Online Mast of Business Analytics Program Overview

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Learn more about earning your online Master of Business Analytics from Ohio University.

Transcript

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Hello everyone and thank you for joining

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us for Ohio University’s online masters

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of Business Analytics webinar. My name is

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Brittany Smith and I’m one of the

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enrollment advisors for the online

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masters of the business analyst program and for

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today’s webinar presentation we will be

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discussing the Masters of Business

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Analytics in great detail so that way

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you can determine if the program is a

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great fit for you. So let’s jump right in

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and get started. So for today’s webinar

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along with hearing directly from the

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director of the program and the

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professor for this webinar we’ll also be

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discussing Ohio University’s background

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as well as the College of Business which

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the program stems from we’ll also talk

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about the curriculum and learning

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outcomes again to help you determine if

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the program fits your needs now at the

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end of the presentation we will cover

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the online admissions criteria for those

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that are interested in applying so that

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way you can further understand the

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process we’ll also have a Q&A session so

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please be sure to utilize the Q&A box to

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type in your questions and we will cover

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as many as time permits if your question

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is unable to get answered however please

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feel free to reach out to your

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enrollment advisor for further

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information hi so I like to introduce to

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you all our program director and

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associate professor of business

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analytics he’s going to talk about the

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business analytics program dr. bill

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young welcome thank you first and

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foremost I just wanted to thank

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everybody for attending live or watching

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this recording later we certainly

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appreciate you taking some time out of

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your day we know you’re busy working

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perhaps today so we appreciate the times

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that you spend with us I’ll just give

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you a brief overview

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of what my accomplishments and one of

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what I’m actually doing here at the

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University my background is in in

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engineering and when I was a mechanical

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repeat mechanical assistance Bhd student

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I just really got fascinated while

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working with a with general education on

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a cost estimation project that we were

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working on to predict the cost of

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manufacturing cost estimation times of

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assembly and inspection and all kinds of

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other things for these jet engines I

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just grew a passion for data analysis

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and then throughout my you know studies

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in a toe you essentially I would take

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you know more and more courses and do

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deeper dives into data analysis so I

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really changed because you know I

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started out being an electrical engineer

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and and eventually found my pathway and

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you know I’m super excited to be here at

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the College of Business and and helping

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the college you know really transition

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into a new state which is offering you

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know business analytics at our undergrad

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and grad level so as far as other things

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I do I you know I teach analytics I

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research analytics and even Hugh’s it

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for fun too so I I’d be remiss if I

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didn’t mention that as well so that’s a

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little bit about me and maybe we can

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have Jill nice use also with us

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introduce herself hello everyone I also

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would like to thank you for taking the

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time out of your day to learn more about

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our program it’s a program that we’re

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really excited about and hope that you

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can get excited about it as well

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I am the financial and operations

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officer for the College of Business

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graduate programs area and I also

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oversee all our student services and

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Student Success team so I’ll be working

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closely with the team to make sure that

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all of your needs are met while you’re

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in the program in terms of making sure

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that you know what classes you need to

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register for what textbooks you need to

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buy where to pay the bill all that good

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stuff so welcome and I hope to work with

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you over the next couple of years

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awesome great thank you both for those

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brief introductions and thank you again

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for joining us today it’s always a

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pleasure to hear directly from our

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program directors and associates so

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moving back to dr. young he’s going to

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cover with you all the program overview

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of the business illness program and

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what’s all entailed in our curriculum

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thank you again

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sure thing so let me give you an

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overview and let me kind of Express you

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know why Business Analytics and who this

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degree is really for because I think

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that’s an important factor to kind of

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stress during the webinar well first and

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foremost you know over the last decade

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even you know we’ve had an explosion of

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collecting data we now have things like

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Internet of Things where everything is

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connected where we’re generating social

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media data we’re generating data from

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CRM ERP systems that really is just

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untapped so there’s a lot of hidden

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information and valuable insight in all

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this data that we collect and we’re

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getting to a point where companies need

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an operational advantage by looking at

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the data that they have to make better

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decisions and to make better decisions

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in my opinion that’s all about reducing

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the risk surrounded by that decision and

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of course we know good decisions can

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lead to bad outcomes and bad decisions

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can lead to good outcomes but data

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analysis gives us another perspective to

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our own intuition and our own subject

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matter expertise that we can you know

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just augment and change and get another

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insight on on what the real course of

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action should be so that’s largely why

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we need it we need an operational

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advantage as a business so there’s all

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types of techniques out there that we

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need to master it’s all about

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discovering those hidden insights and

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the data we can talk about Big Data

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we could actually talk about small data

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which doesn’t necessarily get discussed

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a lot because big data is such a hot

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topic but there are several techniques

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that we need to them from a predictive

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standpoint we need to look at historic

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data and see what the future is going to

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look like we can use predictive tools to

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look at historic data

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maybe it’s transactional data and say

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hey is this customer likely to buy our

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product or service or not you know we

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can look at other recommendation systems

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and predictive analytics and say you

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know if this customer buys this and this

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customer is similar to this you know

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what is the next likely thing that I can

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upsell or cross-sell you know this

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customer to purchase excuse me so

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there’s a lot of capability so that’s

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just one pillar of analytics that we

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focus on there’s another pillar of

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analytics that we focus on which is

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prescriptive analytics prescriptive

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analytics is all about looking knowing

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your business developing usually fairly

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simple models around your business and

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optimizing resources so you can think of

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a manufacturing problem

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you know I have this this much raw

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material that these many components I

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have this many man-hours on machines and

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I have demand to fulfill you know how

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many of each certain product do I need

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to make to maximize my profit or to

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reduce my cost you know I have eight

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different warehouses and 50 different

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distribution centers and how do I route

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packages from each one of these in the

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most cost effective and safest perhaps

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way you could look at financial

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optimization and say what should my

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portfolio look like to maximize maybe

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profit or my return by minimize risk at

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the same time so two competing you know

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goals there and you know the third

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pillar is is where we start the program

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which would be descriptive analytics

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what does simply the data look like what

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is it trying

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tell us you know that that form of

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analytics we need a lot of intuition

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about our business and things like that

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and you know if I have to say that

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there’s a fourth pillar it’s about

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managing data so again we’ll need to

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know how to prepare data how to cleanse

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data how to get data ready for model

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generation or knowledge discovery and

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that takes a knowledge of SQL and data

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databases and and as we go further down

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along that conversation data is not

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always nice and neat

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you know CF structured data you have

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unstructured data for example Twitter

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Facebook text analytics it’s all

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unstructured so we need to learn

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techniques to structure that data so all

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in all you know we want data-driven

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decision-making at the company level and

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at this time I always tell my

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undergraduate students that they are at

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such a an important point in this sort

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of analytics journey because the demand

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of analytics is so high but the supply

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of analytics qualified professionals is

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so low that if they really have an

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interest in analytics they can really

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help their organization their current

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organization their new organization just

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like you all and you really can become a

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leader you know within your organization

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with with your analytics knowledge so

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it’s really really an important and

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exciting time and if I had to draw other

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inspiration from my undergraduate

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students which you know we’ve had a

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program for the last four years and our

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undergraduate business analytics major

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our job placement rates are near 100%

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not nine months after graduation but

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before graduation so the demand and the

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need the necessity for these skills is

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really at an all-time high

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wonderful

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Thank You dr. Jung for that

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very detailed business analytics program

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overview I hope that everyone listening

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is gain some insight from that

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information now we’re going to jump into

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more about the college of business more

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specifically as far as the excellence

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that our program holds and if you could

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please dr. Jung or Jill cover just a

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little bit about Ohio University more

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specifically on the College of Business

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itself

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thank you sure thing so the College of

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Business

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you know something near and dear to all

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of our hearts you know as a bobcat

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there’s there’s something must be in the

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water in the air here because once

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you’re a bobcat you’re always about cat

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nur’s there’s always just a certain

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passion you know that we feel for our

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programs and we feel for our students

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and in just the area in general so first

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and foremost there there is a lot of

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synergy here at the college which is

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just a tremendous amount of support to

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launch new programs like this to put

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people in teams and power to try to

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achieve the best possible learning

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environment for our students you know

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it’s just unreal so that’s that’s one

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thing I like to always mention in it and

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I also like to mention that you know an

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important part of our sort of brand is

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the faculty the staff and the students

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that we have

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you know it’s special so I always hear

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from people that are book reps that are

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client-facing with other professors at

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other universities and they say oh you

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it’s just such a different place I come

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to here I come on campus I meet with a

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professor there’s literally

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doors are wide open students are in the

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offices talking to their faculty

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students are in the hall waiting on

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benches to talk to their faculty where

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you know most other places it’s a

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closed-door environment hey good luck on

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your homework we’ll see you next week

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you know type of type of situation and

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we’re not like that we’re a teaching

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first Institute

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but we do put also a lot of emphasis on

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keeping up with state-of-the-art

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technologies and we do have a demand for

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research from a faculty perspective so

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it’s always always have to mention that

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because I always hear it and I always

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hear from students who aren’t former

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Bobcats what it actually means to be a

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bobcat and how much pride they have and

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how our faculty and staff alike get back

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to our students as quickly as possible

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and they’re just shocked that sometimes

