Dr. Chris Smith: Wake Forest’s MSBA

Are you comparing graduate school programs to determine which one would best help you obtain data science & machine learning skills? Are your data science interests more applied rather than theoretical? Would you like to learn these skills within the context of solving business problems? If so, then you should consider Wake Forest’s Master of Science in Business Analytics (MSBA) program!

FEATURED GUEST:

Name: Dr. Chris Smith

Email: csmith@wfu.edu

LinkedIn: https://www.linkedin.com/in/christopher-smith-phd-2289391a/

SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!):

Donate: https://bit.ly/37gbLvS

EPISODE LINKS:

Wake Forest's MSBA: https://business.wfu.edu/masters-in-business-analytics/

Wake Forest's Online MSBA: https://online.business.wfu.edu/programs/online-masters-business-analytics

Malcolm Gladwell's Revisionist History (podcast): https://podcasts.apple.com/us/podcast/revisionist-history/id1119389968

PODCAST INFO:

Host: Ted Hallum

Website: https://vetsindatascience.com/thedatacanteen

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-data-canteen/id1551751086

YouTube: https://www.youtube.com/channel/UCaNx9aLFRy1h9P22hd8ZPyw

Stitcher: https://www.stitcher.com/show/the-data-canteen

CONTACT THE DATA CANTEEN:

Voicemail: https://www.speakpipe.com/datacanteen

VETERANS IN DATA SCIENCE & MACHINE LEARNING:

Website: https://vetsindatascience.com/

Join the Community on LinkedIn: https://www.linkedin.com/groups/8989903/

OUTLINE:

00:00:07​ - Introduction

00:01:07 - How Wake Forest's MSBA was conceived

00:02:41 - Why Wake Forest's MSBA is a good fit for veterans

00:05:28 - Chris' background and personal data science journey

00:10:49 - Math phobia

00:12:59 - Prerequisite requirements for Wake Forest's MSBA

00:19:59 - A thorough introduction to Wake Forest's MSBA curriculum

00:41:14 - Wake Forest's various delivery models for the MSBA

00:47:23 - Character traits of successful MSBA students at Wake Forest

00:54:26 - Typical career outcomes for Wake Forest's MSBA grads

01:06:35 - Chris' tips on preparing to enter Wake Forest's MSBA

01:11:37 - Chris' favorite data-related podcast

01:13:00 - The best ways to contact Chris

Transcript

DISCLAIMER: This is a direct, machine-generated transcript of the podcast audio and may not be grammatically correct.

Ted Hallum: [00:00:07] Welcome to episode nine of The Data Canteen, a podcast focused on the care and feeding of data scientists and machine learning engineers who share the common bond of U.S. Military service. I'm your host, Ted Hallum. Today. I'm joined by Dr. Chris Smith. Chris is prior-service Army, and a fellow member of The Veterans and Data Science Machine Learning community. Prior to his current role as Associate Professor for Wakeforest University's Master Science and Business Analytics program, Chris served on faculty at other distinguished institutions like West Point and the Air Force Institute of Technology. Today, we talk about Chris's own journey from military service to data science, why his program values U.S. Military veterans in the classroom, who is a good fit for his program, eligibility requirements, the curriculum, and common career outcomes.

I hope you enjoy the conversation as much as I did.

Okay, Chris, thank you again so much for coming on the show today. I'm so excited to hear about wake forest MSBA program, and I want to hear about it from the very beginning. Can you just take us to the start of how wake forest kicked off the MSBA program?

Chris Smith: [00:01:07] Wake forest realized that analytics was a player as time has come. They had always been in the master's of business administration, MBA program.

They have masters of accounting, master's of finance and masters of management. But they didn't have anything in the business analytics spot. So about four years ago, they did a search and found Dr. Jeffrey Kam, who was the department head of the business analytics department up in college in Ohio and offered him a position down here to create both an online and an on ground MSBA program.

And he sat there and did it correction. I think he was hired like six years ago, but they actually got the thing up and running. It's about been about four years. So he used the main person that got this program up and running at week.

Ted Hallum: [00:02:09] That's awesome. So if I understand correctly, that means that this MSBA program at wake forest was not like some other program that just got minor tweaks and then rebranded as the MSBA.

This was built new from the ground up.

Chris Smith: [00:02:23] Correct.

Ted Hallum: [00:02:24] This is awesome.

Chris Smith: [00:02:25] Total creation of what should an MSBA program look and smell like, especially with the wake forest field.

Ted Hallum: [00:02:34] Sure. Well, of course, as you know, this is the data canteen and it's geared towards the veterans and data science machine learning community.

So I'm curious to know when it comes to veterans specifically, why should veterans seek out the Ms MSBA program there at wake forest? Because I'm constantly telling our audience go where you're celebrated, not where you're tolerated, because you're going to have a much better experience when you go someplace where you're a natural fit.

So what makes veterans a natural fit there?

So in my opinion I think most veterans are kind of more outcome oriented. And you know, some people have a fear of math or analytics. But if you can get over that anxiety this program is very outcome oriented. We're focused on. Yeah, we're going to teach you the analytics.

We're going to teach you the algorithms. We're going to teach you our and Xcel. But. What we really do is we focus on why you're doing it. So we focus on an outcome based. So talking to the client and figuring out what they want and how to, how to frame your analytic problem that you're actually working on.

And then at the back end, how do you talk to your sponsor or client and explain to them the results of what you're doing, what you've done and how this affects them.

So I, I like one thing you mentioned about people getting over that fear of the advanced mathematics. I know that's something I had to do.

I've got a mentee right now. Who's preparing for grad school and he's had to work through that. How common would you say that is in students to come to your MSBA and, you know, could you re reassure our audience in any way that, that, that is something that they can work through? Not everybody has to have a bachelor's degree in physics or whatever to take this route.

Chris Smith: [00:04:26] Yeah, absolutely. We have a wide variety of different degrees that come to the MSBA program. I think there's like 37 different majors that all come in and not all of them are technical. So there's our undergrad majors. We start off the program with a probability and statistics class that's given using Excel to kind of get your feet on the ground and to get you start thinking quantitatively and really start understanding probability because it's one of those things you'll use for the entire program.

But it, the, the program, like any master's program kind of takes off like a scalded dog, but it. And it has an understanding that not everybody is at the same quantitative level and we make an effort to kind of homogenize everybody to kind of get them all okay. At the very base level of understanding before, but the analytic stuff really quickly.

Ted Hallum: [00:05:28] So one thing I would want to mention is you're a part of our veterans and data science machine learning community. You're an army veteran. You have a very interesting data science journey of your own that has brought you up to this point in serving as a key faculty member there in the MSBA at wake forest.

We'd love to hear that whole story. If you can take us back to you, tell us a little bit about where you're from, whether or not you were in you were interested in stem stuff when you were a kid and then how you progressed from college in the military to your current role.

