Online Master of Science in Applied Statistics at Michigan Tech – Program Overview

Online Master of Science in Applied Statistics at Michigan Tech – Program Overview

Learn more about our Online Master of Science in Applied Statistics program, by tuning into this informative webinar presentation featuring Dr. Mark Gockenbach, professor and Chair of the Department of Mathematical Sciences at Michigan Tech and Mary Katherine Howard, admissions counselor for the Master of Science in Applied Statistics program.

00:02 Ashley Zeman: Hello, everyone, and welcome. Thank you for joining us for today's info session about the online Master of Science in Applied Statistics at Michigan Technological University. Before we get started, I'd like to cover a few housekeeping items. You are in broadcast only mode, which means you can hear us, but we cannot hear you. Please feel free to type any questions into the Q&A box at the bottom of your screen at any point throughout this presentation. We have reserved some time at the end to answer your questions, and we hope you enjoy the presentation. Here are our speakers for today's webinar. I'm Ashley Zeman. I'll be your moderator. I'm joined by Mary Katherine Howard, an admissions counselor for the program whom I'm sure some of you have spoken with already. 

00:52 AZ: Mary Katherine can help answer questions about the admissions process and also assist you in getting all your application materials submitted to the selection committee. We're also joined by Dr. Mark Gockenbach who is a professor and Chair of the Department of Mathematical Sciences at Michigan Technological University. He received his PhD in Computational and Applied Mathematics from Rice University in 1994 and held faculty positions at Indiana University and the University of Michigan before joining the faculty at Michigan Tech in 1999. As a visiting lecturer for Indiana University, he taught at the MARA Institute of Technology in Shah Alam, Malaysia from 1990 to 1992. He has also served as a volunteer lecturer for the International Mathematical Union offering courses in Phnom Penh, Cambodia in 2013, 2015, and 2016. 

01:56 AZ: Professor Gockenbach's research interests are in numerical analysis, specifically the numerical analysis of inverse problems. His research has been published in journals like the Journal of Inverse Problems, Mathematics and Mechanics of Solids, and the SIAM Journal on Numerical Analysis. He is the author of five books on partial differential equations, finite element methods, linear algebra, and inverse problems. Welcome, Dr. Gockenbach. 

02:32 Dr. Mark Gockenbach: Thanks for having me. 

02:36 AZ: Here's a quick look at our agenda for today. We'll talk about Michigan Technological University and the online applied statistics program, including the curriculum and the demand for statisticians, then dive into the admissions process, and finally leave time for any questions you might have at the end during our Q&A session. Mary Katherine, I'll turn it over to you now. So, tell us a little bit about Michigan Tech and the online experience. 

03:00 Mary Katherine Howard: Thanks, Ashley. Michigan Tech is a traditional ground campus university. We were founded in 1885, and we're located in Houghton, Michigan. We have over 120 programs and 70 graduate degrees. We have been ranked number 25 in the nation. We're seven schools by Forbes as well as ranked by Forbes for 12 with public universities having the graduates, the highest mid-career salaries. And lastly, another important statistic is that we were ranked number 18 by for return on investment. So, a little bit about the online Master's and Applied Statistics program. The program is at 10 courses total, which is 30 credit hours. You take one course every seven weeks, so that's two courses per semester. This is an accelerated format, it does run continuously from enrollment to graduation and can be completed in just under two years. We do have three start dates per year, and next one coming up in May, and our program is 100% online. 

04:14 MH: As for the online experience, even though the program takes place online, it's still taught by the same faculty members that teach our on-campus program. So, you're still receiving the same quality and rigor that you're going to be looking for through an online program. You will receive one-on-one attention from your faculty members and consistent communication from your student success advisor. Additionally, these courses were created specifically for the online student, so, all of the resources and software that you need will be available to you. 

04:52 AZ: Great. 

04:53 MH: And now, I'll turn it over to Professor Gockenbach to tell us more about the program, curriculum, and the demand for statisticians. 

