Master of Science in Data Science & Strategic Analytics Virtual Information Session

Hi, everyone. I see that we have some folks logging in. We'll give it just a second for everyone to load. We're so glad that you were able to join us for our live session. If you're watching it this at another time, prerecorded. Thank you for taking the time to watch this. And again, we're just going to give a couple of seconds to those who are joining live to be able to join in.

Right. It does look like we have everyone in uh. So let me kick off by introducing myself. My name is Jen Radwanski, and I am the associate director for graduate admissions here at Stockton University. And I am joined by some of my amazing colleagues here who are going to tell you a little bit more about their role with the Masters program in data science and strategic analytics. So just a couple of housekeeping things. First of all, we do have a chat feature if you're watching.

Live you can click on the chat button and we're happy to answer questions. However, I would recommend holding all questions towards the end because more than likely we will cover your question during the presentation today.

And if you are not watching this live, we are going to give you contact information at the end on how you can reach out with additional questions you may have or if you'd like to schedule an in person opportunity. And that goes for anyone who's joining right now or not to. We're happy to work with you not only through this session but also in the future if you are interested in data science and strategic analytics. So without further ado, I am going to turn things over to Cliff Baldwin who is going to be kind of the MC of this presentation.

And his amazing colleagues, whom he will also introduce. So Cliff, take it away.

All right. Thank you, Jen. So this is a presentation on the data science and Strategic analytics program. And we read several years ago, audiences are more relaxed and more attentive if there's pictures of cute little animals on the screen. So that's why on our title screen, here I have my 2 cats. Just keep everyone relaxed. We're going to talk data science, but but nothing scary.

Um, so, as Jen said, I'm Cliff Baldwin. I'm an adjunct here at Stockton and the data science and Strategic Analytics program. My day job, I'm a senior systems engineer at the Federal Aviation Administration, the Technical Center and right outside Atlantic City.

Prior to working at FA, I worked at the Bureau of Economic Analysis in DC. I'm also a member of the International Council System Engineering. And then my background is I have a bachelors in math. And then I went when I realized I needed a little computer knowledge on top of that one got a master's degree in information systems and then after that I got my doctorate in system engineering and in in.

In light of our data visualization, I made a little visualization for myself, our data visualization class. So that's that little logo I put together. I think that it's an attempt to sum up what I do. I do our programming data science. A systems engineer by day, data scientist by night I guess. And I've been applying my data science skills to food microbiology.

And so with that, I'm going to pass it to my colleague, Professor Kaman Dunn.

Hi everyone, my name is Kiana Dunn. And I guess before I get started I want to let you know that I was alumni in the program. So the the program is near and dear to my heart. So I'm one of the instructors in the DS a program. And just to give you a little bit of background about me, prior to coming to Stockton I worked in the Pharmaceutical industry and for about 10 years and.

Animal research and it was animals. I have a degree in animal science and after some time I decided to do a career shift and I went to Stockton, got my masters and here I am now and currently I'm a project manager doing.

Uh, disparity studies. And now I'd like to consider myself as someone who has taken the diet data science skills and applied it to my passion projects. So I do a lot of advocacy work, community building and social justice work, and just sort of you can see my bio there. But what I want you to kind of take away from me is that.

Um, data science can be applied to any background. So whatever your focus is, whatever your passions are, whatever your interests are, that's very important. And when you couple that with the data science skills, you could be amazing. And so the picture there is me at NYU law school presenting on some redistricting maps, and I had an opportunity to be a part of a team where we redo district lines for New York City and so.

If any of you know about redistricting, it's a it happens every 10 years. And redistricting equals money and power. And so I had a great opportunity to work on that project. And so outside of my data science and adjusting, I am a disco roller skater. So that's a little bit about me.

All right, Cliff, I'll pass it back to you.

Alright so as we get started I wanted to give this little story, this came out of the New York Times magazine I think it's a weekly insert New York Times about 10 years ago. So it's about the department store target just in case you're not familiar with targeted large change large chain department store in the in our area and across the country and target has this loyalty program and the loyalty program. You know I think everyone probably familiar you have a little card.

Swipe the card when you're paying. Supposedly they give you coupons based on what you buy or something like that. So behind the scenes target decided this would be a good opportunity to to see to use the loyalty card program to recruit people that we're going to have a baby to shop more target because mothers would be and and fathers to be do some of their shopping at Target but then would go elsewhere to buy other items when they were going to have a child.

