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Hello, I'm Ellie winter at Miami University Alumni Association, and welcome to this session of winter College 2021 for more than 17 years, when our college has been the alumni associations, premier Alumni Education event, we are so excited to be able to bring it to a broader audience on our virtual format, we have an amazing.
Over these two days, you can navigate a full schedule by clicking events by type on the websites selecting when our callers 2021, the drop-down menu feel free to join programs while they're in progress, can't make sessions will be recorded and posted online.
The only option is to might social event, which is full.
Will not be recorded our session today is from Big Brother to make data why Miami?
At the forefront or the data science field presented by Sandy stagger also joining today is Kevin Park.
As director of Miami University Center for analytics and data science.
Sandy is responsible for providing co-curricular experiences that allow steam.
[inaudible] the application of analytics and data science Prior to joining miami AND spent 15 years at 84.1 degree and done Humvee USA for most recent role, was Vice President data science and analytics, where she was responsible for developing a roadmap that would bring transfer innovative thinking to 84.51 degree focused on driving.
At scale across the enterprise.
The AND holds a Master of Science in statistics from Miami University and a Bachelor of Arts in mathematics and business from St.
Joseph University.
All right.
Let's get.
Started please, remember you can submit questions and comments.
During the live session by clicking, Ask a Question below the video.
Thank you so much, LE Thank you, everyone for joining us today I'm going to pull up a couple of things right now.
We're really excited to take you on this journey.
[SPEAKER] Through Miami analytics or the journey that we have been on over the last 20 years.
We're going to think through a bit about where we've been.
We're gonna walk you through some of the major milestones that Miami has tackled then we will.
Take a glimpse into what's happening today.
Where are we at present day with respects to analytics and data science?
And then we're going to give you a little not into the future and let you experience where we're headed and where we plan to go.
And it's in that part two where we're going to ask you all for a little bit of help as well.
So you actually have a role to play.
Our future plans.
I have heard through my kids that when you're live streaming like this, that one day you become YouTube or tiktok famous.
So I would say subscribe below, but I don't think that option exists.
But you can always subscribe to the Center for analytics and data science.
Are Analytics blog.
So feel free hit us up on our webpage at anytime.
Like I mentioned, we're gonna take a glands that are passed a peek into the present and then a nod to our future.
Let's start first with talking a bit about what it means to experience data science in our everyday lives we're all interacting with it whether.
Know it or not.
Think about when you Google something, is you start to type in your search into the search bar automatically, Google is predicting what you're going to say, Where are you going to type next based on things you've typed in the past.
And similar searches that others have made.
When you're on Facebook and you post a picture.
If noticed, Facebook has face recognition software that's data science.
That's machine learning.
It's figuring out who those spaces are in predicting who's in that picture based on who you posted about.
Another great example, I get an email, at least weekly, if not.
More often than that, offering up suggestions of shows that I might be interested in based on what I've watched in the past, the hard part about something like Netflix is if you have multiple people watching from one profile, then it's going to be a [inaudible] of predictions.
And suggestions, not necessarily based on you yourself, if you shared.
If you share that subscription I only mention that because I get a lot of Barbie videos which aren't something I generally want to watch, but my three-year-old enjoys watching that item here that I haven't the middle, the search up shit, right where the create ad here.
I was looking.
A pair of wedge drills and I also need a new pair of sunglasses for the summer a couple of years ago.
As soon as I started searching it, every ad that would pop up on my homepage anytime I was on the Internet would be for wedged sandals and sunglasses.
Those were ever ad that I was getting for awhile.
Also, it's learned.
From us, it sees what we're doing in it's trying to predict what we would like to buy and giving us suggestions based on what we've clicked on.
Pokemon Go, Pokemon Go was a huge thing.
I think my father-in-law still plays it to be honest with you, Pokemon Go is artificial intelligence clearly poking on Cuba characters are not in our houses.
Yeah, we can pull up the app on our phone, pointed anywhere we want, and the character will pop up and we go catch them.
I've never actually played it.