0:13:53.290,0:13:58.270
within four hours five hours ten hours

0:13:55.990,0:14:01.270
whatever that we’re emailing them back

0:13:58.270,0:14:04.600
and answering their questions that that

0:14:01.270,0:14:07.180
you need answered so anyway our faculty

0:14:04.600,0:14:09.130
our staff are important when we’re

0:14:07.180,0:14:10.510
talking about our courses our curriculum

0:14:09.130,0:14:13.060
you know I could say we’re definitely

0:14:10.510,0:14:15.040
committed to quality one thing that’s a

0:14:13.060,0:14:17.320
differentiating factor amongst any grad

0:14:15.040,0:14:19.810
program is that we have our terminal

0:14:17.320,0:14:22.270
degree holding faculty developing and

0:14:19.810,0:14:24.280
teaching our courses so when you take a

0:14:22.270,0:14:28.180
class you’re not taking a class that’s

0:14:24.280,0:14:30.940
been maybe developed by a PhD and then

0:14:28.180,0:14:33.550
given that class to a grad student who’s

0:14:30.940,0:14:36.310
overseeing the course and your discuss

0:14:33.550,0:14:40.450
discussing topics and asking questions

0:14:36.310,0:14:41.320
to the instructor instructors assistants

0:14:40.450,0:14:44.380
or something like that

0:14:41.320,0:14:46.630
know you’re talking directly to that

0:14:44.380,0:14:48.250
terminal degree holding faculty in and I

0:14:46.630,0:14:51.610
would even stress in some of our latest

0:14:48.250,0:14:53.830
data it’s more than 80% of our faculty

0:14:51.610,0:14:55.830
who teach in our grad programs have the

0:14:53.830,0:15:00.270
terminal degree and actually in the

0:14:55.830,0:15:03.970
analytics program I can’t think of any

0:15:00.270,0:15:06.160
at the moment so you know we take a lot

0:15:03.970,0:15:07.900
of pride in and offering our students

0:15:06.160,0:15:10.150
direct contact to the subject matter

0:15:07.900,0:15:13.030
expert I will say if they’re not there

0:15:10.150,0:15:15.160
are the practitioners in industry and I

0:15:13.030,0:15:17.890
actually can’t think of one person in

0:15:15.160,0:15:22.360
mind Andy good night he’s a retired Air

0:15:17.890,0:15:24.340
Force captain and he works with us here

0:15:22.360,0:15:27.460
at the University and he’s retired now

0:15:24.340,0:15:29.650
but just had such a passion for Analects

0:15:27.460,0:15:32.800
I want to come back and teach and he has

0:15:29.650,0:15:34.330
a lot of experience as a meteorologist

0:15:32.800,0:15:36.790
actually in the

0:15:34.330,0:15:38.650
Air Force and the has a lot of

0:15:36.790,0:15:40.750
experience with data analysis and and

0:15:38.650,0:15:43.570
once you have those skills you know

0:15:40.750,0:15:46.240
analyzing whatever you know it’s almost

0:15:43.570,0:15:47.230
like a universal set of skills that it

0:15:46.240,0:15:49.240
doesn’t matter what data you’re

0:15:47.230,0:15:53.650
analyzing you can do it you know you can

0:15:49.240,0:15:56.500
do it for any field so our faculty are

0:15:53.650,0:15:58.420
special they are teaching focus faculty

0:15:56.500,0:16:01.630
who want you to learn and will do

0:15:58.420,0:16:05.140
everything they can even down to having

0:16:01.630,0:16:07.540
private conversations with you online or

0:16:05.140,0:16:09.300
whatever it might be and we’ve been

0:16:07.540,0:16:11.920
recognized for that talent and that

0:16:09.300,0:16:13.210
commitment to excellence you know every

0:16:11.920,0:16:15.790
year about this time we’re always

0:16:13.210,0:16:18.520
interested in the new reports so we’re

0:16:15.790,0:16:21.580
always rated as US News and World Report

0:16:18.520,0:16:22.960
best quality and best value which is

0:16:21.580,0:16:24.880
important because you know we’ll talk

0:16:22.960,0:16:28.960
about the credentials that we have at

0:16:24.880,0:16:31.090
our University namely AACSB

0:16:28.960,0:16:33.700
accreditation and we have that for both

0:16:31.090,0:16:35.470
our undergrad and grad programs and

0:16:33.700,0:16:37.330
that’s something that I’m not a lot of

0:16:35.470,0:16:40.570
programs in the world have I think less

0:16:37.330,0:16:43.960
than 15% if I’m not mistaken have that

0:16:40.570,0:16:46.440
rating so it’s always something special

0:16:43.960,0:16:50.640
so at the cost point at the price point

0:16:46.440,0:16:53.340
around $35,000 you know you’re getting a

0:16:50.640,0:16:56.920
AACSB accredited school that less than

0:16:53.340,0:17:00.460
15% of the business schools in the world

0:16:56.920,0:17:04.080
have in terms of value or also

0:17:00.460,0:17:07.830
recognized by fortune US News and World

0:17:04.080,0:17:10.960
Report’s as you know best online program

0:17:07.830,0:17:12.910
recently we our undergraduate program

0:17:10.960,0:17:14.710
was actually ranked which I think

0:17:12.910,0:17:16.180
there’s always similarities between the

0:17:14.710,0:17:18.970
quality you’re dealing with the same

0:17:16.180,0:17:21.840
faculty right so undergraduate program

0:17:18.970,0:17:25.690
was ranked 15th best public institution

0:17:21.840,0:17:27.370
by Bloomberg and when you actually

0:17:25.690,0:17:30.040
combine public and private we were

0:17:27.370,0:17:31.840
ranked 38th so that’s that’s tremendous

0:17:30.040,0:17:34.030
because of the hundreds of the thousands

0:17:31.840,0:17:38.920
of schools that were or in that

0:17:34.030,0:17:42.040
consideration when it comes to poets and

0:17:38.920,0:17:43.960
quants that’s a major outlet now about

0:17:42.040,0:17:46.860
where students get their information

0:17:43.960,0:17:49.860
from and the reason I want to talk

0:17:46.860,0:17:51.570
about the MBA rating because there’s a

0:17:49.860,0:17:54.059
lot of parallels between our MBA program

0:17:51.570,0:17:57.420
our online MBA program and the online

0:17:54.059,0:18:00.179
MBA n the analytics program so we

0:17:57.420,0:18:02.940
arranged 15 and 2008 by poet Zach wants

0:18:00.179,0:18:06.240
as one of the best online MBA programs

0:18:02.940,0:18:08.850
in the world so like I said there’s

0:18:06.240,0:18:11.640
parallels some we have an MBA program

0:18:08.850,0:18:14.910
with analytics if you want a shallow

0:18:11.640,0:18:17.280
dive and a deeper dive across general

0:18:14.910,0:18:19.500
business practices you know the online

0:18:17.280,0:18:21.080
MBA with analytics is probably right for

0:18:19.500,0:18:25.140
you if you want a deeper dive and

0:18:21.080,0:18:26.820
strictly analytics and strategy then

0:18:25.140,0:18:30.480
you’re going to want to take the online

0:18:26.820,0:18:32.370
MBA in program with us and what we’re

0:18:30.480,0:18:35.669
doing is leveraging the courses that we

0:18:32.370,0:18:38.160
have in the MPA ed and you know those

0:18:35.669,0:18:40.679
have been well received by students and

0:18:38.160,0:18:42.660
we’re adding on to that the deeper dive

0:18:40.679,0:18:51.000
into the newer courses that we’re going

0:18:42.660,0:18:53.730
to offer so I’d like to talk a little

0:18:51.000,0:18:56.309
bit about software because you know I

0:18:53.730,0:18:58.530
did initially really promote this as

0:18:56.309,0:19:02.720
much but you know when I’m hearing from

0:18:58.530,0:19:05.549
students and we’re interfacing with our

0:19:02.720,0:19:07.470
staff about you know getting questions

0:19:05.549,0:19:08.850
from our students a lot of questions are

0:19:07.470,0:19:10.740
centered around what kind of software

0:19:08.850,0:19:14.370
capabilities that we’re going to feature

0:19:10.740,0:19:15.630
in our program and I want to talk about

0:19:14.370,0:19:18.390
this because I think there’s some

0:19:15.630,0:19:20.790
strategy that you all need to know about

0:19:18.390,0:19:23.330
sort of the curriculum and this is a

0:19:20.790,0:19:27.450
handful of courses that we have and

0:19:23.330,0:19:30.510
essentially I’m involved with the first

0:19:27.450,0:19:33.000
sort of row of those classes and I’m

0:19:30.510,0:19:35.520
also involved lightly more of a

0:19:33.000,0:19:38.309
directing role with the other courses

0:19:35.520,0:19:40.530
but anyway what we do is we actually

0:19:38.309,0:19:42.360
start you out with a course called 6320

0:19:40.530,0:19:45.120
and that’s the course I teach every

0:19:42.360,0:19:47.640
semester and that course has been really

0:19:45.120,0:19:50.100
successful in a few different ways one