Chris Smith: [00:05:59] Okay. So I grew up around silver spring, Maryland and.

I, I, frankly, I failed trigonometry when I was in high school. There was no way that I had never even heard of west point. So I had no way I could ever apply to west point or whatever. But I couldn't get in my grades. Weren't good enough. So I enlisted in the army and while I was there, I found out about the, what west point prep school that at the time was in Fort Monmouth, New Jersey.

So I applied to that, got accepted went through that program. And that's where I started finding a real interest in math, science kind of analytics sort of thing. And then ended up going to west point and graduating with a undergrad in systems engineering. But the thing that I left west point with was a drive for, I wanted, I had some great professors at west point some ones who really kind of shaped.

Helped me to shape who I wanted to be in the future. And so one of my career goals was to try to return and offer that sort of leadership teaching role model to other cadets. So I when I had the opportunity, I applied to go teach at west point and was accepted. So they sent me to a master's program and then I came back and taught at west point for a couple of years.

And after that, I really started getting the bug and applied to a PhD program. So they sent me to university of Virginia and I did my PhD in systems engineering there. And then I did a an assignment that had, I was a center director for an operations research program. So operations research is essentially analytics, just another name for it.

So we did operations research or analytics projects for the army. And then I ended up teaching in the graduate school. The air force has graduate school operations research and recently retired, decided that's what I kind of wanted to do when I grew up. So I looked around for places that I could teach and wake forest was a very kind of home feel.

A lot of their they, they focus on the whole student. It's not just let me get you this education. It's let me get you this education, but oh, by the way, we want you to be someone who is ethical, someone who is a leader those sorts of things. So it, it really resonated with me.

Ted Hallum: [00:08:32] I love your path because it's, so the more I talk to veterans who have made the transition to data science and machine learning or analytics you would think.

That the typical story of somebody getting into these fields would be, well, I was super into stem as a kid. And then I did all the AP courses that I could possibly do in math. And then I went on to get a bachelor's degree in mathematics or physics. And then, you know, maybe I got a quantitative master's degree even, but that was pre 2010.

And then as soon as data science became a thing, then I went the route of data science, but it's so much more common. And I, I, I feel like some people let that hold them back because they feel like they don't have the right background. And the, one of the things that I really want people who stumbled upon this podcast to realize is there is no right background.

This is a new field. I had a previous guest on the show say that every company is an analytics company, a data company, some just don't know it yet. So what that means is, is that people are the future. Doesn't matter, What Stripe of the business you come from, you're going to have to know the data piece.

It's going to become a part of everyone's reality to know how to do the quantitative data part because that's just going to be a reality of almost every company. And so I don't think it makes sense to think of a particular pigeonhole background and say, well, you have to come from a background like that to get into analytics, data science, and machine learning.

That's just not true. And when I look at your background, you started out as an MP. Then you were an artillery officer, you know, and then there was this slow progression, you know, then you got into, you were operations research systems analyst, which is, you know, you said operations research is much more like analytics.

And then you know, here you are today, a key faculty member in a. Analytics degree program at the graduate level. So I think that's a fantastic background. I hope people in our audience who are infantry men, artillerymen whatever will hold you up as a template and be inspired and, and say, well, maybe I didn't think I could do this before this episode, but Chris did it and Chris is killing it.

And so I think I can do it. That's what I hope people get out of this.

Chris Smith: [00:10:49] Yeah. So the thing, I mean, I appreciate your words and I think that would be awesome because I think you're absolutely right. The thing that along my path is it was never the math that really interested me, the math was kind of a side product.

It was wow. You mean, I can really look at explore and solve all sorts of these really like not easy problems. That's cool. That's the thing that got me. The math was kind of just the way to do it, but I want her to be able to solve problems. I wanted to be able to better understand how to do things more efficiently or better.

Ted Hallum: [00:11:24] Right. It's not the end. It's a tool. It's a means to an end. Well, for most of us, for some, for some people, the study of mathematics, isn't it,

Chris Smith: [00:11:34] but it doesn't have to be that that's, that's why so I teach, I've taught college algebra online for a different school for like six, seven years. And it's, it's from a, a veteran focused school.

And, and I always get people who are like, you know, the first forum post is like, watch this video. And it talks about people who like hate math and like, you know, smashing their head against the computer and you know, all this kind of stuff. So people, people really look at this, like with anxiety, I mean, math anxiety is an actual thing that people look at it.

They're like, oh my gosh, it just, I start getting hives. But the thing is, I mean, that's not the thing that you know, that everybody who does analytics has to love math. It's you have to put up with it in order to be able to really add value and answer a problem

Ted Hallum: [00:12:30] for sure. Well, crisper people for our listeners who are at the point in their journey where they want to take it to the next level, they're looking at graduate programs.

I want to kind of approach your program and the same process that I think they would. And so the very first thing is to look at the prerequisites that are required because you obviously have to meet the prerequisites in order to get into the program. So if you could give us a rundown real quickly of what's required from a prerequisite standpoint to get into weight forces in the SBA.

Chris Smith: [00:12:59] Okay. So as far as the prerequisites so let me, let me just give you the averages. So on average, a GPA for undergrad is about 3.4. Average GRE is about three 15. Average GMAT is six 63. But as far as as far as floors you know, I don't, I don't know that we really have floors. We like to look at the whole candidate and just see, you know, who's out there and who we can bring in.

Cause I mean, our, our, our program is all about, I mean, diversity, we, we want to try to include some diverse populations. We got about 37% female which is really high 14% underrepresented groups, the minorities. We tend to have a lower population of of international students. I know one of the things about MSBA programs and it tends to really attract a lot of international students and we purposefully hold our percentage lower to kind of leave room for the.

Non-international students. So we have been keeping it about 50% international which I think other programs are closer to 70, 75%.

Ted Hallum: [00:14:36] Now I know certain, I know some programs have certain mathematic course prerequisites or programming course prerequisites does the MSBA there at wake forest have any of those?

Chris Smith: [00:14:46] So I think most anything I think they, they want you to have like a college algebra but the first course, like in, in the first secrets, when you, when you joined in the summer is probability and statistics. So that's one of the kind of fundamental courses that you're going to need throughout the rest.

So I think largely if you don't know how to code and if you're not that strong and probability and statistics, if you wanted to take as I think we were talking previously, or we we'd seen some of the pre-talk prep. If somebody wanted to take a Coursera course in probability and statistics, that would be probably something to do to get a little bit of a leg up and better understand what you're getting yourself into.

But I don't think you need to, as sort, as far as prerequisites, it's a college degree. And you know, close to our averages of that I just mentioned.

Ted Hallum: [00:15:42] Okay. Now, as far as that, as far as the algebra course, if a student didn't have that obviously they could like go to a community college and take an algebra course, but if they didn't want to go that route, could they, could they get a waiver to do an algebra Coursera course?