And it it's a big a big money business for target if they could get those customers so target put together or gathered there. They didn't call the data science team at that time. It was their predictive analytics team. And they asked them to look at the data and figure out how they could bring in more of these mothers to be to shop at Target. And they figured out that when a woman or a family is going to have a a baby, they change certain buying habits.

And for example, women often change to unscented lotions and unscented soaps. So would that loyalty program, they could say that certain people, they could tell if someone's male or female and they could tell if they were going from scented lotion. So all of a sudden unscented lotions and and, you know, items like that. So then target would send out coupons to them and say here's a coupon for a crib or baby material, you know, diapers, whatever, because they have it available at Target.

So then there was a store in Minneapolis. One of the stores, um, a man came in and asked to see the manager, and he was quite irate because his teenage daughter, who was in high school, was getting these coupons for.

For, you know, baby material and the the manager, you know, with the manager of a local store. I'm sure this came from the corporate, so he had nothing to do with it. But the manager was quite apologetic and, you know, apologized to to this guy. The guy went home and the manager thought about it, you know, I guess he realized he would have been upset too if it had been him. So a few days later he called, he called this customer again to offer another apology. But this time the man was not so angry. And it turns out.

The teenage daughter was actually pregnant and the parents didn't know. So this story shows both the power and the risk of data science. I mean, it's quite powerful. Target was able to figure out this girl was pregnant before her parents figured it out.

Yet it also shows that, you know, with, as they say in the Spiderman and Marvel superhero movies, with great power comes great responsibility. Right at Target really shouldn't have been telling people someone was pregnant, so they backed off on that plan after that. But it just goes to show a little bit the power of what we're getting into when we talk data science.

So what is data science? If you're not aware already, it's taking some elements of math and statistics, some elements of computer science, because you have the code, right? We're talking about a lot of data to analyze. And if you have a lot of data, you can't do it by hand or with some little spreadsheet. You need to be able to code something and bring it in and then apply domain knowledge. And that's what Professor Dunn said a minute ago. You know, no matter what discipline you are you are in or come from or interested in, you could probably apply data science there because if you have.

Data on whatever discipline you're talking about, and then you apply the other skills. From day to science, you can get information as it says at the bottom, extract insights from messy data to increase the knowledge. Target did it, and I think they they're.

Heart was in the right place. It might have been for money, but it was sort of in the right place. How they started out, they just hadn't thought it through and they extracted some insights that maybe they shouldn't have. Hopefully in our program we only show or course you can use it anyway, but we, we teach that, you know, use it right in the right way.

For for good, use your powers for good.

I like this quote. This is a quote from the. I don't know if he's still the head engineer at Slack, but at the time the head engineer at Slack where he said a person, a data science is someone who's better statistics than a software engineer and better software engineering than any statistician. And I like that because when I started the reason in my bio I talked about how I started at the Bureau of Economic Analysis is I started as a statistician and they hired me because of my math background and also because I had some computer skills and but I wasn't a statistician.

But I knew more statistics than there are software people. And then as I said in that, you know, I got my masters degree in information system so that I would know more software than the statisticians.

So this, this quote speaks to me because I feel that's how I got into data science and when I was at the Bureau of Economic Analysis, we didn't have the title data scientist. I'm sure my title wouldn't be classified that if I were still there. Haven't been there in a while.

Um, so this is uh, a little overview we put just to show you how Ed Stockton in the program and I'll go a little more in the program in a few slides, but we try to cover the whole data science lifecycle. So we start out, you know what's the problem or question you're trying to address and targets case they were trying to increase.

Customers, right? People that are buying a baby.

Supplies. So that's identifying the question. Then we have classes on how do you acquire the data, how you get the data and clean the data and then analyzing the data, modeling the data goes into some machine learning and then stewarding the data. That's what apparently a target was lacking at their time. They didn't think about the ethics of what they were doing that, you know, it's fine. You know, a lot of people, when they're going to have a baby, they're quite happy and they tell everyone, great, I'm sure they're happy to get the coupons. But there are others, you know?

A private matter and target shouldn't be telling on them and in our stewarding.

OK.

Sorting data. I can't even remember the exact name of the class data stewardship class, right. And that it covers things like considering those ethical issues before going out and advertising what you found or actually looking for. And then at the end, we got the communicate that data you've gone through all the problem of of analyzing data, figuring out a solution to a problem or answering a question. And that's great, but if you keep it, you know, just to yourself.

Doesn't really add value to anyone but you. So we talked about how you communicated so that your point is well received and the full impact of it is appreciated by those who who asked to have it answered or would like to know that problem results.