I assume that's how you play the game based on how I hear relative speak about it.
Alexa is another example of artificial intelligence.
It's voice recognition software inside of that as well.
You can ask Alexa any question, and my Alexa just spoke to me actually because I've said her name.
And then the last thing I have on here is Amazon.
Amazon, when you search for an item, right?
It's going to offer up other.
Or related items that you might be interested based on purchases that others have made.
Of similar products.
So all of these, all of these companies, all of the things we're talking about today are data science in real life.
So these are things that we all come in contact with and whether we realize that are you are all apart of this data science revolution.
I'd take a second throughout this section or throughout this top, we're going to hear from three different students our first student is linear Carter.
She is currently a bachelor student in the analytics and statistics field.
She's also.
Pursuing her computer science minor, 1.5.
Her step in and talk to us a little bit because she's going to share with us why she decided to choose analytics as her major.
So I came into college wanting to be a lawyer, and I quickly realized that was not the right path for me.
And that's how I originally got involved with statistics and data analytics.
In my sophomore year, I was involved with an independent study project through the stats department that involved.
Analysing a big dataset.
And through that, I was introduced to catch the next year.
So I've worked as a project in turn that cats for two years now.
I've worked with Development Office here on campus.
I worked with a third bank and I'm about to start a project with the Miamia tribe.
[SPEAKER] Just speaking with Lydia yesterday, actually, you're talking about this presentation and I said to her, I was just intrigued by the fact she came in and wanted to be a lawyer.
And now she's studying analytics and statistics and in fact, going on to get her Master's.
And she said she didn't even know that this world.
Necessarily existed when she was in high school.
She knew about analytics and stats, those classes were offered, but it wasn't something that she thought she was good at or that she was top of her class in, but lydia, putting the effort and she put in the time she has, she gets excited about a pulling out insights from, data and she uncovered that because of experiences and the opportunities that she was presented with during her time here at Miami.
There is a whole lot of stuff on this slide here and we are certainly not going to talk about all of it.
The reason that I'm sharing this with you is I wanted to compare where Miami is and has been compared to industry trends.
This space.
We are keeping pace AND oftentimes we're doing things at the same rate or before industry, industry trends.
The trends start picking up an industry.
What do I mean by that?
So back here in the early 2000 statistical programming, I was actually.
Part of the very first course that was launched in the Masters of statistics program.
But that's where part of the heart of data science is programming and using statistics to uncover insights and trends in data.
And it requires you to him.
Some sort of computer science programming.
There was a visualization lab that opened up in the early two thousands visualization.
So Tableau is a visualization tool.
It was founded around 2003, but we're keeping pace with these trends.
The more you hear about data science, the more you learn about data visualization and data.
These are all incredibly important aspects of the field.
It's not just the data, it's how do you share and communicate the insights from that data in a way that people are going to understand.
Data science became a buzzword in around 2008, that was by dy.
He was a I think it was the very first data scientists or chief data scientist for the US government.
But he was so he introduced this term, Data Science team made it a buzzword.
And that's when that same time two things started happening in.
Miami the Math and Stats department started to split.
Ai, has started to get added Analytics.
Majors are co majors were added into the curriculum.
And we started changing the terminology from analytics to data science.
So now when you're earning a degree, it's not necessarily just in statistics, it's more of a data science holistic degree.
You can see here to from an industry perspective, I put some things on here where Google Analytics launched in around 2012 ish, or maybe a little bit earlier than that.
But that's around the same time as you see that things were happening and things were taking form at Miami.
So we're absolutely keeping pace with the industry.
And I would argue that we're setting the stage, we're setting the precedent, we're creating the students that all of these companies are looking for when they're hiring well-rounded analytics and data science professionals.
I would be remiss here if I didn't mention that in 2015 the Center for analytics and data science was found.
And our purpose, which we'll talk about in just a minute, is really about providing students with the opportunity to develop data and quantitative literacy skills, not just those students earning quantitative degrees, but frankly across the entire campus because all students, all major.
All fields of study used data.