Or could they tack an an algebra course onto the MSBA there at wake forest to meet that requirement? Like at the beginning,

Chris Smith: [00:16:04] So that's a good question. I think that they would have to do that a case by case basis. Okay. Because I think it would also depend on what the whole candidates like. So if this is the only thing that candidates missing, but yet when you look at the whole candidate, they got a lot of other things going for them.

Then they might be willing to work with them to try to make things work. But I think college algebra is one of those ones that you really you're going to need that before you actually come into the program in order to be successful. Because, you know, if you don't, if you're not as familiar with building a mathematical equation, like X equals, whatever you're probably going to struggle.

Ted Hallum: [00:16:43] Sure, sure. Now, before we move on past prerequisites, the last thing I wanted to ask about is I know I've taught from talking to the population that people that are in the veterans and data science, machine learning community, some people have anxiety about doing the GMAT of the GRE. Obviously, you know, if they're in that camp, they haven't done the test yet.

So if a student wanted to get a waiver because they have anxiety about it are there options to, I I've heard of some schools having like an in-house test that students can take, or if they've got a certain GPA in their last three quantitative classes or something like that, they can get a waiver. Are there any waiver options like that at wake forest for these tests?

Chris Smith: [00:17:23] So not formalized. But one of the things that we've learned with with COVID is it's really hard to do standardized tests both SATs, but also GREs GMATs, things like that. So, I mean, if there were issues where you know, due to COVID, it was difficult to do one of the standardized tests. We may be able to kind of work something out.

But then again, I think that would also be kind of case by case. So I don't think there's anything formalized right now.

Ted Hallum: [00:17:54] Okay. Now with the thing about podcasts is they go out there and then they kind of hang around and people find them sometimes two or three years later. So once COVID is starting to disappear in the rear view mirror, which I know is going to take a long time for that to migrate out of our consciousness.

But I'm assuming folks come across this episode once that's you know no longer a big issue. How would that change your answer about the potential waiver for the standardized tests?

Chris Smith: [00:18:23] Well, I guess just, just like with anything, I don't think there's a plan to create a formalized process for not having a standardized test because it's just one of those things that schools use to help.

Delineate candidates and to better understand candidates. But I think if, if wake is a school that's interesting to you it would be worth going ahead and talking to one of the admissions people and just seeing and just saying, Hey, listen, here's my situation. What do I need to do? Because I think that if nothing else, we have great personalities at wake, a lot of our staff members are just great people and they're not, we're not all about just getting people in the program just to get them in the program.

We want to get people who, who will be successful in the program. And if, you know, if you're not someone who would be successful in this program they're going to tell you, they're going to say, listen, this is what you would need to do, or they'll tell you how you could get to be successful by coming into the program.

Ted Hallum: [00:19:28] So if someone is looking at themselves and they're looking at the requirements like, wow, I'm pretty good on this requirement. I'm good on that requirement. And I just haven't done the standardized test. They shouldn't necessarily let that hold them back. They should still at least have a conversation with an admissions counselor and find out they ought not assume that that's a guaranteed roadblock

for them.

Chris Smith: [00:19:47] And just like anything in the military. I mean, you, you, you use your own initiative and go ahead and don't let something hold you back just because you're not sure.

Ted Hallum: [00:19:59] Okay. So proceeding on from the prereq was that the next thing that I want to take a strong look at is the curriculum. And so we'll look at that from three vantage points, but I know from talking to you, you have some pretty cool slides that we should throw up here at this point.

Chris Smith: [00:20:13] Okay. So let me go ahead and share these. Can you see the size

Ted Hallum: [00:20:22] I can. Yep.

Chris Smith: [00:20:23] So we started, I mentioned Jeff cam came to wake forest and then was told, Hey, build an online and on-ground MSBA program. So one of the things he did was he started looking at what are the industry needs, because I think this is really important to know when looking at our program.

And so this is some of the stuff that he found. So first of all what he did is he took a look at he scraped a whole bunch of job or job postings for analytics jobs. And he did a little bit of text mining to find what are the most common terms used. And by far, some of the most common terms are communication, interpersonal skills, managerial skills, things like that.

So you see those soft skills up at the top and then about midways you see modeling and analysis programming those sorts of things. So, so that's going to be really fundamental in the shaping of this course. When you look at some interesting jobs out there what we find is that there's a lot of data science, analytics jobs out there, and heck industrial engineers could kind of be lumped under there as well.

So what kind of tools does the industry need? So this is one of the things that. Again, in looking at those job descriptions, they found a job postings, machine learning optimization predictive modeling forecasting. When you look at analytics or data science, you see over the number of years, years it is growing.

So while data science isn't necessarily growing analytics in general is growing. There's a lot of jobs out there that are highlighting key words of analytics and then jobs on LinkedIn just by the number. So statistics, data science, or analytics. So analytics is a key thing that's that's coming up. So to go over those, those are fundamental and understanding how the program was shaped.

Yes, we teach the schools that the, the, the tools of statistics data mining forecasting. Things like that, but we also are heavily focused on the client and some of those soft skills that you need to be a successful analyst. You can do the best analytics in the world, but on the front end, if you don't know how to frame the right problem, if you can't talk to the decision maker and get the right problem frame and understand really what the problem is, you might as well not have done the project.

And at the backend, if you've done this great analysis and you can't communicate it to a decision maker effectively enough to get them to understand what it really means to them. It's not enough just to say the answer's three. Well, what does that mean? So if you can't communicate that to a client or to a sponsor of whatever research you're doing, You might as well not have done the research.

So we are very focused on, yes, you're going to learn the tools, but you're also going to learn the soft skills on both ends of the project. That's going to help you be as successful a data analyst.

Ted Hallum: [00:23:59] That's right, Chris. And I would add, as you and I were talking about before the podcast kicked off, it's not just identifying and understanding the right problem that the, that the customer has given you, and then being able to communicate your analysis at the end, but it's also sometimes sitting down with the customer and saying, well, what's your ultimate application, this analysis going to be because their identification of the problem may not be correct.

So sometimes it's that matter of walking through with them and finding out exactly how they intend to use your analysis. And then that can help you to realize they're not asking me. They're not posing the right problem to me. If I answer this, I'm going to be answering the wrong problem. And then once that's identified, you're just going to have to do the analysis all over again.

So it helps if you have that diplomacy and that ability to sit down with your consumer, whoever that is, your stakeholder and work through exactly how they intend to use your analysis and then say, ah, yep. So this is the real problem you want to answer and then answer that problem.

Chris Smith: [00:25:12] I challenge every one of the students that I teach that for, depending on your timeline, of course, but for the first like couple of days to a week that you have a problem.