0:19:47.640,0:19:51.990
way that it’s been successful and the

0:19:50.100,0:19:53.580
reason why I’m bringing it up now it’s

0:19:51.990,0:19:57.179
because inevitably I’m going to get the

0:19:53.580,0:19:59.370
question well if I’m and they’re I use

0:19:57.179,0:20:00.419
the word math phobic if I’ve never

0:19:59.370,0:20:04.009
really had

0:20:00.419,0:20:06.960
a degree and something related to

0:20:04.009,0:20:08.999
analytics and I’ve never maybe tasted

0:20:06.960,0:20:11.159
some of that success or maybe I

0:20:08.999,0:20:13.889
struggled with that stats class way back

0:20:11.159,0:20:17.309
when you know is this program right for

0:20:13.889,0:20:18.809
me and I say absolutely and then I say I

0:20:17.309,0:20:21.119
get another question well I’m an

0:20:18.809,0:20:23.279
engineering student I’ve had quite a few

0:20:21.119,0:20:25.559
quantitative courses as this program for

0:20:23.279,0:20:28.529
me or should I think a data science

0:20:25.559,0:20:30.950
course or program and I say no you

0:20:28.529,0:20:33.570
should you should seek this one too and

0:20:30.950,0:20:35.489
you know it’s not because I want to draw

0:20:33.570,0:20:36.809
everybody into the program I want to

0:20:35.489,0:20:39.659
draw you in because I think it’s the

0:20:36.809,0:20:42.179
right program for you so what we do in

0:20:39.659,0:20:44.609
the program is to onboard all students

0:20:42.179,0:20:46.289
saying okay you may know this you may

0:20:44.609,0:20:48.389
not but we’re going to build your

0:20:46.289,0:20:51.210
confidence and build your set of skills

0:20:48.389,0:20:55.409
through a friendly environment of Excel

0:20:51.210,0:20:58.739
and Excel is such a useful and popular

0:20:55.409,0:21:02.279
tool that you can you can really master

0:20:58.739,0:21:04.649
what Excel can do for us until you at

0:21:02.279,0:21:07.409
the point where we really can’t work in

0:21:04.649,0:21:10.259
an Excel environment and when I say

0:21:07.409,0:21:13.830
really no Excel I mean I’m so passionate

0:21:10.259,0:21:15.570
about Excel it’s unreal I could probably

0:21:13.830,0:21:17.100
name 50 different shortcuts on the

0:21:15.570,0:21:21.419
keyboard that I use daily

0:21:17.100,0:21:23.100
but anyway hmm excuse me

0:21:21.419,0:21:24.989
you know when I talk about knowing Excel

0:21:23.100,0:21:26.460
you’re going to know Excel you know when

0:21:24.989,0:21:28.919
I talk to people all the time they know

0:21:26.460,0:21:30.629
Excel they don’t know Excel you know you

0:21:28.919,0:21:33.899
will shrink and it’s quite powerful

0:21:30.629,0:21:36.840
because it sets up your success for all

0:21:33.899,0:21:38.639
other courses that you see here so even

0:21:36.840,0:21:40.559
if you’re running you know some sort of

0:21:38.639,0:21:42.450
bigger data set or maybe doing some

0:21:40.559,0:21:44.909
pre-processing of your data for R or

0:21:42.450,0:21:46.859
Python you know that usually involves

0:21:44.909,0:21:49.759
some sort of Excel because it’s nice

0:21:46.859,0:21:52.859
it’s visual it’s a familiar environment

0:21:49.759,0:21:55.499
so my main point here and I’ll continue

0:21:52.859,0:21:56.850
but my main point in here is that we’re

0:21:55.499,0:21:58.139
going to start you off with a friendly

0:21:56.850,0:22:00.539
environment that’s going to build your

0:21:58.139,0:22:02.840
confidence and interest and analytics

0:22:00.539,0:22:05.519
then we’re going to move into other more

0:22:02.840,0:22:06.899
sophisticated software that is demanded

0:22:05.519,0:22:11.279
by industry today

0:22:06.899,0:22:13.830
so our R is a great tool R is free R is

0:22:11.279,0:22:14.340
community driven you know much like

0:22:13.830,0:22:17.220
Python

0:22:14.340,0:22:19.440
as well but you know it’s great because

0:22:17.220,0:22:21.960
organizations are wanting to use and

0:22:19.440,0:22:24.179
leverage the capabilities of our and

0:22:21.960,0:22:28.650
Python in particular so we have whole

0:22:24.179,0:22:31.110
courses dedicated to those technologies

0:22:28.650,0:22:33.659
so for example if I was looking at

0:22:31.110,0:22:36.630
predictive analytics one in the first

0:22:33.659,0:22:38.460
block of courses we do stay in an Excel

0:22:36.630,0:22:41.070
environment build your skills there and

0:22:38.460,0:22:44.490
leverage an add-in based on Frontline

0:22:41.070,0:22:46.350
systems front line solvers that’s

0:22:44.490,0:22:48.659
actually create add-ins for Excel and

0:22:46.350,0:22:49.799
then we’ll do all the modeling will do

0:22:48.659,0:22:52.559
all the pre-processing and

0:22:49.799,0:22:54.630
post-processing you know I could rattle

0:22:52.559,0:22:56.190
off the names of the algorithms we use

0:22:54.630,0:22:58.169
but we go through the gamut

0:22:56.190,0:23:00.779
you know linear regression logistic

0:22:58.169,0:23:02.039
regression discriminant analysis neural

0:23:00.779,0:23:03.630
networks

0:23:02.039,0:23:05.730
you know classification and regression

0:23:03.630,0:23:07.710
trees and clustering and all kinds of

0:23:05.730,0:23:09.929
other stuff and then when we get to the

0:23:07.710,0:23:11.820
predictive to course we’re saying okay

0:23:09.929,0:23:14.220
let’s revisit some of these things we

0:23:11.820,0:23:17.179
had in predictive one but let’s try to

0:23:14.220,0:23:19.409
integrate a new software technology and

0:23:17.179,0:23:22.980
explore those same methods and

0:23:19.409,0:23:24.750
additional ones in the our class and

0:23:22.980,0:23:26.250
then of course programming for analytics

0:23:24.750,0:23:29.700
you know you got to have an

0:23:26.250,0:23:32.190
understanding and you really should have

0:23:29.700,0:23:33.899
an ability to automate things and

0:23:32.190,0:23:36.779
develop things and create things and

0:23:33.899,0:23:39.870
there’s always a boundary between you

0:23:36.779,0:23:42.270
know how much creation that business

0:23:39.870,0:23:45.419
Anna analytics major would do versus a

0:23:42.270,0:23:47.909
data scientist you know the main

0:23:45.419,0:23:50.279
difference there is that data scientists

0:23:47.909,0:23:53.630
are more about creating new

0:23:50.279,0:23:56.070
methodologies and business analytics

0:23:53.630,0:23:58.980
analysts if you will are all about

0:23:56.070,0:24:00.659
applying existing methodologies now it’s

0:23:58.980,0:24:04.070
not to say the business analytics major

0:24:00.659,0:24:06.690
won’t create new ways of analyzing data

0:24:04.070,0:24:09.659
because all data is somewhat unique and

0:24:06.690,0:24:13.200
the situations that we have are unique

0:24:09.659,0:24:15.419
and the business domain is unique but

0:24:13.200,0:24:17.539
when I talk about new methods creation

0:24:15.419,0:24:20.600
I’m talking about very sophisticated

0:24:17.539,0:24:24.360
maybe pre or post processing

0:24:20.600,0:24:27.630
technologies that are very specific to

0:24:24.360,0:24:29.850
some applications so for your

0:24:27.630,0:24:32.100
off the application you know you’re you

0:24:29.850,0:24:36.390
want to be a business analytics person

0:24:32.100,0:24:40.020
and you know having an application focus

0:24:36.390,0:24:42.450
is actually very cool and very rewarding

0:24:40.020,0:24:45.750
because you can really impact the bottom

0:24:42.450,0:24:47.730
line of your organization and so wrap up

0:24:45.750,0:24:50.400
here by saying in terms of the software

0:24:47.730,0:24:53.250
used by saying the course goes all the

0:24:50.400,0:24:55.560
way to Big Data and that’s what the last

0:24:53.250,0:24:57.750
two courses are about so this is where

0:24:55.560,0:25:00.420
we’re beyond the capabilities of Excel

0:24:57.750,0:25:02.790
maybe we have a million records you know

0:25:00.420,0:25:05.310
which Excel can only have I think a

0:25:02.790,0:25:06.870
million rows but surely can’t process

0:25:05.310,0:25:09.570
that data even if you could open the

0:25:06.870,0:25:11.790
file with a million rows filled Excel

0:25:09.570,0:25:13.620
but you need to move on to bigger and

0:25:11.790,0:25:17.250
better things and that’s getting an

0:25:13.620,0:25:20.610
understanding of how our SQL works and

0:25:17.250,0:25:36.090
how to use the software surrounded from

0:25:20.610,0:25:38.910
that query language thank you again dr.