05:01 DG: Well, thanks again for having me. Welcome to everyone listening. This is really a fantastic time to study statistics, and you're welcome to do your own research on this. If you do any kind of Google search on the best jobs, best occupations, best careers, you're extremely likely to run across statistics as one of the top choices. So, we have accumulated a few of the references that you might find. For instance, US News & World Report recently did sort of a comprehensive look at the best jobs to have based on things like employment outlook, salary, the stress level of the job, work-life balance, all those different things. They give seven factors that they looked at, and they actually listed statistician as the number one job for business, not something that I was expecting to see, but that's what they listed. 

05:54 DG: They also determined that a statistician is the second-best STEM job. It is the most popular job for millennials according to If you look at the Bureau of Labor Statistics, they publish a list of the 20 fastest-growing occupations. This is a projection over the next 10 years, and they listed statistician as the seventh fastest-growing job over that period with a 34% projected growth through 2026. If you look carefully at that list, some of the fastest-growing jobs are not necessarily really desirable jobs, like home health care aide or personal attendant. So, if you restrict that list from the Bureau of Labor Statistics to the jobs that pay at least $60,000 a year on the average, statistician is number three. So, it is really one of the well-paying and fast-growing jobs in the United States right now. 

06:51 DG: LinkedIn did a study based on what job skills employers are searching for when they use the LinkedIn database, and they determined that data analysis, which is basically what statistics is, is the second most important job skill in 2018 that employers were actually looking for. The Bureau of Labor Statistics lists the median salary for statisticians as $85,000. So, we could go on. This is not a comprehensive list. And as I say, you're welcome to do your own Google search and see what you learn, but there's no doubt that there's really a tremendous amount of opportunity in statistics. 

07:30 DG: So, I want to talk about our curriculum, and then I'll also talk about the people who will be delivering the curriculum, and I'll talk some about what our graduates have done with their degrees. So, this is what we're trying to accomplish with the curriculum in applied statistics. First of all, we want to give you a very strong foundation in the techniques of applied statistics, and as I'm going to talk about this in more detail, but I'm referring both to very traditional techniques that maybe statisticians have been using for the last 60 years, and also much more modern techniques that really raise the eyebrows of recruiters when they hear that our students know predictive modeling, or they know data mining. Those are more modern techniques that are very much in demand right now. We are going to place a pretty strong emphasis on statistical software because whenever you're doing real data analysis, the data sets are large, and you have to use software. 

08:26 DG: We're focusing on two popular packages. R is probably the most popular and fastest-growing open-source software package in statistics. SAS is a traditional and proprietary package that is still in a lot of demand. So, R being open source, you can download it and install it yourself. We will provide you with instructions on how to do that. SAS is proprietary, but Michigan Tech has a license that allows all of our students to also download it and install it on their own machine. So as part of the orientation course, you will get information about how to install and get started with the software. So, the techniques, the software, we do have a couple of courses that focus on the mathematical concepts that underlie statistics. This is probability and statistical inference, and I think probably everyone knows today... In fact, you all are probably working professionals, and right now you're looking to upgrade your skills, and I think everyone knows that you can't just get a college degree and rest on that information that you learned for the rest of your career; you really have to be continually learning. 

09:32 DG: So, one of the things that we wanted to put into this program is enough mathematical foundation that you can continue to learn in statistics throughout the course of your career. And then we want to make sure that you have the skills that are required for a working statistician. So, for example, how do you deal with messy data? We can't restrict ourselves in our courses just to textbook problems with artificial data sets; you need to know how to take a data set that might be collected in the real world, which might mean that it has missing data or even incorrect data entries and how do you clean it up and put it into a form that can then be analyzed. So, in particular, some of the projects that you do in your courses will involve real-world data, not just textbook data. 

10:21 DG: Communication is obviously extremely important. How do you report on the findings of a statistical analysis? And especially as a statistician, you're often explaining your results to non-specialists. So, not only do you have to be able to correctly report on your findings, but you have to know how to communicate those technical results to non-technical colleagues. So, these are the things that we're going to focus on in our curriculum, and I think these aspects make it a very strong degree and a very marketable degree. 