You should not actually touch a computer. You should do no coding. You should, frankly do no kind of data manipulation. Your primary thing should be talking to the, to the stakeholders, talking to the, the decision maker, figuring out where does this problem kind of rest and the whole structure of things, then sit back and say, okay, what is the problem?

Because oftentimes, like we've talked about the decision-maker is focused on a symptom. They think they know the problem. And that's what they're telling you. Hey for example, we had a, a project where a bank, one of the experiential learning projects that we do. So all the students that come through the program take a, we call it a practicum.

So they, they vote on and select a real-world project with a real-world sponsor in October. And they work on it all the way until may. And they present their results to the sponsor. Well, the, the bank had a question of how do we get this good type of account holder. We want more of them. So where can we find more of these types?

And they had some description on what they thought a good account holder, a good kind of business partner was. And once you looked into the data, what you found was they didn't have a problem, like looking for the right people, they had a retention problem, and they're the right type of person that they wanted actually only stayed with the bank for about a year and a half, and then they left.

And so the key is, well, how do we actually keep those people? And so it kind of totally changed the direction of the project. And they started looking at data revolved around why those, why that class of client was actually leaving the bank and then offered the bank some good suggestions on how do you actually retain those people?

Rather than good ideas on how to actually get them.

Ted Hallum: [00:27:21] I love that vignette because it makes it so tangible to understand how easy it is to, to ask the wrong question and then put a lot of work into trying to answer the wrong question. A lot of times I'll talk to people who haven't done this yet. Maybe they're still in school or they're about to go to grad school and they'll say, well, you know, how could you, how could you ask the wrong question and then do a bunch of work to answer the wrong question.

It's surprisingly easy to ask the wrong question. If you're not careful. And a lot of times just talking to one stakeholder, isn't enough. A lot of times you need to talk to stakeholders across multiple parts of the business to find out because like, you're going to provide your analysis to one stakeholder who's then going to.

Secondhand, give it to someone else and downstream it's going to get used. And that's where you find out, oh, the ultimate person that's really going to use this. This is what they need. And it's something totally different or a variant, you know, that makes a difference. Yeah.

Chris Smith: [00:28:19] Yeah. I mean, so my background's in system engineering and kind of what we're talking about is there are systems that reside within systems and exactly why this is so interesting is because just because your decision maker has that focused view on what they think the problem is, it's your job.

Oftentimes the business analytics person is the first person that's actually spending their time looking at that problem. Because most of the times, these problems are just off the critical path of whatever the decision-makers doing, whatever it's like, Aw, man, this thing's always been bugging me. I need somebody to kind of take a look at this and they have gut feelings about it, but they haven't spent time looking at the problem.

What are the inputs? What are the outputs who touches it? You know, things like that. So they really understand what's going on with it. And so the business analytics person is one of the first person to actually do that. So sometimes some of the best results you can give is just your feedback on looking holistically at what the problem is.

Absolutely. No, frankly, isn't analytics at all. It's just kind of common sense, looking at things and talking to people.

Ted Hallum: [00:29:30] Absolutely. So earlier I'd said we were going to go through and look at your program from three different vantage points. The first one of those I'd like to take a look at as essential technical foundations.

Of course, you know, That's the way I have it worded in my notes here. I think you make a great point that those soft skills and the ability to communicate is probably the absolute, most fundamental foundational thing that you need in order to be successful. But people that go to school and earn a master's degree in this area, they definitely want to make sure that they're going to get the technical skills they need.

So I wanted to find out in terms of the program and languages use the other, maybe data visualization tools the different types of modeling for machine learning. What can people expect to get from a technical standpoint, if they come to the MSBA there at Waco?

Chris Smith: [00:30:24] Okay. So the two main tools that we use throughout the program are Excel and R okay.

So fundamentally you're going to walk away with a grounding in R and Xcel. We don't get to VBA within XL, but for most of the things that, that. Yeah, you can use a lot of functionality within Excel without getting to the virtual basic coding. So we do XL, we do our there is a class that talks about my SQL to work databases.

There is a class on Tableau that really works kind of a visualization those types of things. But then again, there's a semester long class of visualization analytics visualization. So how do I, not only visualize it, not only craft a slide that's, that's effective, but how do I present the material?

And then while we don't, I don't think we emphasize, you know, get hub or repositories they're certainly available for students because they're going to be doing enough, are to be able to kind of save their coding, save their output things like that.

Ted Hallum: [00:31:41] Cool. So there's definitely, there's definitely output.

The students will produce that they're prudent. They can put that in, get hub and that'll for anybody who's listening, it hasn't tuned into a previous episode of this podcast. Just know that that's definitely something that you need to do as you do your art, coding projects, your Python coding projects in any program, whether it's a data science bootcamp, or if it's a graduate program, like what we're talking about here with the MSB at wake forest because we've had data science and machine learning, hiring managers come on the show and we've had recruiters for companies that have to go out and find candidates for data science, machine learning roles come onto the show.

And they've told us repeatedly that one of the biggest things they look for is proof that a person has the skills they say. That they have, because everyone is learning the keywords now and all the, all the resumes are packed with the right keywords, but not everybody actually possesses those skills.

And so they look at these GitHub profiles to be able to see, oh yeah, this is an awesome art coding project that this person has done. And they really can do, they've got the chops that they claimed to have in the phone screen or whatever.

Chris Smith: [00:32:50] One of the things that, that is included in the tuition at wake forest, have you ever heard of LinkedIn learning?

I have. Yeah, absolutely. So, so LinkedIn learning is a thing. It was just recently bought by LinkedIn, used to be called Lydia where it was like a more kind of faculty oriented. Like if here's the little three, five minute video, if you want to give it to your students, to get them to better understand how to do Excel, how to do R Python, you know, whatever.

So LinkedIn learning bot. Like Lydia has it as a premium kind of aspect of it. And they have little kind of courses set up so that if you follow these videos and they have little tests and stuff like that set up for the, so like essentials of Excel our programming Python, things like that. If you go through and they have little tests along the way, and you finish the course, it'll actually put a medallion of a certification on your LinkedIn profile for some of these

Ted Hallum: [00:33:54] kidding.

Yeah. I did not know that

Chris Smith: [00:33:57] that's in addition to the get hub. That's one of the reasons why the wake forest tuition includes that LinkedIn learning profiles so that you can actually do what you need to, to add those things onto your LinkedIn profile.

Ted Hallum: [00:34:09] That is a fantastic tip. Thank you for sharing that.

I actually didn't know that. That's awesome. Yeah, I mean, you know, once you gain, once you attain these skills and obviously I've got Chris here because the MSP program that at wake forest would be a great way for veterans to attain these quantitative skills. You have to be able to communicate them in a way that's believable to the employers that are going to consider hiring you.

And they look a lot at LinkedIn profiles. They look a lot at GitHub profiles, so making sure that those are up to snuff and that they communicate everything that you're capable of is huge. So definitely, absolutely take Chris up on that tip to go out and do the LinkedIn learning and get those badges added to your profile.