0:25:36.090,0:25:41.880
Jung for that insightful information so

0:25:38.910,0:25:44.550
now let’s cover a key part of our

0:25:41.880,0:25:48.050
program one that makes it stand out from

0:25:44.550,0:25:52.980
the rest it is a big part of our online

0:25:48.050,0:25:55.260
Masters of Business Suite programs this

0:25:52.980,0:25:57.870
leadership development workshop weekend

0:25:55.260,0:26:01.110
which I’ll have our Associate Director

0:25:57.870,0:26:02.460
of Operations and graduate programs Jill

0:26:01.110,0:26:05.370
nice tell us a little bit about this

0:26:02.460,0:26:09.020
wonderful opportunity that we have for

0:26:05.370,0:26:11.730
our students in this program yes

0:26:09.020,0:26:13.590
absolutely it’s my pleasure to always

0:26:11.730,0:26:15.330
get the opportunity to talk to our

0:26:13.590,0:26:17.760
students and our potential students

0:26:15.330,0:26:19.830
about this program we really do feel

0:26:17.760,0:26:23.580
like the leadership development program

0:26:19.830,0:26:25.710
that we’ve developed is probably one of

0:26:23.580,0:26:27.120
one of the most valuable aspects of the

0:26:25.710,0:26:30.270
program and it really sets our program

0:26:27.120,0:26:32.220
apart from other online programs we know

0:26:30.270,0:26:34.440
that maybe online students at first

0:26:32.220,0:26:37.140
glance don’t want to think about having

0:26:34.440,0:26:39.150
to come to campus and meet with people

0:26:37.140,0:26:41.280
in person and be here that’s not really

0:26:39.150,0:26:43.710
what an online program is about but

0:26:41.280,0:26:45.990
with our program we just really have

0:26:43.710,0:26:47.970
felt and have found in the past with

0:26:45.990,0:26:49.650
students coming here that it really

0:26:47.970,0:26:52.140
helps students feel connected to our

0:26:49.650,0:26:54.510
campus and connected with each other and

0:26:52.140,0:26:55.770
connected with the faculty and we just

0:26:54.510,0:26:58.110
think that’s a really important thing

0:26:55.770,0:27:01.500
that we’re going to do across all of our

0:26:58.110,0:27:04.980
online graduate business programs excuse

0:27:01.500,0:27:07.650
me we do hold to every year so one is

0:27:04.980,0:27:09.690
held every April and every August you

0:27:07.650,0:27:12.150
would only be required to come to one of

0:27:09.690,0:27:15.150
those workshops however your tuition

0:27:12.150,0:27:18.240
does cover up to three and that includes

0:27:15.150,0:27:20.160
your housing and most meals so for the

0:27:18.240,0:27:22.020
most part you just have to get yourself

0:27:20.160,0:27:24.750
here to campus and beautiful Athens Ohio

0:27:22.020,0:27:25.860
and we’ll take care of the rest and make

0:27:24.750,0:27:28.560
sure that you’re well taken care of

0:27:25.860,0:27:30.720
while you’re here but again you do have

0:27:28.560,0:27:32.100
the opportunity to network with other

0:27:30.720,0:27:33.990
students that you’ve been working with

0:27:32.100,0:27:36.390
in class and have gotten to know online

0:27:33.990,0:27:40.020
so it’s really cool to see people put

0:27:36.390,0:27:41.460
faces to names and really excite they’re

0:27:40.020,0:27:43.440
excited when they get here and get the

0:27:41.460,0:27:45.180
few folks that they know but we do

0:27:43.440,0:27:47.130
partner with the Walter Center for

0:27:45.180,0:27:50.060
strategic leadership which is a center

0:27:47.130,0:27:52.710
here in the College of Business and they

0:27:50.060,0:27:54.660
really keep the content fresh for each

0:27:52.710,0:27:58.020
of these weekends they bring in top

0:27:54.660,0:27:59.820
industry speakers that take care of the

0:27:58.020,0:28:02.070
keynotes for us and they’ll talk about

0:27:59.820,0:28:05.730
leadership and various other topics that

0:28:02.070,0:28:08.520
are current in the in the world at this

0:28:05.730,0:28:10.680
time but then you’ll also have breakout

0:28:08.520,0:28:13.530
sessions that will be for first-time

0:28:10.680,0:28:15.330
attendees or repeat attendees and then

0:28:13.530,0:28:17.670
you’ll also get to take part in breakout

0:28:15.330,0:28:21.030
sessions that are applicable to your

0:28:17.670,0:28:24.360
either your concentration or your degree

0:28:21.030,0:28:26.790
programs so because you would be in that

0:28:24.360,0:28:29.910
physical education program you would be

0:28:26.790,0:28:32.160
in concentration breakout sessions with

0:28:29.910,0:28:34.350
our online MBA students who are taking

0:28:32.160,0:28:36.330
the business analytics courses so just a

0:28:34.350,0:28:37.920
really another cool opportunity to

0:28:36.330,0:28:42.060
network with people who have same

0:28:37.920,0:28:44.760
interests that you do and I will say I

0:28:42.060,0:28:47.070
just want to add on to that that I you

0:28:44.760,0:28:48.930
know I’ve been a program director in

0:28:47.070,0:28:50.550
that online MBA program and now the

0:28:48.930,0:28:53.220
program director of the analytics

0:28:50.550,0:28:54.809
program but more specifically the online

0:28:53.220,0:28:57.629
MBA program for about

0:28:54.809,0:28:59.999
six years and when I came on board I’m

0:28:57.629,0:29:01.769
like what are you guys doing requiring

0:28:59.999,0:29:04.139
students that want to take an online

0:29:01.769,0:29:05.820
degree to come to Athens for two days

0:29:04.139,0:29:08.399
you know there’s a reason why they want

0:29:05.820,0:29:13.289
to take an online course and I was such

0:29:08.399,0:29:16.379
a skeptic of the LDP and it turns out I

0:29:13.289,0:29:18.360
was wrong turns out you know semester

0:29:16.379,0:29:20.879
after semester April in August after

0:29:18.360,0:29:23.519
those events closed there is such a

0:29:20.879,0:29:26.730
tremendous amount of feedback from the

0:29:23.519,0:29:29.730
positive feedback from our students that

0:29:26.730,0:29:32.129
they might also been skeptical but they

0:29:29.730,0:29:34.470
came and they really enjoyed themselves

0:29:32.129,0:29:37.259
and really learned about themselves in

0:29:34.470,0:29:39.480
terms of their leadership and what they

0:29:37.259,0:29:41.669
need to do and what they need to sort of

0:29:39.480,0:29:44.490
look out for and in their own

0:29:41.669,0:29:47.159
organizations to sort of self empower

0:29:44.490,0:29:52.019
themselves that it’s been a tremendous

0:29:47.159,0:29:53.369
success so I definitely ate crow but you

0:29:52.019,0:29:57.090
know I think it’s important to point

0:29:53.369,0:30:00.509
that out because you know we’ve had such

0:29:57.090,0:30:03.240
a zest with it that it’s it’s great yeah

0:30:00.509,0:30:04.139
I always leave those so energized but I

0:30:03.240,0:30:06.360
digress

0:30:04.139,0:30:09.499
let’s talk about the online masters of

0:30:06.360,0:30:12.379
Business Analytics curriculum so this is

0:30:09.499,0:30:15.509
definitely something I’m excited about

0:30:12.379,0:30:17.549
you know and I talked with my colleagues

0:30:15.509,0:30:20.879
in our analytics and information systems

0:30:17.549,0:30:23.700
department and really have created what

0:30:20.879,0:30:26.820
we think is really the best set of

0:30:23.700,0:30:30.539
courses that you can really have in a

0:30:26.820,0:30:32.279
thirty credit hour program and you know

0:30:30.539,0:30:35.070
as I walk you through I’m going to

0:30:32.279,0:30:37.379
relate back to the sort of stories and

0:30:35.070,0:30:39.840
the informations and the information

0:30:37.379,0:30:41.999
that I said earlier you know this is the

0:30:39.840,0:30:44.100
this is in general the sequence of

0:30:41.999,0:30:46.440
courses that you would take from the

0:30:44.100,0:30:48.929
very first course data analysis for

0:30:46.440,0:30:50.909
decision making which is something I

0:30:48.929,0:30:53.519
want to rename actually to descriptive

0:30:50.909,0:30:55.669
analytics so we start summarizing data

0:30:53.519,0:30:58.529
we start building your confidence

0:30:55.669,0:31:01.470
generating some reason a vagator Excel

0:30:58.529,0:31:03.690
taking the fullest capabilities of Excel

0:31:01.470,0:31:06.389
that you could possibly imagine and then

0:31:03.690,0:31:08.340
we in that course with a review of the

0:31:06.389,0:31:11.370
most important

0:31:08.340,0:31:15.510
probability and statistics concepts that

0:31:11.370,0:31:19.830
are applicable to analytics we take a

0:31:15.510,0:31:22.190
no-nonsense approach you know I love

0:31:19.830,0:31:25.760
statistics I love solving problems

0:31:22.190,0:31:29.010
related to probability but I know

0:31:25.760,0:31:31.340
there’s a limited application of some

0:31:29.010,0:31:34.290
things that you’ve had in college

0:31:31.340,0:31:36.330
hypergeometric binomial distributions

0:31:34.290,0:31:38.580
you know tell me what the business

0:31:36.330,0:31:40.800
application is for those you know it’s

0:31:38.580,0:31:45.300
very limited you know something like a

0:31:40.800,0:31:47.750
normal probability very very useful so

0:31:45.300,0:31:50.850
my message here is that we’ve really

0:31:47.750,0:31:54.840
taken a look at the curriculum and

0:31:50.850,0:31:57.720
really eliminated non-value-added topics

0:31:54.840,0:32:00.780
and reinforced them with extremely in