10:53 DG: Now here's the way the curriculum is structured. Everyone takes the same two core courses first, and these core courses are the only prerequisites, pardon me, for the rest of the coursework. So, the first course would be Probability and Statistical Inference I. This will give you a foundation in the mathematical notation and terminology that you're going to be using in the rest of the degree. It's often said that mathematics is the language of science; it's definitely the language of statistics. So, I would encourage you when you take this initial course, you really need to embrace that language. One of the big things you have to do, if you haven't done it previously in your education and career, is just to become fluent with using algebraic notation, formulas, variables, et cetera, and speaking the language of mathematics. 

11:50 DG: Technically, there's a survey course on statistical methods. This will provide... It's basically an introductory statistics course taught at perhaps a little bit of an advanced level. This does raise an interesting question about the prerequisites for the program. So actually, everything you need to know about statistics is in the program. You could take this... You could enroll in this program having just a background in calculus and linear algebra. We also recommend a course, an introductory course, in statistics because it is a little odd to enter a Master's program in a discipline that you've never studied before. But the truth is everything you need to know about statistics is taught in the program starting with these two courses.

12:32 DG: So, one lays the mathematical foundation, the other one gives you a sort of a survey introduction to basic statistical methods. MA 5701 also, pardon me, has a project requirement so you will get your first taste of what it's like to do a statistical analysis and write a report on your findings, and it also provides the initial introduction to the software. So, you will get a taste of using both SAS and R, and those will be used throughout the rest of the curriculum, so you will become very familiar with them and gain a lot of expertise in using those software packages. 

13:12 DG: Now, all the courses in the program are half-semester courses. So that means the core courses will be completed in one 14-week semester. So, you'll take MA 4700 in the first half semester, MA 5701 in the second half semester. And then our expectation is that you'll continue to take two half-semester courses one at a time through the rest of the program until you're finished. 

13:36 DG: We thought it would be advantageous for you to be focusing just on one subject at a time. So, we want you to be able to make timely progress through the curriculum, finishing in five semesters, hopefully, but still at any one given point in time, you'll just be focusing on one course. And we do have three intakes, which I think is a nice feature of our program. You can start in January, in May, or in late August to early September, depending on when our semester starts. It depends on when Labor Day falls in September. 

14:06 DG: Okay, so more details about the curriculum. As I said, one of the things that I'm really proud of in this curriculum is that it's a nice blend of very traditional statistical techniques that really every statistician has to know and modern up-to-date methods. So, regression analysis, generalized linear regression, no matter who it is, if you're doing data analysis, you're using regression. So, to have a very firm understanding of methods of regression and generalized linear regression is critical for a statistician. I actually jumped at the end of my list here on this slide. Another very traditional topic is design and analysis of experiments. You could even say that modern statistics basically started about 120 years ago when Ronald Fisher figured out how to design statistical experiments, this was in a context of agriculture, so that you would end up collecting data that will yield meaningful statistical results. Now I think a modern statistician should be able to function in either environment. If you have the opportunity to plan your statistical studies, you will get better data and more meaningful results, and this Design and Analysis of Experiments teaches you how to do that. 

15:20 DG: On the other hand, an awful lot of analysis, data analysis, now is done in the context of business where there's incomplete data. It's opportunistic data. It's not an experiment that you planned, and you have to know how to deal with that as well. So, you're going to get both points of view in this curriculum. Going back up, now the third bullet on my list. One of the really modern courses in our curriculum is predictive modeling, and I can tell you as a math department chair and as someone who deals with recruiters, recruiters are very excited to hear that our students are learning predictive modeling. This is usually what businesses want to do in particular. They want to be able to take data that they've collected and make a prediction about what's going to happen in the future, and there's a collection of statistical techniques for doing that grouped under the title of Predictive Modeling. And so, this is a key course in our curriculum. 

16:15 DG: Similarly, statistical data mining, data mining refers to taking a large data set and extracting meaningful information from it. Sometimes you'll hear the phrase "big data." So, data mining is about extracting useful information from big data. So, this is another very modern course in statistical technique. 

16:35 DG: Computational Statistics, well, this is a course that teaches the algorithms that are used in computing intensive statistical methods. So, you may have heard of the idea of a Monte Carlo simulation where you're simulating a stochastic or probabilistic event by doing lots of calculations on a computer. So, this course teaches Monte Carlo simulation. It teaches how to estimate functions and how to do resampling and methods for exploring the structure of data. So, in this course, you're definitely going to get a real workout in your knowledge of R or SAS, but that's true in almost every one of these courses. Predictive modeling, there's a project requirement. You'll definitely be analyzing data and using software in that course, similarly for statistical data mining. 