All right, Chris. So for the next vantage point that I want to Take a look at your MSBA from it's the analytics and the AI project management and storytelling. So this goes kind of hand in hand with that fundamental, fundamental communication skill that you've already talked about in terms of specifically storytelling and taking your analysis and telling a story with it.

How does your program prepare students to do that?

Chris Smith: [00:35:18] So, I mean, I mentioned before we have a semester long class that looks it's called I think it's called, it's gone through a couple of different iterations called analytics in the boardroom. That part of it talks about the visualization. How do we create good visually appealing, but yet getting the point across sort of slides that don't distract people, things like that.

But other part of the class is how do you communicate that? And, and I'm glad you're mentioning storytelling because that's the, that's the way they bring across the education is they say, there's a story here that you're saying that, you know, there is a beginning point, a kind of data and why, and an end point.

And so they kind of walk people through how do you craft that story of whatever it is that you want to whatever part of analytics that you're presenting and throughout not only that class, do they give you examples? But I mentioned before you do a practicum class that starts in October and kind of goes throughout the rest of the program and you are continually having opportunities to reach out to your sponsor to give them an in progress review or to ask for more information or to ask for different data or things like that.

So you're continually having opportunities to be able to tell the story.

Ted Hallum: [00:36:41] Absolutely. So I think the true value that most people serve in an organization once they get these skills is being able to dig through the data, dig through the chaos and derive insight. And so you should be, if you're doing a good job, You should be pulling novel narratives out of the data.

That's where your value is. You're helping people see things that they haven't seen before. But a lot of times, especially if you're dealing with a stakeholder who's been in that particular business or industry for 25 or 30 years, when you're delivering something novel expecially, if it happens to be counter to his or her intuition, you're going to need a compelling narrative.

Cause you're going to have to sell them on what you found. And so that's where his skill of storytelling I think is absolutely huge because it teaches you how to take your findings and present them in a compelling way that even if someone would normally be. Skeptical once they hear you present it to them with like, as Chris said with that beginning, middle and end and, and, and you, and you've made it compelling, then they buy off on it and they say, yeah, I don't know how this person got that insight from that messy data, but they're absolutely right.

And I'm willing to, I'm willing to buy off on it and we'll go in that direction with that new, that new golden nugget of information. So Chris, the last vantage point that I want to take a look at your MSP programs, curriculum from is data management and governance. And I bring that up just because it's not necessarily pertinent to every single job out there, but there are some, you know, the healthcare industry with the HIPAA requirements and then certain other government roles you know, like folks that are maybe in the intelligence community or whatever, there are compliance policies and regulations for the data that.

You know, people have to adhere to. So as far as preparing students to deal with compliance issues in the real world w how does that integrate into the curriculum there at wake forest? MSBA so

Chris Smith: [00:38:43] Great question. One of the most important things that I feel strongly about analytics professionals is we need to be ethical because you've heard the stories about, you know lies, damn statistics.

You know, you can crack a statistic to say anything you really want to. And so I think it's really important for us to hold the banner of being ethical and holding to the laws, policies, things like that, that, that are applicable. Not just, not just for whatever job you're holding, but as a person and as a human.

So we emphasize. Leadership and ethical use of analytics throughout the course. So there's at least two or three different parts, places where ethics and leadership kind of play into the, the core structure. And so I think by laying that foundation down, I think it'll make it easier for, you know, if you have an understanding that that ethics plays a part, a fundamental part in analytics, that when you get into a place like the intelligence community, or what have you, that has policies around the data use and management, things like that, you're more willing to abide by them because you understand that's just part of the way we do businesses.

We gotta have an ethical understanding of the data we're using. Does that? Absolutely.

Ted Hallum: [00:40:15] Yeah. Yeah, no, that's, that's perfect. You know, people who are looking at programs like your MSBA program, they're looking to get the S the skills that they need, and we're too to have wonderful gainful employment.

But then, you know, you also have to keep in mind, depending on where you end up getting employed, you want to continue to be employed and having those ethics in place and being willing to stick to them in certain industries is absolutely key. So if you find yourself in one of those industries, make sure you're always doing the right thing as prescribed by your by your industry, employer agency.

So I think Chris, that when people are looking to take their skills to the next level, to a graduate program, the first thing they look at is, you know, as we've talked about the prerequisites, can I get in, then they look at the curriculum and they say, is it gonna prepare me in the way that I want to be prepared to the way I think I need to be prepared.

And then people are concerned with, you know, how they learn. And that's where I think the delivery model becomes something that they are concerned about. And they want to know, you mentioned already that the MSBA, their workforce has multiple delivery models. So I'd love for you to take us through those and let students know what their options are for actually getting into this degree.

Chris Smith: [00:41:34] Okay. So first of all, there is a completely online MSBA program and it, it covers the same timeline. So it starts in late summer and goes until may. So about a 10 month program, the, the types of classes, the things you do are very similar to what you do. If you are on ground, it's just, it's all online and online is, you know, there's some content online that you have to work with, but you're also meeting with the professors at least once a week, sometimes more the on ground, if you're on ground before COVID every, you know, you had two classes a week you were meeting with your project partners off campus or, or what have you, as you needed to.

But it was largely a fully synchronous and in-person as COVID has gone through, we've been able to go all the way up to fully remote. But right now we're kind of hovering around the hybrid where there's some asynchronous content that's on there. So for for a standard two time meeting class, there's some asynchronous content out there that would cover the contact time for one meeting.

And then you actually meet in class once. And we try to reduce the section sizes. So there's not as many people in the classroom maintain the social distancing. I think what we'd like to do once we get a little bit of the herd immunity. And once we get cleared up from a lot of the COVID sort of stuff, we'd love to go back to the, on ground and fully synchronous and in-person variety, but right now, or we're hovering at the hybrid.

Ted Hallum: [00:43:25] Okay. Real quick on the, the fully online or the online version of the program other than those scheduled, like, it sounded like Office hours that you were describing is, is the rest of it fully asynchronous. And then also is it, are there ever any like on campus workshops that are required, no, some degrees require you to come for like a week or two and do a workshop or is it, they, they, they can literally live anywhere in the country and do the online program.

Chris Smith: [00:43:57] So I think that to answer your question I wasn't discussing office hours before. If you have a standard class online, you are having class in a zoom session with a professor at least once a week. If you're asking for office hours you can set that up as well and usually done over zoom, but you will have an in-person class at least once

Ted Hallum: [00:44:24] a week.

Okay. I'm glad I asked that to clarify. So the, the online version is not fully asynchronous. They will be certain times.

Chris Smith: [00:44:34] Okay. At least 50%, but it, there may be times where you actually have more meetings in zoom than than asynchronous

Ted Hallum: [00:44:44] content. Okay, perfect. So for our working professionals who are listening, make sure you have that flexibility in your professional schedule where you can hit those those required synchronous zoom meetings and stuff like that.

