: Yeah. Yes, I do. So this, this was my capstone project. Uh, I basically wanted to take a blend of business and, and blend it with a different type of data. So I took weather data with El Nino's effects and, and pulled a lot of data from NOAA and, and other places like that. And then, uh, looked at it for ... I grew up some commodities.
So taking, um, uh, commodity prices for wheat, corn, and soybeans, uh, and looking at how those were affected in different areas of the country based on El Nino's effects. So it was, it was, it was great. And it was, it was something Dr. McCarthy mentioned earlier, like a great part of, uh, data science's like the exploratory nature of it and be able to drill down. So that was a huge part of this. I, I had read a article years prior to it on, uh, a traders who were using weather data in Brazil to trade coffee.
[00:18:00] So th- that, that kind of sparked it and I wanted to do something similar to that. So it was extremely good learning experience. Um, I (laughs) but to be honest to that, I, I, my geographical area was a little too large, so the findings weren't what I was hoping, but, uh, also important part not to, uh, fudge anything. So I, you know, I went with what I found. Um, and then this, this actually played into, uh, my current position. Um, this was something I had on my resume. And, uh, we talked about it in, in depth on my interview, uh, I really think it helped me to get my current position.
[00:18:30] And so the neat thing about this is that, you know, there's the thesis if you want it, there's the capstone applied experience if you want it, but it's also exactly what you want it to be. And I tell everyone whatever you're interested in, you know, let's go for it. I have, I have people who did, uh, clustering for NBA, uh, teams and players. It was really (laughs) really, really neat analysis, but that's what really go- it got them excited. I had another student who did something really fun with, um, it, with the NFL. They wanted, they wanted be able to predict who was gonna win, uh, each week in, in the NFL. And they actually had a pretty good model. Uh, not one that I necessarily would, uh, put a bunch of money behind, but knowing the limits, there were, there were some opportunities. So it's one of those things that this can be exactly what you want it to be.
[00:19:00] Um, I have to tell you that the ... I, I generally am pushing most students towards the applied one, just because it is so rich, but again, this can be just like the electives, something that you can really customize. Uh, and I have, I have students who did a whole bunch with, um, um, uh, what was it, the, uh (laughs) uh, forgive me. It was the, um, uh, like a bed now that, that had breakfast. Airbnb, and they just scraped a bunch of Airbnb data and they found an Airbnb expert and they, and they, they had this really thoughtful Airbnb analysis.
[00:19:30] So this, again, that's what they were excited about, and, and we were able to support them, uh, doing a fun Airbnb analysis. So, uh, so it's pretty exciting. This is, when, when you, when you think about your graduate experience, it's gonna be a courses and then just capstone has to be one of the main things that's gonna go on your resume.
[00:20:00] And that's how it is on my resume. It says, you know, this is my graduate degree. This is what I, this is my thesis, and it's written out, the title's written out and it generates exactly like Sean said, a whole bunch of conversations that you can have, because there's so many layers to that. There's so many layers that ha- that go into that full project scope. So, um, moving on to this next slide. Oh, I know. I just wanted, you know, I highlight here the applied industry mentors. So w- what makes Utica College different? It's, it's hard to say exactly, but up here is our list.
[00:20:30] And I really have to say I'm really, really proud of our faculty. Um, what's really, really, uh, we have faculty that really, really strive and really, really dedicated to helping you learn. And th- this really dovetails really, really nicely into the next one. Um, the class size.
[00:21:00] Uh, if there's a class size, especially in the core where you're, you look around and there's 22 students, that's the biggest class you'll ever be in. Um, most of them are capped at 20 and, and we work really, really hard. The faculty have smaller class sizes to provide more engagement with you, better feedback. Um, this is in a synchronous degree, but that doesn't mean you don't see faculty. Um, I have open office hours. I have meetings with students. Again, I th- I think I mentioned earlier that I me- I meet with every student, uh, at the beginning of every, uh, five Oh one class, which is the introductory class that everyone takes. Um, you know, just to talk about goals. Um, we really work on, uh, making our curriculum cutting edge, and we're actually constantly, constantly updating it.