17:26 DG: Time series analysis and forecasting is a special look at data that is collected over time. So, if you had, for instance, if you're recording temperatures daily, monthly, whatever it might be, over a period of time, that's a time series, and there're special techniques for analyzing that, and there's also a forecasting element here based on a time series. How can you forecast what might happen in the future? 

17:53 DG: And then the last course is MA 4705, which is the second course in the sequence on the mathematical foundations. And again, I'll just say that our intent in including the probability and statistical inference is to lay a foundation for you so that you have enough understanding that five years from now, 10 years from now, when you need to learn some new statistics, you've got the background to do that. 

18:21 DG: Let me talk now about the faculty who are delivering the courses. So, the two core courses are taught by teaching specialists, and what I mean by that is these are individuals who focus on teaching and are not active researchers. Phil Kendall has a Master's in Statistics from Michigan State. He teaches the Probability and Statistical Inference sequence. He is an outstanding teacher, he is an award-winning teacher, a member of our Academy of Teaching Excellence, and he has a lot of experience in teaching both introductory and advanced statistics. So, I think you're going to get a very good, organized, clear and helpful introduction to the mathematical foundation of statistics from Phil. Ray Mohsen earned his PhD in Statistics, actually focusing on probability here from Michigan Tech. He teaches the statistical methods course. He is another experienced and popular on-campus instructor. 

19:12 DG: And then when we moved to the more advanced courses, these are taught by experts in statistics, biostatistics, and econometrics. These are both very good teachers and very active researchers. Qi Jong has his PhD in probability and statistics from Beijing University. If you don't know, Beijing University is like the Harvard of China, so that is the top university there. Qi focuses on biostatistics, statistical genetics, which means using statistical techniques to analyze genetic data with the hope of identifying the genetic cause of diseases. Also, in genomics. So, Qi is an author of more than 100 publications in biostatistics and statistical genetics. His work is funded by the National Institutes of Health. He's really both an outstanding researcher and an excellent teacher. 

20:03 DG: Yeonwoo Rho received her PhD in Statistics from the University of Illinois, she does time series analysis and econometrics. Her work is currently supported by the National Science Foundation. So, I think it's a nice blend of expertise, biostatistics is a huge area of application for statistics. But econometrics, economics, finance, is another big area for application. And so, you'll get the chance to be instructed by experts in both of these areas. 

20:36 DG: Quiying Sha has her PhD in statistics from Michigan Tech, she focuses on statistical genetics and applied statistics. She's the co-author of more than 60 publications. Her work is currently supported by the National Institutes of Health. And as department chair, I'm kind of in charge of putting together this program, and I can tell you that I've made a big effort to get not only people with expertise in statistics, but also those who are really very good at teaching. So, I believe you'll have a good experience in this program. 

21:07 DG: And finally, I'm going to end by talking about some of the possible career pathways for graduates. Now, the truth is statistics is an enormously broad discipline. You could work in any one of numerous industries, government, science, sports, you'd be surprised. So, there's really no limit to what you could do with your degree in statistics. But just to give you some specific examples, companies that have hired our graduates with the Master's degree include Mayo Clinic; Bio-Stat Solutions; which is a biostatistical consulting company; AA Networks, which provides IT services; Argus Information and Advisory Services, they provide analysis of risk exposure in the credit industry; Epic Systems is a healthcare software company. Some of the job titles that our graduates have started with include statistical programmer, business intelligence developer, and analyst. 

22:09 DG: So, in summary, I think, we've got a really strong program, it's broad, it'll lay a foundation for continued study for you. It has the information and the experience using software that you're going to need. It has the real world aspect that's going to be critical for you in your jobs. Now we've got a good faculty to teach the courses, and as I said before, I'm confident that you'll have a good experience. So, Ashley, I believe I'm turning it back over to you now. 