Yeah.

Chris Smith: [00:44:59] And I think, I think they, they take pains to understand that people, especially if they're doing the online program have jobs. So I haven't looked specifically at when they're scheduling, but the times are, but I think that they're generally reasonable to a working schedule.

Ted Hallum: [00:45:20] Perfect. So the next question, as far as the course work itself, How, what percentage of the coursework in wake forest MSBA program would you say is project-based?

Chris Smith: [00:45:32] So to answer your question it depends. So the coursework that revolves around the tools often the times is is, you know, there's some tests that are involved, but a lot of times there's also projects the visualization the analytics and the boardroom classes I was talking about it's largely coming in, giving presentations or giving like a vignette and the rest of the students watching, or, you know, videotaping it and the rest of students having to watch it and offer feedback and things like that.

The practicum class that starts in October and goes for the rest of the course is all basically project based because you're working on a real world project, you're dealing with a client. So I, I think that it is a mix again, going back to the fundamental of why we started the program and how it was started is we want there to be a strong emphasis in.

Actually doing this and applying it. So you see that in a lot of the classes as you go along.

Ted Hallum: [00:46:46] Fantastic. And I think that's also key because what I was talking earlier about how important hiring managers have told me these, these GitHub profiles are, if students have the opportunities to do project based work, then that means there's going to be output that they can then put in a GitHub profile and that's key.

So I think that's fantastic that you are affording people the opportunity to do their course where a lot of their coursework in that way. So Chris, I want to shift gears at this point because we've talked a ton about the program itself and the curriculum and the delivery model. But I feel like that's only about half of the equation because the other half the equation is the student.

You know, the student is coming into this program and. Each person is unique. Their learning style is unique. They have different traits and personalities. So I'm curious to know, as you've seen tons of different students come into the MSP program there at wake forest and probably some are just off the charts and the way that they Excel and maybe other struggle a little bit, what are some ways that or what are some traits that you see that are common earmark characteristics of those that just knock it out of the park when they come to your program?

Chris Smith: [00:47:56] So I think students who will succeed at Wakeforest are those that are ready to be a team players, because I mean, fundamentally analytics is a team sport. And if, if there are some people who are just bright students and they just love math and they just get it and they tend to do pretty well.

However, I would say most students are, who are in the population of the MSBA program are students who are interested in getting into analytics. Don't have a lot of experience in it right now are open to learning more about it, but are also open to working with each other, to try to understand together what we're doing is that it does.

And, and that I think, you know, again, keeping an eye on the whole point, the whole point is to come and get properly tooled and equipped for a career, doing analytics, data science, whatever, and out in the real world, you're most likely not going to be stuck in a vacuum to do this work. You're going to be on an analytics team, a data science team, and you need to know how to work with other people and inspire other people for great outcomes, because Nobody wants to work with a narcissistic genius.

That is the definition of a horrible day at work when you have to go and work with somebody like that. So, absolutely. I think that's a great answer. I think that that's maybe not something that a lot of people are thinking about. They're thinking about along the lines of, oh, I need to make sure I'm locked down on my linear algebra or whatever.

But there's some other really practical things just about being a decent human being and being easy to get along with that go a long way. And even if like, especially if maybe you're not the most technically savvy and you've got a long ways to go being a, a good teammate, people are going to be more apt to want to help you.

And, and, and you, you make progress so much faster because they will enable you to, to learn Just because they like being around you makes a huge difference.

I think there's a misconception that analytics people need to be those people with zero personalities. And they're just, they just love being in a dark corner and just love, like tap it away on their computer, but that's not the reality.

And, and looking back at those slides of what people are looking for, they're looking for people who can communicate results, who can communicate with, with stakeholders and better define problems. Yeah. We want people who can code and we want people who can apply analytics, but we want people with personalities and, you know, that's, that's also probably a defining technique in different MSBA programs.

Is there are NSB programs out there that are wholly technical based. And they're great. They're like you said, we need people like that into the, into the field, but that's not wake forest. We're interested in the whole student. Are you, are you yes. Going to understand these techniques and how to apply them, but you're also going to understand how to communicate the results, how to elicit from decision makers, how to work with team members.

Ted Hallum: [00:51:08] Yeah, absolutely. Well, and that goes back to what you and I were talking about a second ago, Chris, about the, the criticality of being able to tell a compelling story, if you're a one dimensional monotone person, good luck with that. Your story is probably not going to be very compelling.

Chris Smith: [00:51:23] Exactly.  Going back to what you were saying, that's another place that I think veterans can Excel is because.

The military is, is all about people. Yeah. We have ships or planes or, or tanks or what have you, but it's all about people and dealing with people. And most veterans have had a lot of experience dealing with a lot of different types of people. Not always successfully, not always unsuccessfully, but you had a lot of experience dealing with people.

And that's another thing I think that they bring to the program and to this sport.

Ted Hallum: [00:51:57] Absolutely. Well, Chris, I want to give people the most realistic yardstick possible to hold up against themselves and know, you know, is this the right fit for them because it's a great fit for some veterans and it's maybe not the most optimal fit for others.

And, and for anybody that is naturally inclined to do this type of work, I want to inspire them. I hope this podcast pushes them to take their. Analytics journey, their data science journey to the next level. But for maybe those that, that it's not the best fit. I also love it. If you'd hit on a couple recurring commonalities that you see among people that come into the MSBA program and maybe they don't do so well.

Chris Smith: [00:52:38] Okay. So I think we talked about overcoming math phobia and, and I think that's a real thing. And I, and I think that that is out there people who are not willing to open their minds and say, okay, just because I got a math problem wrong, that's not the end of the world.

Let me figure out how I got it wrong. And let me try not to do it in future math problems. People who aren't kind of flexible and Who aren't willing to make mistakes occasionally and learn from them probably will not succeed very well here because I mean, that's, that's the whole learning process is we're trying to get you to be fundamentally different from when you actually came in the doors and in order to change in a fundamental way like that, you've got to make some mistakes and you've gotta be prepared to learn from those mistakes.

So if you're kind of in a, you know what, I just don't want to make any mistakes and that's really important to me. This may not be the program for you.

Ted Hallum: [00:53:42] Yeah. That's key. I think people have to be able to fall down, stand up. Knock off the dust and have that resiliency, which I think, you know, that that's where again, veterans are tend to be a pretty good fit for this career because the resiliency, like they get that most of, if you're a veteran, it's very unlikely that you coasted through whatever duration your military career was and you never failed at anything.

Most likely you had some instance where things didn't pan out the way you wanted. And you had to come to terms with the fact that you could've done better. And then you had to make a plan of all right, when I'm posed with this in the future, how am I going to approach it differently so that I succeed?