[00:21:30] Uh, believe it or not the, the curriculum that, that I'm teaching now. Uh, I think Sean graduated a year ago is, is different enough where Sean would get it, but it, you know, s- the classes are different. So we have some career services that are available. Uh, and, and we have the, we have a agreement or we have, uh, we use the handshake to help with opportunities and networking. Um, lots of professional networking.
[00:22:00] I have to tell your class colleagues are really, really good, and we have a really good LinkedIn group with that. And the last thing is we access the licensed software and everyone kind of goes, "Well, what's up with Alteryx?" Uh, Alteryx believe it or not is, it's coding. It's coding on an enterprise proprietary system, um, that allows organizations that use it, um, to, to really, uh, quickly do, uh, explainable code real fast.
[00:22:30] And instead of bringing in a whole bunch of syntax, you know input colon, uh, you know, this file, this data, connect to this database, what would you syntax and Python or R which of course, those are things that you'll learn. Um, Alteryx allows us to get right to modeling right away.
[00:23:00] And it's actually something that's becoming more and more and more common. When we started with Alteryx about three or four years ago, like four or five years ago, um, it was very, very, uh, non standard for any organization to have e- enterprise level software like that. Now it's almost, it's so common. Um, you know, RapidMiner is, uh, an equivalent, uh, Datacoup is an equivalent. Uh, Databricks is an equivalent there. The enterprise systems are just massive. Southwest Airlines operates almost exclusively with Alteryx. You know, they have like 630 licenses. Uh, uh, the BNY Mellon, which is a huge financial organization.
[00:23:30] They, they have a whole bunch of different software, but they use Alteryx. uh, Ernst & Young, uh, major for, uh, they use, so they use more, uh, but they definitely use Alteryx. Uh, so it's one of those situations where Alteryx, it permeates, um, a lot of major organizations.
[00:24:00] And of, and Tableau, uh, Tableau is one of the industry leaders in a database. And so you'll get the full licenses for those, um, throughout the, the course. So as much, or as little as you'd like, and the neat thing about it is that when you graduate, you can actually, y- your license usually doesn't expire right away, so you can also keep using them, uh, throughout. So, uh, do you have anything, uh, d- how do you feel about Alteryx Sea- Sean?
[00:24:30] Uh, yeah, no, I, I love it. I think it's, it's, it's been the, it was it's, it is the best, uh, data science machine learning software that I've found, um, mix of, ease of use, the, the drag. I, drop drag and drop just is night and day from, you know, like Dr. McCarthy said, having to type everything out. Right? Um, you're gonna learn how to code as well, but if you're using Alteryx, you, you have the skillset, uh, to be able to work more efficiently. Um, and you have just m- much more wide range of, um, platforms and algorithms and all types of things to use. Um, yeah, no, it's great. And then if you want the code itself, you can open it up to see what, what the code was behind the scenes. So you can almost teach yourself a little bit more with our Python by seeing what goes behind the scenes and Alteryx. Yeah-
[00:25:00] Uh, yeah, a- Alteryx uses R and Alteryx uses Python, so you can see, you can see what's going on. It's, it is pretty exciting. And the one thing that people, uh, if you don't point it out, it can go missing but when you're doing data science with Alteryx you're coding, but instead of using the syntax, you know, four lines to bring in a documented format, or, you know, some, uh, data file in to format the, the cells, use a tool or two, and is that, it's drag and drop. And organizations are using it because it allows a tremendous amount of understanding up and down the hierarchy from, from the data tech who is, you know, ma- maintaining the database to, uh, data analyst, to the data, scientists, to the managers who just have no idea about any Python scripting.
[00:25:30] The explainability that happens is a lot of the true value that comes from Alteryx, but it's programming. It's, it, it really is honest to God, it is science programming. And you'll see that in all the enterprise solutions that are out there, Datacoup, RapidMiner, uh, there's just, there's just so many of them out there. Uh, we, we use Alteryx because they were one of the first and they're, and they're fabulous. They're, they're, they're quite good and they're growing, uh, and they have a great ... they really love you like a college student.