Now, that's the currency.
And we want to make sure that students understand and know how to use data.
So we think about where we are today.
I wanted to start off with hearing a little bit from a recent graduates, Sophie armor.
She graduated.
Last year with her bachelor's in finance, entrepreneurship, and she also earned a business analytics minor.
We're gonna hear from Sophie about why she wasn't about her experiences, her time, her interaction with analytics at Miami, Why did that set her up for success in that first career?
First job I took ISA to anyone regression with Dr.
[inaudible] myself where one year.
And she is how I got involved in cats.
Doctor asked me to join a project that she was going to lead with a bank.
This was the start of a very influential and critical real-world experience for me as it best paired me for entering the analytics field and learn how to work on a cross functional team as critical questions, they'll different models and draw impactful insights.
This was a huge, we even got to present at the client.
Something that is hard to find in some other co-curricular is personal engagement with employees and alumni.
It is one thing to hear them come in and tell you what they do or give you advice.
But what has been more impactful for me.
Is more of a mentorship or specific opportunities to gain skills so small projects or lessons that can really help you understand what it feels like, what you would do this will assist you to make the major like decision as to what you want to do with your life.
I was able to create a fantastic network and work on three projects through kids.
While also being involved in other things.
One of the things that I am a bit jealous if I'm being honest about current graduates or students that are at Miami now, are these types of experiences that they have?
I often said when I was at 8451, when we were interviewing some of the new graduates or the entry-level positions I used to say, I would never be hired today.
Based on my experiences were in the past because I didn't it didn't it wasn't as enriching.
And these types of opportunities didn't exist.
Now, sure.
1520 years ago, these types of opportunities in the world, in the field of data.
Science hadn't necessarily as a vault, wasn't as evolved as it is today, but I'm quite impressed by the caliber of students that I get to encounter and work with every day while at Miami when we think about this world of data science, there's this data science venn diagram that you see in front of you right now.
And the world of data science is made up of three different fields, or ways of thinking.
You have applied statistics.
So this is really the understanding of the theory and the application of the statistical models and the techniques of machine learning algorithms, artificial intelligence you also need to have programming skills.
It's not enough to just understand the Well there's definitely something to be said for understanding the output from these models.
But we also need.
To be able to code it up.
The problem, pull out the data, extract your data, manipulate that data, and then write the models, put it into production.
So that you can automatically run in the background.
And then you need the domain knowledge.
And this is where I'd say my experience.
It says we're much more in the Applied Statistics and domain knowledge.
I was more familiar with n, comfortable with the problem framing what's the problem we're trying to solve?
What sort of analytics or data science solution can we put on top of that to pull out insights and then drive that recommendation.
You have the unicorn.
Scientists, when you have all three of these fields coming together in one person, it's very rare to find somebody who is an expert or elite at all.
Three of these areas.
But the interesting and the really great part about what we're trying to do here at Miami.
And we'll talk about where we're headed in just a minute.
Is that we're bringing together all of these fields of study into one building, into one area to help drive and help develop the student's data scientists that organizations that companies are looking for.
There are many different roles within the data.
Science function though.
I don't want to make it sound as if the only type of person that accompany would hire as this uniform?
I was somebody who was much more of an analytics translator, which is a very important role to companies.
And I think that we don't want to minimize the impact of those types of positions as well as are the software engineers you need people behind.
The scenes who can program this up and put applications, put products into production so that they are running seamlessly in the background, similar to all of those data science in real life applications, we discussed.
But that true unicorn data scientists is something that all organizations are looking for.
We're going to help develop and provide those to the industry.
Cats I told you I was going to talk about us for just a little bit here.
We provide the data analytics and quantitative literacy skills that our students need when they enter the workforce.
We do that through three different ways.
One is educational opportunities.
Everything that we do is co-curricular, so it's not necessarily credit bearing.
We provide opportunities for students to get their hands dirty with data, to play around with that data, pool insights out, create visualizations in that story that is gone.
And to drive action, we do that through workshops.