22:41 AZ: Yes, thank you so much, professor, for sharing all those details. And friendly reminder to all of our viewers today, that you can type any questions that have come to mind in the Q&A box at the bottom of your screen. Our Q&A session will begin shortly. But this next slide, I'm going to have Mary Katherine take us through the application process. 

23:03 MH: Okay. So, the first step applying to this program is a discussion with an enrollment advisor. We use that conversation to get to know our students and to confirm that you have all of the admissions requirements. So, as you can see on the side of the screen, you need to have a bachelor's degree to qualify for this program. Also, we're looking for students with a math background, including calculus, linear algebra, and intro to statistics. But we do not have a GRE or a GMAT requirement. So, after you've spoken with your enrollment advisor, the steps to apply: First is completing the application. You'll need to submit a resume; two letters of recommendations, that can be academic or professional; a personal statement, which is telling us more about your personal background; a statement of purpose, which explains why you want to pursue the program; and transcripts from all colleges and universities that you've attended. 

24:01 AZ: Okay, great. So, if you're interested in getting started, want to learn more, or talk through next steps, get in touch with Mary Katherine or Rene. Hopefully, they've already reached out to you, you have their contact information. If not, at the end of this presentation, I do you have a slide with that information for you. Alright, and with that, now it's time for our Q&A session. So, if you have any questions, please type those into the Q&A box at the bottom of your screen. We'll do our best to get through as many questions as we can today, and if there's anything that we can't answer, we'll be sure to follow up with you directly.

24:40 AZ: So, our first question is, "How is this program different from a data science program?" Professor, is that something you can speak to?

24:50 DG: Absolutely. So, data science could be described as kind of the intersection of statistics, computer science, and domain-specific knowledge. So, data science as a discipline is broader than statistics. I think it would be expected that in a data science program, you would take some courses in computer science, and then you would take courses in some specific domain, it could be medical or health care informatics, could be geographic information systems, it could be business intelligence, something like that. On the other hand, statistics goes into more technical depth. Not all data scientists need to have the kind of technical depth we provide in the Masters of Applied Statistics. On the other hand, in my experience, every data science team needs to have these skills. So, some of the Master's in Applied Statistics would be an ideal member of a data science team, although maybe not every data scientist would have that level of technical detail.

25:53 AZ: Okay, great. That's very helpful, thank you. The next question is, "What skills do I need to have coming into the program to be successful?" Professor, that would be probably another good one for you to answer.

26:09 DG: Yeah. So, statistics is one of the mathematical sciences, you definitely need to have some comfort or familiarity with working with formulas, variables, equations, et cetera. Specifically, most of the courses are posed in the language of calculus, so you do need to have some background in calculus. A number of the courses specifically use linear algebra or matrix algebra, so you should have had some exposure to linear algebra, matrix algebra. And then computer skills. You don't have to know R, you don't have to know SAS, because we're going to start from scratch with those software packages. However, it be very helpful if you have a comfort level in working with some kind of software package. It could just be Excel, it could be you used MATLAB or MiniTab or one of many other programs. So, if you're kind of comfortable sitting in front of a computer and issuing commands and interpreting the results, that's something that you're going to need.

27:14 DG: And if you've never taken an online course or program before, let me just say that you have to be self-disciplined. Time management skills are really critical. You have to be willing to work hard. And then you really need to be willing to seek help when you need it. Sometimes people might think, "Oh, if I'm really smart or really good," or whatever, "I should all figure this all out of my own." Well, a Master's program in statistics, that's a pretty technical subject matter that you're trying to master, and everybody needs help. We've definitely designed our program with the expectation that there's going to be a lot of interaction between instructors and students. So, I think a willingness to seek help when you need it will be critical.

28:00 AZ: Okay, great, thank you. Next question is, "Is this a newly created online degree? If not, how long and how many students are currently enrolled?" So, Mark, that would probably be another good one for you to answer.

28:17 DG: Yes, this is newly created, we're launching it in May with the summer term, so we're expecting maybe a dozen students the first time are out, and in general, we're expecting class sizes from 20 to 25 students.

28:35 AZ: Okay. But this is an on-campus program and it has been for some time, correct?

28:41 AZ: Yes, we've been graduating students with the Master's in Statistics for quite a number of years.