[00:26:00] Um, so real quick, um, some l- some highlights of, of learning online at Utica College, you know, it's really great academics, uh, really accessible faculty. That's really the, the key. Like the faculty are accessible. Many of you might have done some on- other online courses. And actually one of the most consistent feedback I get from from students is like, man, I've never had so much contact with a faculty member before, uh, in any online class.
[00:26:30] And arguably, uh, and some on ground classes, you know, you can go, you can sit in a class, but the connections that we have, uh, the feedback you will get, uh, in the, in the, whenever you reach out it, you know, we're there.
[00:27:00] I've, I, I meet with students, I've met with students early, early in the morning. And I have met students (laughs) late, late at night, uh, to help, uh, them overcome things. I me I meet with them on weekends. I strive not to, but sometimes that's when, uh, people really need the help. And so, um, I've actually, uh, I've been, I've been on, uh, on a, a small trip and, and I take breaks to meet with my students who need, who need help. Uh, so flexible schedules, there's online program is meant to kind of merge into your life. Most of you, uh, have jobs. Most of you probably have families of one sort of another, um, this is, uh, a program that's meant to have some flexibility. Uh, I know Sea- Sean, you, uh, maybe you can talk a little more about that.
Yeah, definitely. So when, when I started the program, excuse me ... when I start the program, I was, I was ... actually, even before I started, I was looking at different programs. Um, I, I took a class in person, um, and I had, uh, I had to commute ... I had a 45 minute commute each way. Uh, sometimes, you know, we get started late ans it let out early and it just wasn't, I, I just, I found like that's what really opened my eyes to online courses that you don't really need to be in person anymore.
Uh, and I took another course that was online, but it was, there wasn't any interaction with classmates. There was hardly an interaction with the professor. So then, you know, then I found Utica.
[00:28:30] And like Dr. McCarthy said, uh, the interaction between faculty and students is, is, is amazing. I mean, I think I was definitely one of those person people, um, emailing on weekends and getting responses back within the hour. So I could get to you to work on something that I was, I was working on. The interaction with the classmates as well was great. So, um, you know, people are spread out all over the country if not the world and some of the courses that I was in, um, we all talk multiple times a week on chat boards and we're working on assignments together and group projects. Um, and one of my closest classmates, I'm still, I still keep in touch with, she was out in Kansas city and I was in Boston in DC. I, we, we talked multiple times a week and work on, work on, you know, coding together and things like that. So, um, the interaction between faculty and student and student and student is, it was great.
[00:29:00] So I have to tell you, it's one of those situations where, um, there's, this is one of those, you know, some people are concerned about an online degrees. Some people are worried about the interactions. It really is, what, what you put into is what you get out of it. You reap what you sow, your interactions with your class colleagues can definitely happen, uh, or, or not. You know, there's people that, that might make their, their pathway through an online program and, and s- and not actually have a lot of engagement.
[00:29:30] But that's, you know, but the other times you, you have people that have tons of interaction. I have s- um, when I set up groups in 501, and interestingly enough, um, there's been multiple situations where those, as they moved their way, the, the students move their way through the c- the program, I have them together in the capstone, uh, and they're still hanging out once a week, uh, uh, because that's the sort of a r- little routine they set up.
[00:30:00] So, um, so I have to tell you the, uh, so we strive to be flexible. Uh, the, the goal is to work it into your life. Things happen, you know, two years to get a degree, people move, people, change jobs, people find the significant other, people might lose a significant other. I actually had a student who, who's partner died. Uh, we find, you know, the, the person wanted to continue. We found a way to, to help them, uh, through these very, very challenging times. Uh, life happens, uh, and we recognize we're just part of your journey and we strive to support you, uh, as you're, as you're on that journey.