And experientially learning opportunities as projects, real-world projects that some of our clients and some people like you all bring to us to solve for you.
We also try to create collaborative opportunities through partnerships.
With students or connections through students, faculty, and corporate partners a lot of this does happen through the projects that we work on but the more we provide this intersection of skills and ways of thinking, the better off we believe, not only will academia be, but industry will be as well, which leads into the.
Innovation side of things.
There is something to be said for the world of academics and industry professionals coming together to drive the innovation of the future it's not necessarily just going to be academics doing research.
That's going to find the new path.
Forward we really do need our industry partners come to us and say, how can we work together to find solutions to some of these problems that we're all facing.
That are more innovative in nature.
And we truly believe that by bringing these affinity groups together, cross-disciplinary groups, together, we'll be able to.
Drive that data science vision for the future.
That's a bit about where we are today.
Let's talk a little bit about where we're headed.
Certain or hopefully you all heard about the new data science building that will be on campus and I guess maybe three to four years.
They he generously made a donation to sponsor this building.
And the purpose of this building, what we're really striving for is that Miami is the academic epicenter for data science and we really do believe that this is something that can happen through a while it sounds really crazy that this building can provide that.
What it's doing is driving transdisciplinary partnerships.
We are bringing together the computer science department, the statistics department, and programs from the business school, all into this one building.
If that sounds familiar to you, reference back a few, a few slides back to the venture.
Venn diagram.
We're bringing those experiences altogether in one building.
So students have become exposed to all three fields, to all three of those worlds, and they start to develop those skills.
While at university versus learning some of that when they get, when they enter, enter their first job.
One of the reasons I was most excited to join Miami was I was training all of these new hires or all of these entry level data scientists and analysts when they came to 8451.
And I thought some of these skills should be taught at University they should be taught while.
There are in the four walls of that academic institution that's what we're trying to do here.
And we really believe we'll be able to provide those experiences to students through this mic, mic Bay Data Science Building.
Not all about just educating the students who are attending my AMI.
We also think about those learners outside of Miami, those who have left and want to continue.
Your you're still curious and you still want to be a continuing learner.
We have opportunity for you as well.
So I'd like to turn over right now to Kevin Park and he's going to walk you all through the corporate analytics training program that we are developing.
Alright, thanks Sandy.
Like Sandy in addition to developing the analytical skills of our students and campus community, The Center for animals.
We'll do this science here at Miami has been able to engage externally with our business and corporate partners by offering a corporate Analytics Training Program.
And the goal of the corporate analytics Dream Program ultimately is to help us grow by getting the most value out of their data AND WE hope TO accomplish this by offering and leading training program that is designed for industry professionals the modern workforce in mine and equip them with the skills, quantitative literacy, skills, in data analytics.
So currently we have two training courses that are available.
The first being Stats 101 or introduction to statistical methods.
And the second being the Data Science 101 training course.
In the first statistics 101 training course, we are introducing descriptive statistics, introducing statistical terms to really build up that quantitative literacy skills for industry professionals.
And then we work our way up towards how to we utilize the ban statistical methods such as confidence intervals for statistical estimation or how do I use statistical inference, hypothesis testing to answer business questions with data driven insight?
And the purpose of these courses is not to really provide heavy mathematical theory behind statistics, but how can I use statistical methods are out there to make my work easier, but also make the best insights from the data that I have.
Worked with in my day-to-day job.
So the second course being Data Science 101.
That's a little different from statistics because we are an a present different machine-learning algorithm.
Data science models that are widely used in industry to help provide those more advanced analytical skills or complex datasets that industries are often delving with today.
It's our plan through the Center for analytics, and data science.
To continue expanding our portfolio of training courses, including a data visually.
Of course, in a statistical programming course as well.
What's nice with this training program as well as that we've been able to deliver this material and not just one way the normal in-person live three-day workshop setting, but we've also been able to offer the training program in a way that combines a hybrid mode of learning, where students can access these materials virtually online, but also have valuable in-person environment through a synchronous session as well..