0:31:57.720,0:32:03.480
our opinion value value added subject

0:32:00.780,0:32:05.370
and skill building opportunities so we

0:32:03.480,0:32:08.220
do go through the descriptive we go

0:32:05.370,0:32:10.740
through the predictive predictive as a

0:32:08.220,0:32:13.320
tremendous amount of value to an

0:32:10.740,0:32:15.690
organization being able to sort of

0:32:13.320,0:32:21.210
predict the future and having that time

0:32:15.690,0:32:23.880
to take a step back and sort of prepare

0:32:21.210,0:32:27.470
for the future it’s important you know

0:32:23.880,0:32:30.570
whether dare I say layoffs occur or

0:32:27.470,0:32:34.470
restructure of your organization hiring

0:32:30.570,0:32:36.240
you know reallocation expansion you know

0:32:34.470,0:32:38.910
all these kind of business decisions are

0:32:36.240,0:32:41.520
related to knowing what the future is

0:32:38.910,0:32:44.130
going to look like and then we go into

0:32:41.520,0:32:46.520
predictive 2 which builds your skill set

0:32:44.130,0:32:49.830
and a tool that’s very high in demand

0:32:46.520,0:32:52.290
but Before we jump into predictive 2 you

0:32:49.830,0:32:54.750
know we we try to sprinkle some strategy

0:32:52.290,0:32:56.490
in there so there are definitely the

0:32:54.750,0:32:58.680
courses that are the skill building

0:32:56.490,0:33:01.680
technical courses and then there’s the

0:32:58.680,0:33:03.630
courses that focus on strategy you know

0:33:01.680,0:33:05.850
because if we’re focused on technical

0:33:03.630,0:33:08.550
skill bidding all the time we’re missing

0:33:05.850,0:33:12.180
out on some important concepts you know

0:33:08.550,0:33:15.090
like the ethical use of data you know or

0:33:12.180,0:33:17.040
strategic use of information systems how

0:33:15.090,0:33:20.430
do we acquire how do we manage these

0:33:17.040,0:33:22.070
systems to get the data we want so

0:33:20.430,0:33:26.789
there’s all kind of

0:33:22.070,0:33:31.380
strategic if I if I daresay strategic

0:33:26.789,0:33:32.970
strategy strategic use of type of you

0:33:31.380,0:33:35.309
know conversations that we’ll have in

0:33:32.970,0:33:37.700
our strategic use of information nation

0:33:35.309,0:33:40.049
and strategic use of analytics courses

0:33:37.700,0:33:41.730
prescriptive is all about optimization

0:33:40.049,0:33:44.520
all about dealing with the future an

0:33:41.730,0:33:46.320
uncertain future I might add trying to

0:33:44.520,0:33:48.480
make the best possible decision with the

0:33:46.320,0:33:50.820
knowledge that we have today you know

0:33:48.480,0:33:53.580
whether it be relying on forecasts of

0:33:50.820,0:33:56.460
demand and we’re trying to plan demand

0:33:53.580,0:33:58.860
or where the markets heading or you know

0:33:56.460,0:34:00.750
anything related to those business

0:33:58.860,0:34:03.659
environments we’re trying to use

0:34:00.750,0:34:06.090
optimization tools to to say what course

0:34:03.659,0:34:08.190
of action should I take right now you

0:34:06.090,0:34:10.649
know what is my portfolio mix look like

0:34:08.190,0:34:14.280
you know how many how many workers

0:34:10.649,0:34:16.830
should i reallocate to this sector to

0:34:14.280,0:34:19.889
get the most out of my organization and

0:34:16.830,0:34:23.760
fill my demand and use my resources

0:34:19.889,0:34:26.220
effectively what’s my 30 day planning

0:34:23.760,0:34:28.409
horizon look like you know do I need to

0:34:26.220,0:34:31.740
shift workers around what is my you know

0:34:28.409,0:34:33.179
overall work schedule look like when do

0:34:31.740,0:34:36.510
I need employees here when do I don’t

0:34:33.179,0:34:38.820
you know when do I not just all sorts of

0:34:36.510,0:34:42.510
interesting problems logistically how do

0:34:38.820,0:34:44.369
I ship you know from ABC and you think

0:34:42.510,0:34:46.260
your ups and you’re driving around town

0:34:44.369,0:34:48.450
and what’s the best course of action to

0:34:46.260,0:34:50.899
take to deliver the packages and the

0:34:48.450,0:34:53.970
fastest manner of time reducing miles

0:34:50.899,0:34:56.550
reducing your carbon footprint and and

0:34:53.970,0:34:58.800
while being on the safest Road possible

0:34:56.550,0:35:00.690
you know so it’s encompassing all of

0:34:58.800,0:35:02.820
these factors that are important to

0:35:00.690,0:35:04.349
business you know we’re data science

0:35:02.820,0:35:06.420
doesn’t look at that you know data

0:35:04.349,0:35:08.609
science is more about algorithm

0:35:06.420,0:35:09.990
generation and things like that we

0:35:08.609,0:35:12.599
actually have that’s why I think it’s

0:35:09.990,0:35:15.030
exciting that’s why as an engineer I’m

0:35:12.599,0:35:17.220
actually excited and more proud to be a

0:35:15.030,0:35:19.280
part of business analytics because the

0:35:17.220,0:35:22.830
problems are real we need real solutions

0:35:19.280,0:35:25.800
we need applicable solutions that that

0:35:22.830,0:35:27.359
really matter in today’s society so it’s

0:35:25.800,0:35:29.490
exciting because they’re challenging and

0:35:27.359,0:35:33.450
they’re fun and they’re rewarding to

0:35:29.490,0:35:35.640
solve once we do solve them then we move

0:35:33.450,0:35:36.090
on to something like programming for

0:35:35.640,0:35:37.770
analytic

0:35:36.090,0:35:40.590
which I discussed earlier it’s all about

0:35:37.770,0:35:43.110
Ottomans autumn is autumn is a ssin yeah

0:35:40.590,0:35:46.650
that’s a it’s about automating things

0:35:43.110,0:35:48.360
automating processes taking different

0:35:46.650,0:35:50.730
processes that you might have developed

0:35:48.360,0:35:53.670
in in other courses and putting them

0:35:50.730,0:35:56.040
together and a less manual fashion but

0:35:53.670,0:35:58.470
automating them to where you can explore

0:35:56.040,0:36:00.780
more and more and change more parameters

0:35:58.470,0:36:04.020
and look at the sort of effect that

0:36:00.780,0:36:06.990
those changes have that’s important

0:36:04.020,0:36:10.710
whether that’s sort of concrete and well

0:36:06.990,0:36:14.160
explained perhaps not but trust me it’s

0:36:10.710,0:36:17.040
important then we get into sort of the

0:36:14.160,0:36:19.670
BI courses you know like like I said

0:36:17.040,0:36:22.770
data is ever-expanding

0:36:19.670,0:36:26.010
we got more of it every second we have

0:36:22.770,0:36:28.470
faster computers better memory cheaper

0:36:26.010,0:36:31.010
memory better storage cheaper storage

0:36:28.470,0:36:34.230
cloud computing Internet of Things

0:36:31.010,0:36:35.820
natural language processing text

0:36:34.230,0:36:39.480
analytics whatever you want to call it

0:36:35.820,0:36:40.950
those problems are complicated and we

0:36:39.480,0:36:42.960
need methods of dealing with that

0:36:40.950,0:36:45.930
because they can really add value to a

0:36:42.960,0:36:48.330
business if we grasp that Yelp reviews

0:36:45.930,0:36:50.310
Twitter analysis sentiment analysis you

0:36:48.330,0:36:53.190
see it everywhere you know politics

0:36:50.310,0:36:55.320
restaurant reviews whatever so it’s

0:36:53.190,0:36:57.360
important and then we end up with sort

0:36:55.320,0:37:00.060
of an applied business experience which

0:36:57.360,0:37:02.370
i think is really unique it’s definitely

0:37:00.060,0:37:05.400
something I want to add to so I’ll try

0:37:02.370,0:37:07.950
not to forget applied business

0:37:05.400,0:37:10.290
experience that that course the two-hour

0:37:07.950,0:37:12.330
course you’ll be partnered so the

0:37:10.290,0:37:15.030
one-hour practicum is where you come in

0:37:12.330,0:37:17.310
but that two-hour course at the end you

0:37:15.030,0:37:21.990
come into a vironment where you’re the

0:37:17.310,0:37:24.600
lead analytics person on the team and

0:37:21.990,0:37:26.460
you’re working with MBA students that

0:37:24.600,0:37:29.550
might be from healthcare might be from

0:37:26.460,0:37:32.070
our executive management group might be

0:37:29.550,0:37:34.950
from accounting you know they might be

0:37:32.070,0:37:36.930
from operations you know might be from

0:37:34.950,0:37:38.760
entrepreneurship you know something like

0:37:36.930,0:37:40.500
that you’re going to have a team that’s

0:37:38.760,0:37:44.100
diverse of skill but you’re the

0:37:40.500,0:37:46.140
analytics person on that team and you’re

0:37:44.100,0:37:48.970
going to go through an experiment with

0:37:46.140,0:37:50.260
them experience with them as well

0:37:48.970,0:37:52.660
getting their understanding about the

0:37:50.260,0:37:54.760
problem you’re doing the data analysis

0:37:52.660,0:37:56.109
and trying to persuade the team to take

0:37:54.760,0:37:59.560
a course of action based on your

0:37:56.109,0:38:01.780
decision-making skill set and I think

0:37:59.560,0:38:03.160
it’s a unique opportunity to just

0:38:01.780,0:38:06.190
explore what it’s like in an

0:38:03.160,0:38:08.140
organization to be the analytics person

0:38:06.190,0:38:10.180
you know because you’re you know you’re

0:38:08.140,0:38:12.849
often going to get resistance everybody

0:38:10.180,0:38:14.500
wants to move into analytics but it’s a

0:38:12.849,0:38:17.349
slow process because people don’t

0:38:14.500,0:38:20.050