[00:30:30] Smart thinking is one of those situa- uh, things, actually, this is something new that Sean doesn't even know. Uh, th- th- there's Python tutor- tu- uh, tutoring in smart thinking now. Uh, it's very exciting that it used to be like a writing, something other, that we, we really help students, uh, who needed extra writing support, but smart thinking now has Python tutoring built into it. Uh, it's pretty, pretty helpful. There's discounts for military law enforcement, federal employees, and some corporate affiliates. Um, Alex will have all that listed for you.
[00:31:00] Um, now I wanna talk a little bit about the Engaged Learning Management System. Uh, you can access that from any device. Um, a, a computer is probably the best, uh, but the session there's, there's interactive sessions that, that we, a lot of times we record them so that if you can't make them, uh, you can, uh, you can definitely get all the information you need. Uh, so let me go to the next slide and let me show you what it looks like.
[00:31:30] So this is actually my current course teaching data science 501, and you can see it's organized, uh, into the eight, eight modules. Each module has a theme, uh, and there's announcements, and which, and I really strive. Uh, you know, it's one of those things that, uh, nothing's perfect. Uh, but, uh, as you're striving to keep curriculum, uh, updated, some- sometimes, uh, uh, uh, s- there, there's new, there's newness that, that g- that comes in, uh, but really strive to make things extra, extra, uh, and ambiguous. Make, make sure they're very direct, and you can understand exactly what, what we want you to learn. And it's broken up to, you have learning activities, you know, you have learning goals and activities, and then there's, uh, you know, something to turn in usually each week. Uh, there's actually 30 something to turn in every week.
[00:32:00] And, and it's very interesting, um, in that online space, your, your week starts on Monday and everything is due Sunday night. So it gives you all a week to kind of prepare, do some work on the weekend, uh, it's due Sunday night. Uh, and then I, I work like hell to, um, to grade everything on Monday or Tuesday, sometimes Wednesday. Uh, but I strive to give that feedback right away. And most, most faculty do that.
[00:32:30] It's really quite amazing, uh, how fast, uh, because w- when, when you have work, that's due the subsequent week, you wanna make sure you have that, that strong, deep, thoughtful feedback that enables you to be successful, uh, on your next iteration. Uh, most of this work is project-based, um, there's, there's, you know, there's, if there's a quiz it's almost worth nothing, uh, meaning it's really there just to help you see what's the important part of readings. Uh, these are all generally, uh, uh, dedicated to help you, uh, by, by doing the work by, by actually, uh, uh, practicing on, on real data. So I don't know, you know, Sean, is there anything you wanted to add to this, uh ...
Uh, yeah, it's that the, the set up was, was very intuitive. Um, it was, it was nice, the the courses or each class is eight weeks and not 16 (laughs). That you're, you're learning a lot in the eight week period, but like, it doesn't get stale. Like each, each week you're building upon the previous week, um, and, and you're by the end, you're, you're ready, right? And then you're m- ready to move on to the next one. Um, s- sometimes I think, and, and, you know, a class that's 16 weeks, it's drawn out too much. This is definitely not the case.
Like you're, you're learning something each week, you're building upon it. Um, the system itself is easy to use and, and you're, and you're good to go. So, yeah, no, I re- I really enjoyed the eight weeks set up and, and using the interactive system.
[00:34:00] Yeah. And I, and that it's, and like when, when I was in graduate school, I, I, my six credit hours was full time, but I did two, three credit courses for 16 weeks. Here, we have it, you know, it's one three credit course in eight weeks. It ends and the next one starts, and then that went into the next one starts. So it's the same load a- as, as I, as it was when I was an on-ground student. So, um, just, there is a level of intensity that might come from that, uh, you know, but at the same time, it's just, it's just one class and it allows you to focus. You don't have to like, okay, am I, am I reading for this class? Am I reading for that class?
[00:34:30] Am I reading for one class? Uh, and that, that level of focus, I think, is there's a lot of value added in that. So, uh, we're actually coming to the very, very end of our presentation. Uh, and are there ... we love to hear if there's any questions.