Thank you, Sandy.
Thank you.
One of the questions that we've received is, why are you creating this?
Why is it?
Why is this a benefit to people?
When I was at 8451 and not even just while I was at 8451, this is something that as I continue to read and research, organizations need to, are looking FOR looking FOR ways TO educate their existing workforce on how to use data, how to become comfortable with data and using data to make decisions.
That's what we want to be here to do.
We want.
To help you all feel comfortable with incorporating data into your day-to-day lives and knowing how to look at it, how to read it, and how to use that to make data informed decisions.
And that's the purpose and that's one of the reasons why we've put a lot of emphasis and attention into creating this training program is because.
As you know, Miami provides such wonderful educational opportunities to our existing, to the students who are on campus we want to continue that for the workforce that's out there today, and we want to help continue to advance the talent that's in the market.
I wanted to take a moment to, because not only did we want to talk about the things that are coming down the path, like the new building and the analytics training program.
These are all really wonderful, great opportunities and we're hopeful that once the building, building all can come in and experience it with us and feel the.
Energy of this space.
We really think that's going to be a powerful experience.
But I also wanted to think through some ways that perhaps all of you can help now, ways you can engage with students because I would assume that's one of the reasons you continue to come back to things like when our college and alumni events is because you're looking for ways to engage with our students body body population.
So I'd like for us to ticket turned over to Derek.
He is currently a masters student in our brand new business analytics program out of former School of Business.
There'll be one of the first year graduates this year.
Also, he is earning his information systems and Analytics degree with an economics minor.
Derek's going on.
To us a bit about basically his entire journey here at Miami.
And think what, why is it, what is it about Miami that has benefited him and has prepared him to leave these leave this campus at the end of this year and be successful in his, first in his first job.
Hello, my name is.
[STUDENT] Derek Davis and I'm a senior that will graduate with a Master's and Bachelor's in business analytics this may hearing that it might be easy to assume that I had my four years in Miami planned out before starting.
Believe it or not, though, I initially came to Miami to study business economics.
Was until I got into about the intermediate class level that I realized that can inform me and had to look for something else fortunately, at that time, I was taking a class called Applied Regression Analysis with Dr.
This Class and treat me about the topic of data analytics and secure recommendation that led to my job at the Center for analytics and data science or cats.
From that 0.2 2000 years ago, I've had the opportunity to work on many analytical projects and society on data analytics is my career path but few have been more impactful than my first project with cats.
Will this project was to build an interactive model to evaluate the true risk of staff behavior for a financial institution using both internal and external data.
This project was the first time that I came in contact with real-world messy data.
In our previous classes, data was very cleaned by comparison with few actions needed to actually prepare the data for analysis.
Here though it took us months to understand, integrate, and prepare a dataset, it was an eye-opening experience to what real-world data analysis means.
While I was definitely tiring at times, I thoroughly enjoyed the process and felt a great sense of satisfaction we were able to present our findings to the client this experience more than anything else taught me how to frame an analytics problem prepare data, managed client expectations, and think creatively about solutions.
There's also the first experience I just.
Grabbed in any professional interview I had from that point forward, as a result of had the opportunity to work in two internships in the financial industry.
And secure to roll with JPMorgan Chase as an analyst and their leadership development program after graduation.
I think the opportunities that I've had professionally largely stemmed from experiences like this.
I believe that additional opportunities for my students to work with business clients like this would be valuable for both the student and the client.
Experience really is the best teacher in my opinion, supervising these options will give students useful insight on what data analytics really..
Unique growth opportunities during their timeline.
[SPEAKER] We deliver our projects each semester to our clients.
It never fails that the first question they ask at the end is, what did you learn?
What was the biggest learning?
Learning outcome for you for this project?
And I think sometimes you'd expect it to be I got to use the new analytic technique or new data science model or machine learning model.
And I will say 95% of the time what the students say is I learned how much time it takes to clean data and if you don't have great data and you don't have good data, clean data.
The output will, mean nothing.
And I love that Derek focused on that because that's one of the things that I don't think we have enough for students.