necessarily trust algorithms black box

0:38:17.349,0:38:23.430
models neural networks who are leading

0:38:20.050,0:38:26.770
the way and in self-driving cars and and

0:38:23.430,0:38:29.859
Watson and very high sophisticated AI

0:38:26.770,0:38:32.260
but managers don’t tend to under to

0:38:29.859,0:38:34.150
apply what they don’t understand you

0:38:32.260,0:38:36.250
know so it’s a challenge and you got to

0:38:34.150,0:38:38.920
be able to communicate and present your

0:38:36.250,0:38:40.420
findings in a way that really makes

0:38:38.920,0:38:41.829
sense to a broad audience

0:38:40.420,0:38:44.200
so that’s your challenge and I think

0:38:41.829,0:38:49.270
it’s a it’s a great challenge the

0:38:44.200,0:38:51.010
program will all in all is 30 credits 20

0:38:49.270,0:38:54.460
months so five semesters

0:38:51.010,0:38:56.410
there’s 11 courses each semester is

0:38:54.460,0:38:59.260
broken down into the first seven weeks

0:38:56.410,0:39:01.660
of a term and then the last seven weeks

0:38:59.260,0:39:04.300
of a term so you’ll be taking two

0:39:01.660,0:39:08.800
courses a semester but only one course

0:39:04.300,0:39:11.710
at a time and that’s very practical for

0:39:08.800,0:39:13.540
working professionals and just in

0:39:11.710,0:39:14.700
general everybody because you can focus

0:39:13.540,0:39:17.410
on one thing at a time

0:39:14.700,0:39:19.660
so you know this set of curriculum that

0:39:17.410,0:39:22.000
we have the our requirements the

0:39:19.660,0:39:24.910
techniques the theory the turning raw

0:39:22.000,0:39:28.510
information into actionable insight it’s

0:39:24.910,0:39:31.180
all centered around you know developing

0:39:28.510,0:39:33.839
of added value to what you can provide

0:39:31.180,0:39:43.119
to your organization or to an

0:39:33.839,0:39:46.839
organization wonderful thank you again

0:39:43.119,0:39:50.079
dr. Jung and zeal for that insightful

0:39:46.839,0:39:51.880
information now that you’ve had the

0:39:50.079,0:39:54.069
opportunity to learn more about the

0:39:51.880,0:39:56.140
program and hopefully determine whether

0:39:54.069,0:39:58.270
or not our online Masters of Business

0:39:56.140,0:39:59.960
Administration are somebody business

0:39:58.270,0:40:01.730
analytics program

0:39:59.960,0:40:04.100
meets your needs let’s learn a little

0:40:01.730,0:40:06.320
bit about the applicant and the student

0:40:04.100,0:40:08.060
expectations so in order to be

0:40:06.320,0:40:10.010
considered for admissions for the

0:40:08.060,0:40:12.260
business analytics program it is

0:40:10.010,0:40:14.270
recommended that all applicants have a

0:40:12.260,0:40:18.970
conferred and accredited bachelor’s

0:40:14.270,0:40:21.770
degree or higher with at least a 3.0 GPA

0:40:18.970,0:40:24.530
and since our program is meant for those

0:40:21.770,0:40:27.250
in the industry we also require a

0:40:24.530,0:40:29.560
minimum of two to five years of

0:40:27.250,0:40:32.210
full-time professional work experience

0:40:29.560,0:40:34.940
now with the work experience we are

0:40:32.210,0:40:37.460
looking for a career progression and

0:40:34.940,0:40:39.920
consistency so it is important that you

0:40:37.460,0:40:43.250
update your resume and make sure that

0:40:39.920,0:40:46.670
information is on there the program does

0:40:43.250,0:40:49.790
not require GMAT or GRE scores and we do

0:40:46.670,0:40:51.200
not accept any transfer credits if you

0:40:49.790,0:40:53.090
are uncertain if you meet these

0:40:51.200,0:40:56.030
requirements please feel free to reach

0:40:53.090,0:41:00.440
out to myself or your enrollment advisor

0:40:56.030,0:41:02.390
for assistance now once you are or have

0:41:00.440,0:41:04.610
met the requirements of the program and

0:41:02.390,0:41:06.830
you’re ready to complete an online

0:41:04.610,0:41:08.720
application you will need to provide us

0:41:06.830,0:41:11.510
with a few things we’ll need your

0:41:08.720,0:41:14.150
transcripts from all previously attended

0:41:11.510,0:41:16.070
institutions it is important that if you

0:41:14.150,0:41:18.650
have transfer credits into your

0:41:16.070,0:41:21.650
undergrad degree that you also provide a

0:41:18.650,0:41:23.810
copy of those transcripts as well we’ll

0:41:21.650,0:41:27.320
also need an updated resume or cover

0:41:23.810,0:41:30.230
letter to show that progression in your

0:41:27.320,0:41:32.540
work experience and consistency we also

0:41:30.230,0:41:34.550
require three letters of recommendation

0:41:32.540,0:41:36.860
with at least one coming from your

0:41:34.550,0:41:39.260
current supervisor or manager if

0:41:36.860,0:41:42.140
possible now with the letters of

0:41:39.260,0:41:43.940
recommendation it is very important that

0:41:42.140,0:41:46.520
you choose individuals that can speak

0:41:43.940,0:41:49.660
highly on your behalf in regards to your

0:41:46.520,0:41:52.670
overall work ethics and writing skills

0:41:49.660,0:41:54.530
and then we also need a personal goal

0:41:52.670,0:41:56.750
statement explaining your career

0:41:54.530,0:41:58.300
objectives and interest in the program

0:41:56.750,0:42:00.650
so this is going to be your personal

0:41:58.300,0:42:02.150
essay this is going to be your piece

0:42:00.650,0:42:04.250
that’s going to kind of tell your story

0:42:02.150,0:42:07.340
of why you’re interested in pursuing

0:42:04.250,0:42:09.200
this degree also why Ohio University

0:42:07.340,0:42:11.900
anything that sticks up to you as far as

0:42:09.200,0:42:13.490
your curriculum this is the key piece

0:42:11.900,0:42:14.839
for that as well

0:42:13.490,0:42:18.560
and then last but not least the

0:42:14.839,0:42:20.599
application fee is $50 now an important

0:42:18.560,0:42:23.450
topic when selecting a graduate degree

0:42:20.599,0:42:25.490
program of course is always tuition so

0:42:23.450,0:42:28.070
the online business analytics program is

0:42:25.490,0:42:30.950
financial aid eligible and the tuition

0:42:28.070,0:42:33.020
cost per credit hour for Ohio residents

0:42:30.950,0:42:35.630
is one thousand one hundred and

0:42:33.020,0:42:37.220
seventy-five dollars and one thousand

0:42:35.630,0:42:40.339
one hundred and ninety four dollars for

0:42:37.220,0:42:43.280
nine Ohio residents so the with that

0:42:40.339,0:42:45.680
cost per credit hour in mind the program

0:42:43.280,0:42:48.830
consists of thirty credits so your

0:42:45.680,0:42:50.780
overall estimated total tuition would be

0:42:48.830,0:42:53.270
thirty five thousand two hundred and

0:42:50.780,0:42:55.640
fifty dollars for in-state residents and

0:42:53.270,0:42:58.339
thirty five thousand eight hundred and

0:42:55.640,0:43:01.280
twenty dollars for non Ohio residents

0:42:58.339,0:43:02.750
and then more information on the tuition

0:43:01.280,0:43:05.150
cost if you need a breakdown of that

0:43:02.750,0:43:10.730
your enrollment advisors can definitely

0:43:05.150,0:43:14.450
provide that information for you awesome

0:43:10.730,0:43:16.910
so we’ve come to the Q&A session of the

0:43:14.450,0:43:18.710
webinar again ladies and gentlemen if

0:43:16.910,0:43:22.070
you have any questions please feel free

0:43:18.710,0:43:24.260
to utilize the Q&A box and we will try

0:43:22.070,0:43:29.450
to answer as many questions as time

0:43:24.260,0:43:33.140
permits so we have a few questions here

0:43:29.450,0:43:36.440
the first question and I like to direct

0:43:33.140,0:43:39.290
this question to dr. young the question

0:43:36.440,0:43:42.230
is what level of query language

0:43:39.290,0:43:45.530
experience is required for this program

0:43:42.230,0:43:50.859
so I guess coming into the program or as

0:43:45.530,0:43:57.020
part of the admissions requirements this

0:43:50.859,0:44:01.150
this presentation person is asking what

0:43:57.020,0:44:05.869
level of experience is required if any

0:44:01.150,0:44:08.780
the quick answer is none so you know I

0:44:05.869,0:44:11.990
think this program is developed really

0:44:08.780,0:44:14.450
for for people that have experience or

0:44:11.990,0:44:17.960
people that don’t you know I and I’m

0:44:14.450,0:44:20.540
being honest here that we’re going to go

0:44:17.960,0:44:23.390
at a pace that it’s going to be

0:44:20.540,0:44:25.700
comfortable you know to to a student

0:44:23.390,0:44:27.050
that doesn’t have the technical

0:44:25.700,0:44:30.830
capabilities

0:44:27.050,0:44:33.640
of all these things like SQL and and you

0:44:30.830,0:44:35.780
know programming skills scripting skills

0:44:33.640,0:44:37.369
all those things we’re going to develop

0:44:35.780,0:44:39.530
the course and the sequence of courses

0:44:37.369,0:44:41.360
in a way that’s going to build their

0:44:39.530,0:44:43.520
confidence that they’re going to be able

0:44:41.360,0:44:46.340
to at the end of the day do very

0:44:43.520,0:44:48.230
sophisticated things is it going to

0:44:46.340,0:44:50.510
require effort sure you’re going to

0:44:48.230,0:44:52.280
you’re going to need to keep on task and

0:44:50.510,0:44:53.930
things like that but but I will say

0:44:52.280,0:44:56.240
these topics are introduced in a way

0:44:53.930,0:44:58.670
that if you don’t have any skills but