Um, so that goes through our program. I believe Professor Dunn's going to talk about some of our.

Graduates.

That's good.

Yeah, so this I, I just love this segment of the of the talk because we get to highlight the students and their exceptional work and how each of them sort of kind of pave their own way, you know, based on their back like we've been talking about their background and their interest and what they want to pursue going forward. And so here we have Gavin, Aisha and.

Melissa and so Gavin came to us. He did have prior coding experience, but his passion was in law. He wanted to go to law school and so, so he was on the fence about that and he said, well, you know what, let me try out this program first because you know, I I just don't know. And so now Gavin is, he's a data scientist, but he's in public policy. So he was able to bridge his.

Um, political science. His interest in politics and data.

And now he's affecting public policy. He works at Rutgers now in their data science program as an instructor. And Aisha, she graduated about a year ago and no coding experience, and she worked as an associate at Home Depot. And last year she updated US and told us that she's a machine learning engineer at Home Depot. So she was able to. She started out.

Not really sure as to where she wanted to go and and so, but she had, she had a skill set. She worked at Home Depot for a number of years and an opportunity came up and now she's a machine learning engineer and then we have Melissa.

Melissa is, well, she's what got me into the program, and I'll tell you why. She was came into the program as a marine biologist with no coding experience at all. But she did have a passion for dolphin migration. I believe I'm saying that right. But she loved dolphins and has been working with them for years down in Cape May here in New Jersey and came in. She applied all of her her projects.

Were related to her personal project were related to dolphins and so this way she the tools that she learned in the program she applied to her homework assignments and her her her outside projects when it came to dolphins and she was able to visualize some of the the migrations of the Dolphins. I mean the the displays are beautiful and during her talk about her project I came to do a visit like you guys are like listening in on the program and I was blown away by.

What she was able to do the power that she had harnessed it from the program and and put into her her work and but now she's a research director and she charters a boat out and takes people out and does dolphin and and and well watching but it's it's it's been like full circle for all of these people they they started out not really knowing on exactly where they would land and have turned out to.

To be some, you know, they've had have started out with an amazing career.

And so next.

We want to talk about or give you an example of some of the the data science practicum projects. So and at the practicum project is what you would end with once the program is done and this is how you finish the program. So here we have Michael and Michael is.

He is very passionate. He's Ghanaian and he.

He chose to do his project on something that was near and dear to his heart, and since he's Ghanaian, he knew that the Ghanaian farmers struggled with keeping coca plants.

Delete disease free. So apparently there's particular diseases that that seek that attack coca plants. So anyway, he created a deep learning project that focused on detecting whether or not these plants either had. I believe the two diseases were black black pot and white frosty white. But anyway, the deep learning application was able to pick up whether or not the crops were affected and early on.

And it was a they would be able to treat the plants to save them. And so not only did he choose a project, that was.

Personal to him, but the impacts are huge because now these farmers, they can maintain their crops, support their families and help out the country, right? I mean that's I mean the coca plant is a huge investment for the this country and so he was not only able to just do the project but it has a huge impact on many other people and so it's still in the development stage.

And.

And we hope that he's able to, you know, do some great things with this this project. All right, Cliff, I'm going to take. Ohh. Go ahead.

No, I I think he also didn't have much of a coding background. I mean, he, he learned what he needed to learn. He knew what he was doing. You know, he had the principles for data science. He knew what he needed, and he picked up what coding he needed to do that as he went. But you know, looking back now, you wouldn't be able to tell by his end result that he didn't have it going in.

And I mean you that's a good point because it's something to keep in mind. As you know technology has just taken off and so it's impossible to know everything. And so with in the program we teach you the fundamentals to data science. But then when you're done you have to sort of go out and sort of figure out what you need to do to stay competitive or you know to to to take on the projects that you need to take on. But you learn the fundamentals, you get the.

With the baseline understanding of what data science is and then and where you can go is up to you. All right, Cliff, I'm gonna pass it back to you.

Alright, so these are just some resources and actually you know and now that I'm thinking about it, we should probably save this slide till closer to the end. But if anyone is interested in the program and would like to get started early, you know, sort of get a foot up on entering the program. We've put together some free resources and you know what, I'll come back to that slide in a second. Let me talk about the program and then I can say how they fit in. So the way our program is designed, it's designed that you can.

Finish it in one year.

You don't have to. You can stretch it out over multiple years. But it's designed so you can do that, and it's set up as hybrid fashion. You know, each three credit class, normally three credit course, you'd have to meet three hours a week.