28:46 AZ: Okay, great. Alright. Next question is, "Are there any calculus materials that you would recommend for us to brush up on the material before we start the program?"

29:02 DG: So, there is an enormous variety of helpful resources on the Internet. Khan Academy is very popular and it's very comprehensive where calculus or linear algebra are concerned. So, I think that's probably the first place I would recommend that you go if you need help. And I'm kind of glad that you're thinking that way, because you will need to just make sure you remember those basic concepts from calculus, differentiation, integration et cetera, and similarly for linear algebra.

29:35 AZ: Okay. Great. Next question is, "Will formal lectures for each course be posted via YouTube or something similar?" This question might be more in regard to the online learning environment, so, perhaps, Mark, you can take us through that and how students can engage with the material.

29:57 DG: Yeah, so our learning management system is Canvas, so that's where you'll find the videos and other materials that you need. I'm not quite sure what you mean by formal lectures. What we're not doing is videotaping a professor who's teaching an online course and giving you videotapes of 50-minute lectures. We're not doing that. So, our instructors have created short videos. So, typical... What might be the material delivered in a 50-minute lecture will be delivered with three short videos with maybe some short quiz after each one, or some kind of learning activity after each one. And these are especially designed for the online environment. So, it is the same material that we teach in our online courses, but we're definitely packaging the information and the delivery for the online environment.

30:56 AZ: Okay, great. Thank you. Let's see here. This question is another one... A little bit specific to someone's background. This individual is a Director of Operations with a background in process improvement and Six Sigma. And he is asking, "How do I show that a degree of this nature would be a benefit to my organization?" So how would the applied statistics degree, perhaps, be relevant to someone with an operations background?

31:36 DG: Yeah, so, that is pretty specific, and you might want to correspond with me by email, and I think my email address will be provided later. And I don't know the exact training that goes into becoming an expert in Six Sigma, but I think if you look at the curriculum as I described in the list of courses, if your company is doing something where predictive modeling is key, where you have to do data mining, then I think it should be a pretty easy sell. I think for anything more specific, you might want to correspond with me directly.

32:09 AZ: Okay, great. Alright. Next question is, "Are there any on-campus events that online students will be invited to attend?" My understanding is that online students are welcome to attend any on-campus events, so if you do live within the region and you're able to travel to campus, you're more than welcome to attend events. My understanding is that a lot of our students are not from the area, so they may not have that ability. Mark, is there anything additional that you do want to add there?

32:44 AZ: Well, certainly, when you face the degree, you're invited and encouraged to come to commencement and receive your diploma.

32:53 AZ: Great. Okay. Next question is, "What is the current tuition cost per course?" Mary Katherine, can you speak to this one?

33:04 MH: Yes. So, the current cost per credit hour is $1,007, and then there's a $38 fee per credit hours, so you're looking at $1,045 per credit hour. Please note that tuition is subject to change. So, this is as of April 2019, so, tuition could change during the course of the program.

33:28 AZ: Okay, thank you. Next question is, "How many hours a week can I expect to devote to this program?" Mark, is this one that you can speak to?

33:41 DG: Yes. I think it depends a lot on your background and how fluent you are with the basic mathematics you'll be using. But I think a pretty fair estimate would be between 12 and 20 hours a week, depending on the course and depending on your own background and just depending on how quickly you work.

34:00 AZ: Okay, great, thank you. Next question is, "Will we have access to online resources such as the library and research material?" I believe the answer to this is yes, there are a number of online resources that students will have access to, but Mark, if you would like to expound upon that, feel free.

34:21 DG: Yeah. An increasing number of books are actually available in electronic form. And when I'm looking for a resource, the first thing I do is check to see if the library has it electronically so that I can view it on the screen, and frequently, it does. So that's a very nice future. We have an excellent document delivery service through the library. So, if you're, say, doing a project and you need a paper, an article from some journal or magazine, you can request it through inter-library loan/document delivery and you'll get a PDF of it usually in less than a day. So that is a service that I use frequently from the library.

35:04 AZ: Okay, great, thank you. Next question, "Is tutoring included on the online student services?" Mary Katherine, is that something you can speak to?