[00:35:00] Oh, uh, Andrew, uh, so how much time is there between classes? Or let me, let me go to the previous slide on this. Um, what ends up happening ... what ends up happening, you see down here, like, uh, classes end on March 12th. Uh, that's when, uh, the final project is due and data science 501, uh, and then there's a weekend and Monday data science 503 starts. So it is, it is, it is, there's a weekend between those classes and that's usually the case every now and again, uh, between the spring and the summer, there might be a week. Uh, and there's always, there's always, always winter break, which is three to four weeks between the end of the term and December, the beginning of the term and, uh, the fall. I'm sorry, in the spring.
[00:35:30] Yeah. Are there any other questions? I'd, I'd love to, I'd love to talk more. The, uh, there is, uh, I have a ... I can talk about this whole day. I, I'm excited 'cause I love, I love teaching, uh, my students, I love data science. It is so rewarding to see ... And I don't know if I, I mentioned this. I teach 501 right now, but the other key course that I teach is the capstone. And, and so, you know, you know, most students have me for two classes. Uh, some had me, uh, every once in a while, I'll teach a database class.
[00:36:00] Um, but the, but I get to see you at the beginning and I get to see you at the end. And it was perhaps the most rewarding thing ever because I, I saw, I saw many people, uh, when they're brand new, you know, data novice. And then, uh, and then they, I guess seem when they're, you know, data gurus. And it's so exciting.
[00:36:30] Uh, so when we have a question about, uh, the minimum GPA to stay in the, the program, uh, the minimum GPA actually, uh, the assignment will just stay in, uh, but the, the rules are, and this is gonna get a little too specific, probably, uh, the, the minimum rules are, uh, you, you have to have a 3.0 to graduate. If you don't have a 3.0, uh, then you have to retake a class to get that. Tha- that's a B average. And then, uh, if you fail two classes, uh, you're invited to leave. Um, uh, and you go, there's always different sort of appeals for that.
[00:37:00] Um, there's one student who was able to appeal it that I know for sure, uh, rifle, you know, he had, he had a lot going on, uh, and he had failed two classes, uh, because of life happened. And it was really rather, um, a bummer, but he was able to appeal that and he was able to continue on, and it's really excited and I expect him to graduate in the fall.
So let's see. There's another question here. Are the projects mostly individual or group? So I'll say this, in the data science course, most projects are individual. Uh, outside in the electives, especially in the business school, I think there are a fair number of group projects. Um, what would you say to that, Sean?
[00:37:30] Yeah, I think that's, I think that's a fair way to put it. I think the majority of data science projects are on your own. Although there was a few that ... there was a few classes that had joined projects, but, um, there were small groups and it was, it was easy and, and everyone worked well together, uh, it's a divide at work. And then the business courses was, were definitely much more group-based. Um, yeah, it was still, it was still a good mix. You still got to work with your classmates, but other times you got to, you know, focus on your own.
[00:38:00] Yeah. The, uh, there was a question about, uh, are there jobs in Syracuse or do, do most people move to work? So what I, what, what ends up happening actually, and, and this is something I get and I'm sending my own thunder here is that most people are working in data organiz- or organizations that aren't data-driven. And I say though, most of your, a lot of your opportunities are right where you are, uh, because to move a, an organization from having data and doing nothing with it to being data-driven, um, that, that's, that's more than enough work for any particular student.
[00:38:30] And then, then again, data science is a team effort. So a lot of people, um, can move. Uh, you might have to move. A lot of people like strive to get jobs where they are, which is when, uh, before COVID was sometimes challenging sometimes not. Uh, but now it seems like there's a whole bunch of people I know that are being hired and, and they never show up to where, where are they go and like the laptop shows up in the mail type of thing. Um, so there's lots of variations.
[00:39:00] Let's see there's some other, uh ... My computer use Power BI. Can I build in Power BI or is Tableau required?