0:44:56.240,0:45:00.290
you have a desired desire and passion to

0:44:58.670,0:45:06.560
learn it you’re going to learn it you’re

0:45:00.290,0:45:09.650
going to do well thank you our next

0:45:06.560,0:45:13.040
question is it seems as though the

0:45:09.650,0:45:15.950
program focuses on soft and/or technical

0:45:13.040,0:45:18.890
skills how does it still develop

0:45:15.950,0:45:20.750
leadership skills as well as if someone

0:45:18.890,0:45:23.210
is looking for that career development

0:45:20.750,0:45:26.930
it does involve leadership in some type

0:45:23.210,0:45:30.170
of way so how can this program help

0:45:26.930,0:45:32.420
develop those leadership skills well I

0:45:30.170,0:45:34.640
think one interesting aspect of this

0:45:32.420,0:45:38.060
program is what you discussed earlier

0:45:34.640,0:45:39.800
with the LDP and like what she said

0:45:38.060,0:45:42.140
you’re the tuition actually pays for

0:45:39.800,0:45:43.550
three of those events and during those

0:45:42.140,0:45:46.580
events you’re going to learn about your

0:45:43.550,0:45:49.790
leadership style and how you like to be

0:45:46.580,0:45:53.260
led and how you can effectively lead

0:45:49.790,0:45:57.200
others you know so that might be through

0:45:53.260,0:45:59.690
various self sort of exams where you’re

0:45:57.200,0:46:01.880
answering questions about various

0:45:59.690,0:46:05.840
situations it might be through

0:46:01.880,0:46:08.869
discussions with our experts in from the

0:46:05.840,0:46:12.350
Walter Leadership Center which is

0:46:08.869,0:46:15.410
something we’re very well known for and

0:46:12.350,0:46:19.550
it also comes into the strategic courses

0:46:15.410,0:46:23.109
about talking about real difficult

0:46:19.550,0:46:27.710
situations surrounding data security

0:46:23.109,0:46:30.290
ethics in a variety of topics you know

0:46:27.710,0:46:32.450
in communication is and also an

0:46:30.290,0:46:35.230
important part so you’ll you’ll actually

0:46:32.450,0:46:39.310
get a chance to develop your skills

0:46:35.230,0:46:40.940
through the courses develop your your

0:46:39.310,0:46:43.430
ability to communicate

0:46:40.940,0:46:46.900
effectively and work with others so for

0:46:43.430,0:46:50.119
example the last practicum and the

0:46:46.900,0:46:52.970
businesses simulation you know you’re

0:46:50.119,0:46:55.640
going to be required to work in teams in

0:46:52.970,0:46:58.010
that course most of the program you

0:46:55.640,0:46:59.599
won’t have to work in teams but in that

0:46:58.010,0:47:01.190
particular one you’ll you’ll work in

0:46:59.599,0:47:02.900
teams as well and that’s where you

0:47:01.190,0:47:04.609
really can apply some of those things so

0:47:02.900,0:47:08.960
leadership development the core

0:47:04.609,0:47:14.240
structure and and the overall capstone

0:47:08.960,0:47:17.240
experience at the end accident thank you

0:47:14.240,0:47:20.720
and so our next question here do we

0:47:17.240,0:47:23.540
accept financial aid for the program so

0:47:20.720,0:47:26.000
I’ll answer that question you can apply

0:47:23.540,0:47:29.540
online for financial aid through

0:47:26.000,0:47:31.490
fafsa.gov and if you are qualified then

0:47:29.540,0:47:33.890
you can certainly use that towards the

0:47:31.490,0:47:38.000
tuition cost of the program we also

0:47:33.890,0:47:41.450
accept tuition assistance programs from

0:47:38.000,0:47:42.770
your employer we also have payment plans

0:47:41.450,0:47:45.800
if you’re looking to pay out of pocket

0:47:42.770,0:47:48.829
and then if you’re using your military

0:47:45.800,0:47:51.470
tuition assistance or your GI bill we do

0:47:48.829,0:47:53.990
have a Veterans Affairs Office that we

0:47:51.470,0:47:58.420
can direct you to for information on

0:47:53.990,0:48:02.089
that or processing your paperwork

0:47:58.420,0:48:05.720
another question here a really good

0:48:02.089,0:48:08.260
question some candidates that come from

0:48:05.720,0:48:11.030
a math or technical or engineering

0:48:08.260,0:48:12.859
background would more than likely I’ll

0:48:11.030,0:48:15.800
be looking to pursue a master’s of

0:48:12.859,0:48:17.839
science and business analytics this

0:48:15.800,0:48:22.720
program is not a mattress of science

0:48:17.839,0:48:22.720
what’s the difference and why is it not

0:48:27.010,0:48:34.839
I’m sorry was that question directed to

0:48:29.270,0:48:37.369
me yes ok thank you could you paraphrase

0:48:34.839,0:48:39.160
quickly I was actually reading some of

0:48:37.369,0:48:42.740
the questions on the screen and then I

0:48:39.160,0:48:45.589
didn’t hear the last one I’m sorry oh no

0:48:42.740,0:48:48.079
problem dr. young so yes if some

0:48:45.589,0:48:50.420
candidates come from a mass or technical

0:48:48.079,0:48:51.920
or engineering background and they would

0:48:50.420,0:48:53.240
more than likely want to pursue a

0:48:51.920,0:48:54.290
master’s of science and business

0:48:53.240,0:48:57.020
analytics

0:48:54.290,0:48:59.570
so the question is why isn’t the program

0:48:57.020,0:49:01.180
not a matches of science and basically

0:48:59.570,0:49:03.950
what’s the difference between the two

0:49:01.180,0:49:06.109
big question this is actually like five

0:49:03.950,0:49:07.940
minutes ago when I said if I remember to

0:49:06.109,0:49:10.970
say something I want to get that point

0:49:07.940,0:49:12.530
out this is exactly it what is the

0:49:10.970,0:49:14.810
difference between a master’s of science

0:49:12.530,0:49:17.359
and just of masters and I don’t want to

0:49:14.810,0:49:19.820
suggest that sounds bad and a master’s

0:49:17.359,0:49:21.650
of business analytics I can tell you the

0:49:19.820,0:49:24.650
difference is a Masters of Science

0:49:21.650,0:49:26.960
program is going to require some sort of

0:49:24.650,0:49:29.780
new knowledge creation for the field in

0:49:26.960,0:49:33.070
terms of a thesis or some sort of

0:49:29.780,0:49:36.820
extended project which often relies on

0:49:33.070,0:49:39.320
working with your advisor which often

0:49:36.820,0:49:41.420
extends the time of your graduation I

0:49:39.320,0:49:43.190
might add in a master’s of business

0:49:41.420,0:49:44.930
analytics we are going through the same

0:49:43.190,0:49:47.869
curriculum you know there is no doubt

0:49:44.930,0:49:49.700
I’ve scoured every program you know in

0:49:47.869,0:49:52.910
Ohio and surrounding areas

0:49:49.700,0:49:54.619
the leaders in the nation I know what

0:49:52.910,0:49:56.300
the courses are know what they are

0:49:54.619,0:50:00.500
looking like you’re going to get the

0:49:56.300,0:50:03.170
same level you know in the courses as

0:50:00.500,0:50:06.230
you would in a master’s science the the

0:50:03.170,0:50:08.450
difference is you know if it’s truly a

0:50:06.230,0:50:11.569
data science you’re going to go into

0:50:08.450,0:50:13.700
more programming and creation of methods

0:50:11.569,0:50:14.810
if it’s a master’s of science you’re

0:50:13.700,0:50:17.480
going to have that culminating

0:50:14.810,0:50:20.810
experience at the end that produces a

0:50:17.480,0:50:22.940
new knowledge and I would say that’s

0:50:20.810,0:50:24.500
fine but I would say actually if you’re

0:50:22.940,0:50:27.440
working professional I would consider

0:50:24.500,0:50:29.869
this heavily you know when you get your

0:50:27.440,0:50:32.119
degree in five months you know you can

0:50:29.869,0:50:35.089
obtain that without having to do a

0:50:32.119,0:50:37.490
thesis now if you have a Masters of

0:50:35.089,0:50:40.130
Science program one of the hardest

0:50:37.490,0:50:42.020
things to do in all honesty I’ve been in

0:50:40.130,0:50:44.630
this environment for a while I’ve had

0:50:42.020,0:50:47.540
friends I’ve had you know acquaintances

0:50:44.630,0:50:50.329
that writing a thesis takes a lot of

0:50:47.540,0:50:53.079
self-discipline you know and it’s it’s

0:50:50.329,0:50:54.710
difficult especially with the number of

0:50:53.079,0:50:57.560
responsibilities that you might have

0:50:54.710,0:50:59.030
around your house and your your job you

0:50:57.560,0:51:01.040
know and I’ll just be honest I don’t

0:50:59.030,0:51:03.319
think there’s anything that’s too

0:51:01.040,0:51:05.900
different from the classes and I think

0:51:03.319,0:51:07.360
the biggest difference is that self

0:51:05.900,0:51:09.760
dedication

0:51:07.360,0:51:11.380
commitment of what it’s going to take to

0:51:09.760,0:51:14.170
produce that new knowledge for the field

0:51:11.380,0:51:16.870
and I say why bother with it get your

0:51:14.170,0:51:18.940
skills get out of here in five semesters

0:51:16.870,0:51:27.190
take those skills back to your

0:51:18.940,0:51:28.930
organization and apply it thank you so

0:51:27.190,0:51:31.270
that’s less of the common questions

0:51:28.930,0:51:33.550
actually that we get from a lot of those

0:51:31.270,0:51:37.390
that are interested in the program the

0:51:33.550,0:51:40.320
next question is if the Diploma States

0:51:37.390,0:51:44.140
online I can answer that question

0:51:40.320,0:51:46.620
basically the online masters of business

0:51:44.140,0:51:49.450
analytics program as well as the MBA

0:51:46.620,0:51:51.490