So you just our veterans just need to take that same mindset that they had in the military and bring that to programs like the one that you have there at wake forest. So Chris, the next thing that I want to cover and this is kind of the, the, the logical conclusion is once people. Take their skills to the next level, through a program like yours, the thing that's on their mind is career outcomes, because that's the whole point.

So I'd love for you to think about that. All the students who typically graduate from your MSBA program, think about the three. And you know, universities tend to have draw, you know, they get a draw on a certain group of industries. They have a strong pull in certain geographic areas. So I'd love to hear about the top three geographic areas.

And I'm sure you have graduates that go all over the world, but the top three geographic areas that people tend to gravitate to once they graduate and the top three industries where your graduates generally Mo tend to find themselves.

Chris Smith: [00:55:27] So the, the, the geographic areas are largely a North Carolina based. There's a bunch of hiring here in the Winston silent Winston Salem area. But then also in the east coast, Southeast coast kind of region Georgia Florida those sorts of places, but then also up in New York, I mean, because, you know, there's, there's a fair amount of analytics in around the, the, the New York area.

So, I mean, if I were going to kind of characterize it, those were, those would be the, the main places. Although, like you said I would hesitate to call those the top three because we have people going literally all over.

Ted Hallum: [00:56:10] Sure. Sure. And then as far as the top three industries

Chris Smith: [00:56:14] Largely it's a consulting.

So people companies that do analytics consulting for other places or it's industry people who have a business analytics sort of job within an industry this is the headquarters for light Haines and like a couple of other businesses.

So those, those are kind of the top two, don't see too many in government. Occasionally you'll see a one or two going into sports analytics which is kind of neat little field.

Ted Hallum: [00:56:53] Yeah, absolutely. Okay. So let's go with what you, what you told me there. So the main geographic area being the Southeastern United States, and then the number one industry being consulting.

So let's take three different students, one student, no career experience. They've, they've done a bachelor's degree and then they rolled straight into your MSBA program. And then let's take student number two, their early careers. They've they've they've they did their bachelor's. They went out and got one to five or six years of industry experience.

And then they did your MSP program and then a third person mid to late career. Maybe they did a bachelor's degree back in the nineties, and then they felt like, you know, to take their career to the next level, they needed to go back and get a master's degree. And they picked it MSBA for each of those three hypothetical students.

They're in the Southeast, that's where they ended up getting a job and it's in consulting. Since those are the most typical outcomes, what can each of those three hypothetical students expect generally in terms of salary once they get out of your program?

Chris Smith: [00:58:04] Okay. So, you know, general bands of salary, I think frankly, between 70 and 80 K maybe some 90 Ks in there.

But that's, that's generally the band and frankly, I, I, we don't see too many mid to late career experience. People in the program, most of the people in the program are either no career experience. And then maybe they're, there's a couple of maybe a third half early career experience. And one to six years is not as much.

It's more like one to three years. So that's our typical population of students. So between 70, 80 K is kind of a good salary type, I would say, but it also depends on the geographic area.

Ted Hallum: [00:58:56] Oh yeah. Absolutely. Cost of living makes a huge difference. If, if you do have a student that ends up going to anywhere around like say Silicon valley or New York city, then naturally the, the the salaries are going to be radically different just so that they can afford a house or an apartment or whatever.

So at this point, I do want to ask you one question. I haven't asked any anyone associated with any prior college or university that's come on the show. And that is you know, I believe when people hear master science and business analytics, their mind naturally goes to, okay, so people will go into become business analysts or data analyst, but I would suspect you've had students graduate from your MSBA program and land in some other really interesting roles.

And so I just wanted to throw out there and see, do you have any vignettes about students that have gone on to do other things that aren't necessarily just business analyst or data analyst?

Chris Smith: [00:59:53] So there's, there's actually like a little subset. It's, it's interesting. There's a subset of people who take the master's of science and management degree and then go directly into the MSBA program.

And usually those types of people are people who are then about to spin off into a PA A physician's assistant sort of program because what they're what they realize, which is fascinating. I had never even thought about it, but you know, seeing what they do it makes sense is they want to know the business end of having a physician's assistance practice and know how to better understand their business and deal with the data of it before they go through the practice of, of being a physician assistant.

So that by the time they get out, they're ready to actually start a practice or get to a practice and improve it or something like that. So there's actually a little small about 10, 10, or so students, a, a group who do that

Ted Hallum: [01:01:04] transition. That's fascinating. I had no idea. I'm so glad I asked you that question.

I didn't, I didn't realize there was a niche of people that were pursuing analytics for that purpose. That's fantastic. Okay. Okay. So, you know, data science, analytics, machine learning, these fields are evolving all the time. And as I mentioned, it's totally plausible that there are people that will come across this episode two, three, four years from now.

So within the foreseeable future, say the next three years, what changes do you envision that will likely happen to wake forest MSBA program as it seeks to remain in step with industry?

Chris Smith: [01:01:42] So we have a board of advisors that we meet with habitually and I mean throughout the year and they are largely local, but people who are in industry essentially our clients.

So the people who hire the people that we graduate. So we keep in touch with them throughout the year to figure out what is happening with them. What's interesting for them. There are some of the main people that give us practicum projects. So that again, you're working on a real world client, but you might also be working for one of the biggest hires out of our program.

And they get a chance to kind of take a look at some perspective, students that are getting ready to graduate to see if they want to hire them. So I, I like to say that one of the good things about our program is that we will continue to remain flexible. We started off focusing on what industry is interested in and what do they want in business analytics professionals.

And I think that we will continue to keep that I think the things that will remain are going to be the communication skills and the soft skills that we teach in the program the techniques and the platforms we've explored. Looking at Python in the program, Python is a great tool. The thing is it's like a Swiss army knife and there's a lot of different things you can do with Python.

Whereas R is focused on analytics. And so we're, we're keeping with our, we have an eye on Python or other types of coding platforms to see what might also be available. So, I mean, we're, we're just keeping our finger on the pulse of what industry wants and

Ted Hallum: [01:03:36] letting that shape. Yeah. And that, that, that's perfect because that's what students are going to want.

They're going to want to make sure that when they graduate, that they've got the skills that the industry is looking for. So that's awesome that you guys have a panel of folks who are not just telling you what they need, but then also I heard you say providing capstone projects So then, is it, is it safe to assume?

And I, I, that's a question I should probably start asking. When, when students do a capstone project, is it always real-world data supporting a real client? Because if so, that's fantastic.

Chris Smith: [01:04:11] The, the, the practicum class that they start in October and go all the way through in may are all with real-world clients.

They've they've worked with banks here in Winston Salem with Hanesbrands with a local ACE hardware branch. The second harvest food, kitchen there's all sorts of different industries or people that that's part of, one of the things that we do as faculty is going out into the the area here and try to drum up interest in giving us participating in projects.