[00:39:30] Now, so, uh, I'm pretty agnostic as it comes to, uh, platforms. Uh, Tableau is actually a very great tool to learn. Uh, Power BI is pretty good too. I have ... my, l- my experience with Power BI is limited, and I'll just be perfectly honest. Uh, but I'm not against it. I think power BI is, is, uh, does very well for what it's suited for. Um, all that to be said, you know, I have students who like, you know, can I do this in R? Can I ... and I had, I had a student actually, he did a, he, you know, he's working Julia and I dare some of you to go find Julia. That's the language is likely to replace Python. Uh, it's already, it's already up and coming.
[00:40:00] Uh, all that to say, um, I'm pretty, um, I'm pretty, uh, open to those opportunities. I think, um, what you might be open to is the, to, to learning that new interface, because, uh, believe it or not, there're ... in data science, there's these, these foundational ideas that we provide that, that can go anywhere, but there's also a very important that, it it would probably be really good for you to say, "I know Tableau, I know Power BI and Tableau," and those skillsets, um, uh, are really, really important. Um, now there's probably a way ... it's probably one of those things that if you're really good at Power BI, Tableau is pretty, pretty straight forward as well.
[00:40:30] So, uh, so, so, uh, someone asked about, uh, programming. Most students coming are new to programming. Uh, we, we s- we strive to ease you into it, uh, and that's why we start with Alteryx because that's the logic of programming. Well, first I need to do this, then I need to, need to do this. And then, then we dabble in R during 503. And so 503, we t- teach statistics, but we teach it with R and that's where you start learning. And, and then believe it or not.
[00:41:00] I think we use R to get in, in data mining and then we use, uh, a little bit of Python and machine learning. Uh, and actually, I don't wanna say a little bit, you know, but you do, but that's how we learn it. And there is, um, there is a learning curve to anything. Uh, what I have found is ... well, actually, let me let Sea- let me, let Sean, uh, jumping on there. What, how d- how did you feel about the learning as, as it took place for you within the program for the program?
[00:41:30] Um, so yeah, so, like I said, I, I had zero programming experience at all before going into the program. Um, it was definitely a, it was definitely a challenge, a hundred percent, it was a challenge. Um, but I think that, uh, professors were very helpful moving forward and, and providing, uh, supplemental training and, uh, resources to help with the coding, um, and like anything, it just takes time. So, you know, the more you do it, the easier it becomes. Um, and the students are also helpful too.
[00:42:00] Like, I mean, like I said, I, I worked with, uh, classmates and, and we did a lot of coding together because it was, it was something that both of us, excuse me, wanted to get better at. And we hadn't had, neither of us had very much experience, if any at all. So it takes time just like anything, but it's, it's something you can learn.
[00:42:30] And this is, this is one of those things that, you know, we're a data science program, you know, and so we're focused on the data part. Uh, program is something that is part of data science, but it, but it's actually much more of a foundational thing. Uh, so, um, so just, you know, it, it is an emphasized, um, believe it or not that, that we have, uh, built in tutoring for Python, uh, with smart thinking and faculty really strive to help.
[00:43:00] Uh, there's a lot of tools that are out there now actually, um, that, that Sean actually didn't get, uh, get to take part of, but the, now there's the Google Colab, which is a way by which we can, um, collaborate, collaborate with students on their Python notebooks. Uh, and there are no books. I haven't done it with R yet, but it's, it's so new. Um, but it's one of those things that, um, uh, there, there's ways to enable you to, uh, to get strong.
[00:43:30] And again, this is one of those things you reap what you sow. Um, can you get through this program with just a little bit of programming? Sure. Do you want to? I don't think so. I think you really wanna dive in and get as dirty and deep into it as, as you can handle. Uh, so here's the question. Here's another question from a guest. Can the program be customized to blend or add additional courses from different specializations? So the short answer is yes. So the specializations, um, are, are basically four electives that we've, that we've, uh, designed to help you, uh, kinda get that strong, foundational domain experience in business, financial crime, cyber, or, uh, social science analytics. Uh, the goal of that again is that, um ... but, but there's just for electives. The requirement to graduate is four electives. So you can choose one from social science analytics, two from cybersecurity, and then one from business or, or zero from business, all from social science analytics, or an independent study.