We have it set up that you meet one hour per course per week and the other two hours are online. So as you can see in the fall, we have those three classes and they're all around data analysis, data visualization, data exploration.

And we meet Thursday night, all three classes, all three professors are there the same time, Thursday night. And we divided up based on when the professors can get their schedule. But it's, you know, six to 7788 to 9. We just tell students be there 6:00 to 9:00, and then you don't have to come any other day of the week, right? You're just there Thursday night and it's, you know.

The fall and spring are the the the heavier classes, right? That's where the coding, the math, the statistics party take place. So in the fall we do focus on data analysis. Then in the spring, same idea we meet one night a week for three hours.

And they're all machine learning sort of the, the modeling based type classes or or altogether there. And then we have summer sessions and the summer sessions are meant to be the, they're not the light. What do we call the?

People aspect of data science I guess, right? The soft skills, that's that's the word I'm searching for. They're the soft skills, so they're totally online. There's no in person requirement at all. Prior to the pandemic, will you just post everything on Blackboard on stock and web?

Portal. But now, thanks to the pandemic, we've all learned zoom. So we do have a few zoom sessions where we can meet with people, but we understand, you know, some are not everyone is required to attend. I think maybe one or two for the practicum we require, but that's it.

And so communicating data storage that's you know, going through the soft skills of how you communicate your finding and and the data stewardship that's the soft skills of.

Considering the ethics of the situation and and how to handle data appropriately. And then this all concludes with the data practicum and that's where you do your final project. And Professor Dunn just showed you Michael Bodens, if I'm saying his name right, his final project.

And we hope that you come in having an idea. If you have an interest, we'll help you through the whole program so that when you get to the data practicum, you can just work that into a hands on project with coaching from us.

If you don't have something coming in, that's fine too. We have a lot of people that explore different things in the fall and spring and and then when they get to the summer we, you know, have a conversation with them, help them pick a project to work on. And the data practicum isn't isn't the same idea where we're, we're having lectures or lessons or anything. It's a hands on experience and we're just there to help whenever you get stuck.

So that's how the program is designed and and if you take all the classes, like I said you can start in the fall and finish it in August. So you start in September, finish in August. But again you don't have to, you can stretch it out if that works better for you because it is a heavy load if you're working full time, but a lot of people have done it.

Um, this is a slide we put up here just to give you an idea of who teaches in the data. Well more than teachers now right in the DSA program and we, we put our specialties we we point out how we all know either our Python, we all know both. But what we're stronger with and that's because those are the programming languages that are used in data science the most. So. So we use them too. And again you won't become a software engineer you won't be able to say you're going to go do.

Crazy Python programs. But we get you started, and we have had people who go forward and learn it more and become crazy software coders.

And then I list everyone there. And at the end we have our Co chairs, Professor Bob Bolton who is with us today and Professor Joe Trout who.

I'm not sure. I do not see him here if he's here today. Um, if you wanna any, if you have any questions, you could always reach out to Professor Dunn, myself or Professor Olson.

And with that, I guess I'll turn it back to you, Jen, right?

Thank you so, so much. Um, So what I want to do now is just share a little bit, you got a really great overview of what what kind of a day in the life of a DSA student looks like, what is the course work look like? What would be expected of you? So I wanted to take just a couple of minutes and talk about what does the application process look like?

And so as you can see here, we've mapped this out just a little bit, but I'll show you where you can find this information on our website as well so that you can refer back to it. But there are a couple of components that I want to share with you all. So an application would it's all online. We have a really great brand new application process that's very easy to use. Once you've gone in, you will upload your materials and I'll talk through what those pieces are. So first you have your resume that you'll be sharing with the committee.

There is an application fee of $50.00. Once you pay that application fee, then and the essay for the program will populate. You'll be able to see that within the application you will need 3 letters of recommendation, and we really recommend that those recommendations are from people who can speak to your academic ability. Preferably one of them should be from a former professor that you've had as at your undergraduate level, or other master coursework that you may have had. So at least one of those, and then someone who.

And talk to you, you know, the area of study again to those who can speak to your academic ability, even if your next door neighbor has known you for 20 years and can speak to your credibility of being a wonderful neighbor. It's not the kind of information we're looking for in this application. Again, we're looking for how you will be academically successful.

Next requirements are that you must have a baccalaureate degree completed already from an accredited institution, and from there a minimum undergraduate cumulative GPA of a 3.0.