So that leads me into how we need you.
Where can you what role can you play in helping our students prepare better, prepare for their time outside of Miami.
I'll start with mentorship and guidance and I think that both Sophie mentioned this as Lydia a bit as well.
They crave when when I started at Miami, I asked them What is it that you like most what is missing from your experiences with cads or even just your educational experience.
Your answers in general and the first thing most students would say to me is, I don't really know how I'm going to apply what I'm learning too.
Problem outside of, outside of my, How do we know what to choose?
How do we know when to use model that is trial and error.
But one way to help them get more comfortable and prepare is conversations with industry professionals and they are looking for opportunities to learn from all of you.
You have great rich experiences that you can share with students.
Sometimes that means sharing your failures so that.
They learn from them as well.
So they're looking for that opportunity to meet with you, whether one-on-one or in a small group setting, where they can almost work alongside you and see how you've taken some interesting new insights or how you attempted to solve a problem.
And then what that outcome what the output looks like.
The next area and Derek mentioned this is access to data and those real-world business problems.
I know some people get don't like that term of real-world, but for a lot of students, they say this doesn't feel like real-world what I'm doing in the classroom only because the textbook example holes are just not textbook data and it's not until you get your hands on that next messy, huge, big dataset that you really understand what it's like to think and work outside of the box any opportunity that students can have to play with data they don't necessarily have to be on the same scale that Derek mentioned and where they spent months cleaning the data it could be that we have just a couple of weeks of time with a dataset.
We did work on a case study last semester with a data analytics company and they provided students with workshop of thing we had two weeks and ten students than.
Just some data and they just have to solve a problem using the dataset that was provided.
So it gave them an opportunity to work quickly.
But also let them play around with some massive datasets.
And then another area is around exposure to applied machine learning.
When I mentioned that students want to learn from all of you what they're also looking for is come teach us and not necessarily in the classroom setting, traditional sense of a classroom setting, it could be through workshop and we're trying to facilitate a bit of this through cads where maybe they bring their computers to a session with you.
And you provide them with an anonymized dataset.
But they're coding with you and when they leave that workshop, they have that piece of code on their machine and then, you know how to use it they know how to apply it, and they can see, they can make that translation that leap from this is what I've learned in the classroom Oh, what a cool, a cool model or a cool method I learned.
And now I know how to really apply it to a business problem.
Then I might, might really be presented with when I, when I enter the workforce, these are three of the larger areas that I can think of or that I continue to hear from students as I talked to them about what is it that we can provide to you?
And I'd love it if any of you who are listening today or who are in the future and tuning in, please reach out to us here's our contact information and we'd love to brainstorm with you on ways to get you engaged in interacting with our students to help create those data scientists and analysts that future work is looking for.
Le alternative over to you.
Did we have any questions that came through as we're going?
We do have a couple of questions and they're actually both.
Pretty, similar Jeff who is from the class of 1980.
Before impact simply from class of 1972, will have a very similar question in terms of how does [inaudible] teach students about the ethics behind data science and the social implications of how data is used.
Not going far into privacy concerns, and how is that handled within the I love that question because cats actually facilitated session during I think it was in October, the university had their diversity and inclusion conference, and I attended my first one last year and I decided at that point in time, I'm gonna get on the agenda for next because I don't think we're talking enough about data ethics and algorithmic bias and those are things that are very important when you think about diversity and inclusion initiatives.
And just basic data ethics practices.
So what we, what we did in that session was provide a framework where ideas around.
What does it mean to have ethical data practices will our next step though, because we haven't necessarily brought this as a formal workshop to students, is exactly that.
Let's take what we did at that diversity and inclusion conference.
Students could participate, but for the most part, the thing goes backwards and staff.
Now let's bring that into bring that to students.
Now, in all fairness, I don't want to make it sound as if cads is the only one playing a role in this.
There are courses within some of the curriculum ON campus that do teach data ethics so that is absolutely happened in pockets but mostly if you're in those majors, so our goal would be to