35:14 MH: There's no direct tutoring, there's not like a tutoring center, but it is the student success coach will assist you in finding support through, and obviously, your professors are your main source of support for your content, so you can go to them with your questions. Unless I'm missing anything. Mark, is there anything that they have access to in terms of tutoring?

35:38 DG: No, but I just want to emphasize that our instructors understand that these courses, in order to be high quality, have to involve a lot of instructor-student interaction, so, we definitely have an expectation that you'll be frequently asking questions, that your instructors will be responding promptly. There will be office hours if you want to get some life help. So, that tutoring, or the assistance is really available from the instructors themselves.

36:09 AZ: Okay, great, thank you. Next question is, "Do I need to know computer programming to be successful in this program?"

36:20 DG: No, there's really very little programming required. So, to me, programming is when you write a program, which would be a series of commands that you're then going to execute by pushing one button and kind of wait and see what happens. There is a lot of interactive use of the software, so you'll be sitting in front of the computer, issuing a command to read in a data set. And then once R has read in the data set, you'll be issuing a command to maybe compute some statistics, mean, standard deviation, whatever. And then maybe you'll be issuing a command to draw a QQ plot. And so, there's a lot of that interactive use of the software, but there is little or no programming that's required.

37:05 AZ: Okay, thank you. The next question is, "Is it possible to transfer credits from previous graduate coursework?"

37:15 MH: So, yes, you can transfer credit if the degree was... If it went towards just studying, but not an actual degree. So, if you've already completed a Master's degree, you can't double-count those credits. However, you've taken a course before that was similar or exactly the same as one that we've already taken that'll be discussion with the department of whether you retake that, an alternative courses given, those are all dealt with on a case-by-case basis.

37:43 AZ: Okay, Mark, anything to add there?

37:46 DG: No, I have the same understanding, yeah.

37:49 AZ: Okay, great. The next question is, "Which statistical package have students found easier to use, R or SAS?"

38:02 DG: My sense is that R is easier to use. And there was a time when I thought, "Well, we're just going to jettison SAS and we're just going to teach R, it seems to be the up-and-coming package and so forth." But the American Statistical Association did a study not that long ago and found that SAS is still in a lot of demand by companies that hire statisticians, so we felt it was important to keep the emphasis both on the more traditional and proprietary package SAS which seems to be very marketable and also on the newer and possibly faster-growing package R. But in terms of which is easier to use I believe that R has the simpler interface.

38:43 AZ: Okay, thank you. Next question is, "What is a typical week like? What types of assignments can I expect?"

38:54 DG: Yeah, so that's a good question. You can expect to watch a number of short videos, so you might think wherein a typical week, you'd have several 50-minute lectures. Now each of those 50 minute lectures, you're going to be broken up into, say, three short video, so you could easily have a dozen short videos to watch during the week. Frequently a quiz or some other learning activity after the video. And by quiz, I mean a short quiz, maybe just a couple of questions to make sure you got the main points. There will typically be a longer homework assignment due, and we're going to do homework assignments due every Monday evening so there'll be some uniformity and sort of the structure of the program. And then beyond that, I think it really depends on the course. Among the core courses, Probability in Statistical Inference will have two exams, a midterm and a final. Statistical Methods has only a final exam. Some of the courses will have no exams at all, but only project requirements, along with the other things that I mentioned, the quizzes and homework assignments. So, there'll be some variation, to be sure, but I think the basic structure of watching videos instead of attending class to watch a lecture and doing some interactive learning activities after each video, that'll be pretty standard across all the courses.

40:24 AZ: Alright, thank you so much. That is going to conclude our question and answer session for today, so we'll wrap up. Thank you so much, everyone, for joining us today. If you have any additional questions, please don't hesitate to reach out. An on-demand recording of this session will be emailed you to tomorrow, and you can also access the recording via the same link that was emailed to you today to log in. So, if you want to view this at any time, you can go ahead and do that. So, thank you again, and I have listed the contact information for us here, so please do get in touch either via phone, via email, and we can assist you. And if there are any specific questions that you have for Dr. Gockenbach, we can forward those along to him as well. So, thank you so much, doctor, for joining us, and I hope that this was helpful for everyone. Have a great rest of your day.

41:23 DG: Thank you, Ashley.