Um, we have to receive all of your official transcripts as well. And so even if you attended Stockton, even if you have maybe transfer credits that you transferred to another institution where you received your baccalaureate degree, every single place where you have received college credit, we will need a transcript from. That's just an institutional policy that we have. Every institution, if you go to their website and, you know, go to the registrar or type in transcript are very easy to find, but you do.

Have to have those sent to us and just a moment I'll show you where you can send that information.

And then we load those transcripts for you to your application. And I know that we have a number of folks who are interested in this program that are that may be joining us internationally. We do accept international applications for this program. And so another requirement would be receiving exam scores from the TOEFL or something similar, which I'll show you in just a second as well. The deadline for international applicants is March 15th. So I do want to highlight that it gives folks enough time to get their.

Their 20s completed and so that's why the deadline is a little bit earlier for our international population, for our international students. You also must submit your transcripts through an accredit, an evaluator. So that would be Wes ECE or span Tran and again I'll show you this in a moment.

So you notice our website is right here and I'm just going to show it to you quickly. This is our data science page, which a lot of the material that we just covered can be found here. So you can refer back to this. But in particular, I want to Scroll down and just show you the admissions criteria that I just shared with you. And you can see that mapped out along with the link to where to apply.

All of the curriculum material that was just covered as well is here, and so again, these are great resources for you to take a peek at after this session concludes.

On the left hand side of our website here, you'll also notice there's information again for our international applicants that I just mentioned. I really recommend going through that with great detail if you are applying to this program from outside the United States. But I also wanted to share with you that there's financial information that can be found here as well because I know many folks have questions about how do I pay for Graduate School. So if you're an in state student or an out of state student, you're going to be looking at the graduate cost.

Right here we also have doctoral costs down here. That'll be your next step, right? But for in state, this is the cost per credit along with a number of different ways that you can apply for assistantships, coordinator opportunities. And then at the bottom, we also have some scholarship information. You can also visit our bursar's website to learn more about payment plans and loans and applying for fasfa through our financial aid website. So I just wanted to highlight those.

Those little pieces and again, if you go to our graduate page and you go to our data science and strategic analytics page, you'll have all of those details right there, so.

That I think that concludes all of our material that we wanted to share with you um Bob, Kiana, Cliff. I want to thank you all so so much for for joining us and sharing your words of wisdom at this time. We do want to turn it over to our live audience that if you have questions please feel free to type those in the chat. We were happy to take some live questions from you all. If you are joining us watching this pre recorded you can see the contact information for Cliff and Kiana.

They're both there and my contact information. I will drop in the chat as well, but you can hear us. I'm also going to show you really quick. If you have back to this Stockton page that we mentioned, you can also go to the contact us. You can make a virtual appointment with me, or you can contact me through this site or contact us through grad school at stockton.edu, but I'll put that in the chat for everyone as well.

So again, if you have any questions, drop those in the chat. I will drop our e-mail in the chat too.

And Cliff, Kiana, Bob, anything else you'd like to add for the group today?

I don't think so other than maybe real quick I'll just go back where the coding resources because I sort of skipped it for the end. So if anyone's interested in the data science and you don't have to, but if you get the question a lot of the times, what can I do to help prepare? We put some free resources together here somewhere.

You are programming language. Some are for Python And some are. I think there's one for SQL, right? So just if you're interested, there's no quiz on it. You're not required to know any, it's just for your knowledge if that's what you want. Did you have anything to add?

Kevin.

No, I just want a second about the coding resources because this will give you an idea of what the program is about because you this is what you'll be doing.

So this is a great example of.

Coding and the coding that you'll be.

The coding aspect, yeah.

Right.

Alright, so that's that's all we've got Jen. Thank you.

Alright. Well, I don't see any questions in our chat. So I just want to thank everyone for joining and the last piece of advice I would share with all of you. One of the most valuable pieces of getting a degree from Stockton University is the ability to connect with our faculty. Our faculty are the best and I hope that you got that sense from this presentation. They care so much not only about the program but their students and if if you're looking for a place where you can really make connections and learn and grow stock.

The place for that especially with our graduate program. So just thank you all for being here, thank you for for supporting our students and we hope you all have a wonderful day. Alright, thank you so much.

Thank you.

Take care. Bye, bye.

Thank you. Bye, bye.

Right.

Today.

We think we're figuring out how to get out.

Yet for our presenters, you can just close out of your browser. Just close X out the whole browser and it'll log you out.

Alright, thank you.

Thank you so much.

All right. Bye.

Hi, Jen.

I am.