you’re getting the same value as a

0:51:49.450,0:51:54.820
brick-and-mortar program so same

0:51:51.490,0:51:56.560
structure curriculum nothing is

0:51:54.820,0:51:59.140
different there except for it the way

0:51:56.560,0:52:01.300
the program is delivered on the online

0:51:59.140,0:52:03.970
platform and because of that and because

0:52:01.300,0:52:06.370
of the accreditation and the value of

0:52:03.970,0:52:10.960
the program it does not State online on

0:52:06.370,0:52:14.680
your diploma or your transcripts the

0:52:10.960,0:52:17.400
next question comes from our audience

0:52:14.680,0:52:22.360
member here is they would like to know

0:52:17.400,0:52:25.360
will there be a somatic skills project

0:52:22.360,0:52:27.760
at the end of the program dr. young

0:52:25.360,0:52:30.160
would you answer that question sure

0:52:27.760,0:52:32.380
thing so that capstone the applied

0:52:30.160,0:52:35.730
business experience along with the

0:52:32.380,0:52:39.340
analytics practicum is that summative

0:52:35.730,0:52:41.080
project at the end in those two courses

0:52:39.340,0:52:44.230
which are taken at the same time they go

0:52:41.080,0:52:48.220
hand in hand essentially you’re put in a

0:52:44.230,0:52:50.410
team and we have a simulation that we

0:52:48.220,0:52:52.570
run like a true simulation computer

0:52:50.410,0:52:55.320
driven simulation of various

0:52:52.570,0:52:57.640
environments so textiles for example

0:52:55.320,0:53:00.790
that you have to compete with other

0:52:57.640,0:53:04.240
teams in your class and you have to make

0:53:00.790,0:53:06.640
decisions those decisions are not only

0:53:04.240,0:53:09.610
based in marketing operations finance

0:53:06.640,0:53:11.850
accounting decisions but those decisions

0:53:09.610,0:53:15.610
are augmented by the data analysis that

0:53:11.850,0:53:18.430
the analytics sort of representative has

0:53:15.610,0:53:20.840
to perform now whether the team takes

0:53:18.430,0:53:26.090
that information that you

0:53:20.840,0:53:27.950
provide them is really you know it’s not

0:53:26.090,0:53:29.390
guaranteed obviously so you’ll have to

0:53:27.950,0:53:32.150
figure out ways to communicate the

0:53:29.390,0:53:33.680
results and justify why you your

0:53:32.150,0:53:38.000
recommended course of action is the

0:53:33.680,0:53:39.910
right way so summative yes you can take

0:53:38.000,0:53:43.130
the data that’s generated by this

0:53:39.910,0:53:45.140
simulation you might want to run

0:53:43.130,0:53:47.960
optimization you want might want to run

0:53:45.140,0:53:50.150
some sort of predictive model you know

0:53:47.960,0:53:52.070
you might want to go to your descriptive

0:53:50.150,0:53:54.800
skill sets and summarize the data

0:53:52.070,0:53:56.150
visually but they’re all you know I

0:53:54.800,0:53:57.890
would say that’s the summative

0:53:56.150,0:54:01.130
experience right there but I would also

0:53:57.890,0:54:03.230
say in all of our courses you know that

0:54:01.130,0:54:04.970
we offer we are a very application

0:54:03.230,0:54:07.430
driven program and I can tell you and

0:54:04.970,0:54:10.310
show you you know pie charts that I

0:54:07.430,0:54:12.530
create about like going into various

0:54:10.310,0:54:14.030
business scenarios and I can tell you my

0:54:12.530,0:54:16.880
predictive course is more about

0:54:14.030,0:54:20.690
marketing it’s more about operations

0:54:16.880,0:54:23.150
production manufacturing and a little

0:54:20.690,0:54:24.650
less about financial applications and

0:54:23.150,0:54:27.020
things like that but I could tell you

0:54:24.650,0:54:29.470
the opposite for my operation or sorry

0:54:27.020,0:54:31.910
my optimization course where it’s more

0:54:29.470,0:54:34.640
financially driven and things like that

0:54:31.910,0:54:37.490
so you know I think in a way that those

0:54:34.640,0:54:40.010
are summative as well because you have

0:54:37.490,0:54:46.310
to apply what you’re learning to

0:54:40.010,0:54:48.830
multiple environments great thank you

0:54:46.310,0:54:52.550
so I see a number of questions coming in

0:54:48.830,0:54:55.580
in regards to the semester base how many

0:54:52.550,0:54:58.610
classes are taken for a semester and if

0:54:55.580,0:55:01.940
there’s a summer semester so as we

0:54:58.610,0:55:05.090
stated the program does have three

0:55:01.940,0:55:08.240
semester starts spring in January summer

0:55:05.090,0:55:10.100
in May of fall in August you do take

0:55:08.240,0:55:12.170
courses throughout the summer program

0:55:10.100,0:55:15.230
which is why the program would be

0:55:12.170,0:55:18.110
completed in as little as 20 months over

0:55:15.230,0:55:20.450
five semesters and so it does include a

0:55:18.110,0:55:22.370
summer semester and it’s one course at a

0:55:20.450,0:55:24.530
time for seven weeks

0:55:22.370,0:55:27.230
however you’re registering for two

0:55:24.530,0:55:31.180
courses every semester which is the

0:55:27.230,0:55:33.500
spring summer and fall which is 15 weeks

0:55:31.180,0:55:34.340
now ladies and gentlemen we have time

0:55:33.500,0:55:35.750
for one more

0:55:34.340,0:55:39.260
question I do see some more questions

0:55:35.750,0:55:41.120
coming in as I stated before if your

0:55:39.260,0:55:43.070
question is unanswered please reach out

0:55:41.120,0:55:45.770
to your enrollment advisor and we’ll

0:55:43.070,0:55:48.440
definitely follow up with you our last

0:55:45.770,0:55:50.870
question here is this program eligible

0:55:48.440,0:55:53.510
to those that do not have a quantitative

0:55:50.870,0:55:55.880
or business-related background if not

0:55:53.510,0:55:56.720
are there any prerequisites or what’s

0:55:55.880,0:55:59.570
recommended

0:55:56.720,0:56:03.950
I’ll gear that questions back toward you

0:55:59.570,0:56:07.070
dr. Jung sure thing

0:56:03.950,0:56:09.230
so so know if you come from an

0:56:07.070,0:56:11.330
engineering background great if you come

0:56:09.230,0:56:13.910
from a business background that’s great

0:56:11.330,0:56:14.390
if you come from any background that’s

0:56:13.910,0:56:16.910
great

0:56:14.390,0:56:19.100
so as I said earlier you know our

0:56:16.910,0:56:22.190
courses are set up in a way that are

0:56:19.100,0:56:24.380
very application driven you know will

0:56:22.190,0:56:26.930
you need a some sort of deep

0:56:24.380,0:56:29.840
understanding of accounting finance

0:56:26.930,0:56:33.440
marketing to solve these problems

0:56:29.840,0:56:36.230
no you know that’s my true belief I

0:56:33.440,0:56:38.330
teach a lot of undergraduate classes

0:56:36.230,0:56:40.670
I’ll be honest with you and they don’t

0:56:38.330,0:56:42.890
have the deep understanding of finance

0:56:40.670,0:56:44.510
marketing and operations but they’re

0:56:42.890,0:56:47.210
able to solve these problems or under

0:56:44.510,0:56:50.600
they’re able to understand the

0:56:47.210,0:56:53.090
high-level implication of why we’re

0:56:50.600,0:56:56.420
solving these problems but they don’t

0:56:53.090,0:56:59.960
have the deep dive and supply chain or

0:56:56.420,0:57:04.910
you know other functional areas so no I

0:56:59.960,0:57:07.010
don’t believe you do I believe truly in

0:57:04.910,0:57:09.050
my heart that as you take these

0:57:07.010,0:57:12.310
analytics courses you’ll actually

0:57:09.050,0:57:16.040
understand these functional areas better

0:57:12.310,0:57:19.190
because it’s more centered around again

0:57:16.040,0:57:21.320
the application so so if you want an

0:57:19.190,0:57:22.850
understanding of finance marketing I

0:57:21.320,0:57:24.980
think you’ll get that through just

0:57:22.850,0:57:27.140
taking the courses and and the way we’ve

0:57:24.980,0:57:29.150
structured our curriculum so you don’t

0:57:27.140,0:57:31.430
need it as a prereq and there’s no

0:57:29.150,0:57:36.040
prereq that you need to take before

0:57:31.430,0:57:36.040
being enrolled into the program as well

0:57:36.670,0:57:43.370
great thank you thank you again everyone

0:57:39.800,0:57:45.590
for your questions I want to thank you

0:57:43.370,0:57:47.190
for attending our online mattress and

0:57:45.590,0:57:49.560
business analytics webinar

0:57:47.190,0:57:51.690
dr. Jung and geo it’s always a pleasure

0:57:49.560,0:57:54.480
thank you as well for taking the time

0:57:51.690,0:57:56.520
out of your schedule today everyone you

0:57:54.480,0:57:58.800
are encouraged to use all the resources

0:57:56.520,0:58:00.450
available to you on your screen to speak

0:57:58.800,0:58:02.700
with myself or another enrollment

0:58:00.450,0:58:05.369
advisor also encouraged that you watch

0:58:02.700,0:58:07.560
our other concentration and program

0:58:05.369,0:58:09.180
webinar videos and begin the admissions

0:58:07.560,0:58:11.099
process we are now accepting

0:58:09.180,0:58:13.230
applications for our next term of

0:58:11.099,0:58:15.599
enrollment thank you again and on behalf

0:58:13.230,0:58:17.460
of the enrollment advisors for Ohio

0:58:15.599,0:58:19.770
University’s online masters of business

0:58:17.460,0:58:21.930
analytics program we look forward to

0:58:19.770,0:58:24.920
assisting you with your interests thank

0:58:21.930,0:58:24.920
you and go Bobcats