So yeah, they're all real world projects and they're all for real world clients in that practical. Awesome.

Ted Hallum: [01:04:55] Awesome. So for any one, listening to this podcast that ends up deciding that wake forest MSBA program is for them. You've heard Chris say there's quite a bit of project based work. That's stuff you can put in your GitHub profile.

We now know that there's a capstone real-world capstone project supporting a real client. That would be another fantastic thing to put in your GitHub profile to, to show, to future hiring managers. But then also it's not just some contrived capstone project with you know, an overused data set or whatever.

This is going to be real world messy data that solves a real problem for a real business. And I think that that's invaluable, you need to have one kind of real practice run if you will, before you're Before someone's paying you to do it.

So I think that's fantastic. So let's say that someone listening to this and they say I'm sold Wakeforest program, you know, is the program for me. For whatever reason, maybe, maybe the fact that you guys offer it online, but they can have re synchronous interaction with the professor. I think that's maybe a unique component that you've described for your program, that the other programs that have come on to the show don't have cause some people need that in-person ability to ask questions or whatever and real time.

But then maybe they're not right there in Winston-Salem, so they need the online component. So whatever it is, something about your program is appealed to them and they say, that's where I'm going to go. And they start applying and they. You know, things are going well with the admissions committee and they want to make sure that they're best prepared to come into your program and succeed.

What are things that you recommend students do so that they come in ready to hit the ground running on day one of the program?

Chris Smith: [01:06:43] So I mean, something we already talked about if they're not feeling too strong on probability and statistics since it's such a fundamental element of what we deal with it would be nice to, to try to knock out a Coursera probability and statistics class.

It'll get you into the practice of doing schoolwork, of doing assignment of, you know, doing some stuff. However, if you don't, I don't think that would disadvantage you at all. Because bottom line is, like I said, you're going to be off like a scalded dog, and you're going to feel like you just took a drink from a fire hose.

In that first summer, but that's okay because you're going through it with 113 of your best friends as well. And it is what it is. I think there's, there's not much you can do to prepare yourself within like a month or two. I really want to, what have you, we're going to teach you what you need to know.

So, so if it were me, I would just say, enjoy your time, enjoy the summer. Because once the time, once the program starts, you are going to be perfect and it's going to be tough, but it's manageable.

Ted Hallum: [01:08:00] Well, as listeners to this podcast know I did an MSBA program. It wasn't the one at wake forest. But it sounds like your program is every bit as rigorous as the one that I did.

And so I would Recommend to anybody who's listening and, and you know, is about to do your program prepare to work, prepared to work hard. I tell people that I w I, my program was 18 months long. And for that 18 months I did nothing. I wasn't working professionals. So I did go to work, but the rest of my life was just eating, sleeping and living that program... that's just what I had to do in order to succeed.

 Wrapping up Chris. There are a few last questions I want to ask you. And that's because our audience has, they have an insatiable appetite to learn and to build their skillset.

And so the first thing is. As they try to map out their future learning whether it's through MOOCs or through academic programs or whatever a certain point of that is geared towards making sure that they're targeting the right skills. So which data science and machine learning technologies get you excited today for the future.

Chris Smith: [01:09:13] So I guess, I guess it depends I noticed that there are a lot of technologies that are reaching out to the common person saying, oh, you can do this. Now. If you look at Excel, you know, you can create pivot tables, you can do all this, that you can be a data analyst. The, the fundamental untruth to that is yes, you can take numbers and you can manipulate them and make them say whatever you want to, but are you actually getting the results you think you're getting?

So I am really interested in, in. Data mining, I'm interested in decision analysis. Those are the two big things that I really that excite me. And I love doing those projects, but fundamentally it's understanding the assumptions that go into the models and that go into the, the techniques that you're using to better understand whether you should use this approach or this approach or this approach, and not just doing something just because you can.

So I mean, I guess the short answer to your question is data science and data data mining both structured and unstructured data mining. I am really interested in but then also decision analysis. Very cool. I like to, even though they're all these neat little tools that are reaching out to the populace saying you can be a data scientist, eh, it's it's not like that.

Yeah. You can move the numbers around, but. You can't really draw that insight from data and, and have it be reliable and scientific, unless you really kind of know what you're doing.

I mean, everybody, everybody looks well. I mean, what'd, you start getting into probability and statistics. You'll see, there are all these different names for tests to determine statistical significance. And you think what the hell it's just statistical significance is some results statistically significant.

Well, the reason why there are all sorts of different tests are because the underlying assumptions for those tests are different amongst each other. And you've got to understand that you've got to understand what those underlying assumptions are to be able to use that test or another one. And yeah, so,

Ted Hallum: [01:11:36] absolutely.

Well, so kind of spring boarding off that what are your favorite data-related podcasts or books and bonus points if they would help people to learn about those specific areas that you said you were

Chris Smith: [01:11:53] interested in? Well, so, so the specific areas, I might not get bonus points. I might not it may not talk to the specific points, but going back to the curriculum, you know, do they better understand, do you better understand the world and can you communicate the results? I really like Malcolm Gladwell's revisionist history podcast where sometimes he uses data, but sometimes he's not, he's just going back into historical records and he's finding things. I mean, I guess that is data, but he's not doing any kind of analytics aside from just thinking about things.

And his books tend to be really well done in terms of taking arcane analytic techniques, and tells you how they came about, tells you about them so that you've just better understand them. So I like Malcolm Gladwell.

Ted Hallum: [01:12:48] Very cool. I have not come across that one myself. I can't wait to tune into it and I'm sure that our listeners will enjoy that.

Cause that sounds very interesting.

I can think of a. Whole laundry list of reasons why people want to reach out to you.

They, they want to talk to you more about the MSBA program. Something that you said is inspired them. Maybe they have a background that they feel like they can relate to you and, and the path that you took through the army or, you know, maybe they just have some other question that they feel like after listing that you'd be the right person to reach out to.

I've got your email address here on the screen. Are there any other ways that you'd like for people to get in touch with you? Is there a way that's better than email even,

Chris Smith: [01:13:26] you know email is the best way. I have email on my phone. I kind of see it almost every day. It's how the students contact me.

So that's, that would be the best way to to reach me is using the email that you have up there on

Ted Hallum: [01:13:40] the screen. Awesome. Well, Chris, no, I've, I've thoroughly enjoyed talking to you about wake forest MSBA program. I think it's a fantastic entry point for people who are veterans to get into analytics, data science, and maybe even go into machine learning.

And I couldn't be more grateful for you coming on the show. Thank you so much for your time and this conversation.

Chris Smith: [01:14:02] Hey, thanks a lot for your time.

Ted Hallum: [01:14:04] Thank you joining you on this conversation with Dr. Chris Smith, as always until the next episode, a bids, you clean data, low P values and Godspeed on your data jury.