[00:44:00] Um, we have a lot of students that are doing independent studies. And again, this is something that we're striving to help students, uh, find new pathways. It's something that wasn't available to Sean, but we totally support now where students go, "I wanna learn more." I have a student now. She was, she was really wanting to learn a lot about da- uh, ethics with, with data and bias with data and social responsibility. She did cast them, she just finished it. It was, it was really, really strongly ... it was s- strong, but if we don't have a class in that, you know, it's kind of baked into the whole curriculum, but she was very excited about it. So we found a way, uh, to definitely, uh, get into that.
[00:44:30] Um, we a question about SQL, we don't, we don't get into SQL, uh, SQL. We don't, we don't get into SQL. SQL is a database. That is ... so (laughs) so the short answer is, uh, I'm expecting SQL course to come very soon. Uh, but we don't have one right now. So I don't, I don't wanna tell you ... uh, there's one coming. Uh, and it would probably be around if you started, uh, this summer, it would probably be around for you when you got around to, to, uh, finishing machine learning.
[00:45:00] Um, actually as we strive to make curriculum, um, awesome, uh, and cutting edge, we really want, um, we really wanna offer a course with SQL. Uh, we actually think it's so important that we actually, uh, uh, we're expecting to actually reduce the, the electives down from four to three and to offer that because we see, we see it as so valuable. Um, and then, so that- that's one of those things that I ex- I expect it to happen in the fall to be offered in, uh, in the spring, uh, if not, if not earlier.
[00:45:30] So, so the short answer is no, but, but we, but we, but it's coming, it's coming.
[00:46:00] Yeah. 'Cause it, 'cause it's one of those things that our, our, that, that our students would just be so much stronger with it. So not, not that they're not strong without it, because what I feel about SQL is what I feel about all languages is that, uh, SQL is important but if you, if you have those foundational ideas, um, you can go and pick up any language you want. You can go pick up Julia, you can go pick up SQL. That's what I did. You know, I knew SaaS, I picked up SQL.
Um, I actually learned ... you know, I'm gonna date myself a lot in college. I learned Ada, which is, was, when I learned it was already dead. Uh, but you learn, you learn some of those key, uh, calls, how to import ... you know, it's like, what are the four things you need to learn any language, um, how to import a library, how to import a variable, how to, how to, uh, how to identify a variable, how to import a dataset and then how to run a loop. You know, once you kinda get those things down in almost any language.
[00:46:30] Uh, SQL is made for database queries, um, it's not, not too dissimilar than, from most kind of key ideas. Any other questions? Those are great questions, all. Yeah, and I. and I, I encourage you to ask Sean and, and here's something that, uh, that I want everyone to know.
[00:47:00] Uh, Sea- Sean, Sean is absolutely fabulous, but if someone wants a perspective that's, other than Sean's a student's perspective, uh, from a, uh, a more recent graduate, uh, or another graduate, you know, the, um, we, we have, we have, we have students, we have graduates that would love to talk to you. Um, this is something that, um, uh, you know, that's the, one of the thing that I'm really, I pride myself in is that, um, when my students come in, uh, they are, um, you know, I ... you know, they're mine. Remember, you know, you're, you're just like my mentor paid forward to me all, all the support that I've ever gotten from, from him, I strive to emulate that and pay it forward to my students.
[00:47:30] So it's really neat to, uh, recognize that, you know, this is not just a, a two year thing. And so, uh, I, I'm constantly writing letters of recommendation. I'm constantly checking in on my, on my grads, on my students to see how they're doing. Um, there's a lot going on. And, and I really strive to support my students all on the way, uh, you know, from, from the, from the coursework to balancing, you know, job offers, uh, you know, do you know, do we go to A?
[00:48:00] Do we go to B? Well, let's weigh them and weight them and, and better understand what's going on. That's really something that I strive is to, to maintain a strong working relationship, uh, you know, w- with my students. You know, mentor mentee, then, then, uh, grad alum to, you know, old, old professor, you know.
[00:48:30]So it's one of those things that, uh, I don't know, it's a great part of the gig. I really dig it. Any- any- anything else, Sean? Is there anything you wanted to add?
[00:49:00] Um, no, I just think ... I mean, yeah, if, if you are studying data science, this is a great program. Uh, I've, I recommend it to anyone who asked me for recommendation. Obviously I'm here, so I (laughs) I highly enjoy the program. Right? Um, but again, like everything that I have done during the program and since job-wise, I pulled from this. So, um, it's a great education's a great resource, it's a great community, um, and you'll pick up a lot of the, the, the, the kind of platform you need to build upon, and then also come out with skills that other, other folks don't have. So you can jump right into a new position and hit the ground running. So if this is what you're thinking, if you wanna go data science route this is, this is a great program.
[00:49:30] And if you're already in data science, I have to tell you, I mean, you know, it's absolutely amazing. You know, I've had people ... I had a, we had one of our, uh, early graduates. He came to us, who, he managed a team of data scientists, and he came because he wanted to be able to engage and get more out of them and understand exactly what they're doing. Uh, and then, you know, it's from that, it was this, you know, this main, you know, almost basically an executive all the way down to people who, uh, are t- are truly trying to transform themselves from where they are in a, in a either an industry or a job that they don't really prefer, uh, a- and, and jump into this.
[00:50:00] And I have to tell you, there's almost, there's almost nothing you can't do. I don't know if you caught what I just said there, but there's like data science is so powerful, um, whether you work for nonprofit or you work for a government, uh, organization, like the VA or healthcare, um, it really is quite, it's really kind of taking over. And, a- and then within the organization, you become highly valued. A- and within the organization, you become part of a, a team that's getting things done and providing new insights. And I find that truly rewarding.
[00:50:30] That's, that's one of the neatest things that when I was working, uh, to help transform healthcare within the VA, um, making a data-driven and then keeping that, those, those i- those me- those main foundational ideas that, that a lot of people, you know, just, they, they don't, they just enabled me to be, to be so powerful and to do so much for my, my organization.
[00:51:00] And it really is, um, you know, when, when you have a data science, uh, degree from, you know, it's particularly from Utica College, but with small class sizes, engaging faculty, um, with, you know, with driven, uh, capstones, uh, using software, uh, like we have, you know, with project-based teaching, it's really, really, really rewarding.
Alex: Well, good. Well, um, this has been great Dr. McCarthy and Sean. Um, I, we've got, a, a ton of information here, and I'm, and I'm glad that this has been recorded because we'll be able to send this out to folks that weren't able to, to make it. And, um, and then for some of the folks that are here now, and some of them may have, um, entered the, a webinar late, uh, this'll be recorded and we can send this back out so you to kind of review again. But, um, Dr. McCarthy, Sean, I appreciate you guys doing this.
[00:52:00] This has been a fantastic, excuse me, webinar. And, um, I think we've got some great questions that were answered today.
Sean Regan: Thank you.
Michael McCarth...: Yeah. Thank you. Appreciate your time, Alex. Appreciate your time, Sean. Then thank you for everyone, who, uh, all the s- prospective students. I hope to see you this summer or maybe this fall.
Michael McCarth...: It really is ... it's really a wonderful, you know, it's a journey.
Michael McCarth...: Uh, but it's, but it's a great one and I'd be glad to be part of it with you.
[00:52:30] Absolutely. And for those who are looking to apply, feel free to reach out to us [inaudible 00:52:23] office. We're normally here in the office Monday through Thursday until about 8:00 PM. So we work late, uh, to service students all over the nation and, um, Fridays we're normally here till 6:00. So feel free to reach out to your program manager, anyone you've been in contact with, or if you haven't been in contact with anyone, feel free to reach anyone in the office and we can help and assist. So, but again, thanks again. I appreciate everything guys. This has been fantastic. And, um, if there's no other questions from our viewers, um, we'll go ahead and end the webinar at this point. And, uh, we're looking forward to hearing or speaking to any of you in the future.