Learn more about the demand for statisticians in this webinar information session, led by Dr. Mark Gockenbach, Chair of the Mathematical Sciences department at Michigan Technological University.
00:03 Ashley Zeman: Hello, everyone, and welcome. Thank you for joining us for today's information session about the online Master of Science in Applied Statistics program at Michigan Technological University. We're happy to have you here today. Before we get started, I wanted to go over a few housekeeping items. You're in broadcast only mode, which means you can hear us, but we can't hear you. At any point throughout today's presentation, feel free to type your questions into the Q&A box at the bottom of your screen. We have reserved some time at the end of our presentation to answer your questions. So please, as anything comes to mind, just type it into that box and we'll get to your questions at the end of the presentation.
00:48 AZ: Here are our speakers for today's session: I'm Ashley Zeman and I'll be your moderator. I'm joined by Vinay Patel, an admissions counselor for the program, who I'm sure some of you have already spoken with. Vinay is here to help answer all your questions about the admissions process and also assist you in getting your application materials submitted to the selection committee. We're also joined by Dr. Mark Gockenbach, who is the professor and chair of the Department of Mathematical Sciences here 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 here at 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's also served as a volunteer lecturer for the International Mathematical Union, offering courses in Phnom Penh, Cambodia in 2013, 2015, and 2016.
02:03 AZ: Professor Gockenbach's research and 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 of Numerical Analysis. He's the author of five books on Partial Differential Equations, Finite Element Methods, Linear Algebra and Inverse Problems. Welcome, Dr. Gockenbach.
02:37 Dr. Mark Gockenbach: Thank you, I'm glad to be here.
02:41 AZ: Here's a quick look at our agenda for today: We'll start with an overview of Michigan Technological University, then talk about the topic "Why earn a Master's degree in statistics," then we'll transition into some information about our statistics program here at Michigan Tech, the admissions process, and finally leave time for any questions that you may have. And as I said earlier, feel free to type any questions in the Q&A box at the bottom of your screen throughout this presentation. Vinay, I'll turn it over to you now to tell us a little bit about Michigan Tech.
03:17 Vinay Patel: Sure. So for some of you guys who aren't familiar with Michigan Tech, we are a brick and mortar university. We're not just an online university that offers online programs, we happen to be a brick and mortar university that offers online programs. So we are a public research university that's been founded in 1885. We are located in the Upper Peninsula of Michigan in Houghton. We have more than 120 programs, 70 plus graduate programs as well. Rankings wise, which matters a lot for a lot of different technological schools and things like that, we do come in in a top 25 in the nation for a STEM University, according to Forbes. And then research-wise, and things like that, we are 12th in the US, where graduates earned the highest mid-career salaries. For a lot of our students here and a lot of times, when I talk to you guys, it's about return on investment, making sure that, "Are you getting the biggest bang for your buck?" And from bestcolleges.com, we were ranked number 18 in the US for return on investment as well.
04:24 AZ: Great, alright. Thank you, Vinay. Now, I'll turn it over to Professor Gockenbach, who will provide some information to help us answer this question: Why should we earn a Master's degree in statistics?
04:39 DG: Thank you, Ashley. There is considerable opportunity now in the field of statistics, lots of job opportunities. I'm personally trained as a mathematician and we mathematicians are trained to be skeptical. So you really shouldn't take my word for this, you can do your own Google search. If you were to type in something like best jobs or fastest growing jobs, you'll find a lot of hits for statistics, or data analysis, or data scientist. I like to go to the Bureau of Labor Statistics to get data. And they recently published their latest list of the 10 fastest growing... Actually 20 fastest growing jobs over the next 10-year period. I believe, statistician was ranked number eight out of those 10. And actually, I'll just skip forward here and look at the top 10. As I said, statistician is at number eight. If you look at some of the ones that are ahead of them, some of them are not particularly well-paying. For instance, home health aide or personal care aide.
05:43 DG: So I would say that statistician is really the fourth fastest, well-paying job in the United States, at least that's the prediction of the Bureau of Labor Statistics for the next 10 years. US News and World Report published a big study of the best jobs, best business jobs, best STEM jobs, best low-stress and high-paying jobs. And you can see that statistician is either number one or number two on all of those lists. Forbes says that statistics is the second best master's degree to earn and actually, number one is bio-statistics, which is very closely related. So, I think there's a lot of evidence out there, and this is just a sampling really of the studies that you can go to. There's a lot of evidence out there that this is a good time to be studying statistics.
06:34 DG: So what does statisticians do, where do they work? The answer really increasingly is everywhere. I am at Michigan Tech, which is traditionally an engineering-dominated university. And one of the things that I've been impressed by is over the last 10 years, companies that for literally decades that have been coming to Michigan Tech to recruit engineers have started coming to recruit statisticians and mathematicians. So companies like Ford, for instance. I was told that at Ford Motor Company, their data science group is the largest growing part of the company. And so they're hiring people in statistics and mathematics and computer science to analyze data and help improve their business performance. Caterpillar is another, it's a heavy equipment manufacturer. Again, they've been recruiting at Tech for a long time to hire engineers and now they're increasingly interested in hiring statisticians or mathematicians to analyze data.
07:32 DG: What sorts of things do statisticians do for these various companies? Well, there's a big interest in collecting marketing data and doing more targeted marketing. Companies feel like they can improve their business performance by being more specific in how they target advertising to individuals, that involves collecting and analyzing data. Companies are very interested in analyzing customer satisfaction data. Increasingly, companies are collecting performance data. Now, if you buy a new car, there's a good chance that that car has sensors and some kinda chip that can communicate and the car is actually sending performance information back to the company. And the people in the company who do data are gonna analyze that and figure out how they can best improve the performance of their engines.
08:29 DG: And then there are the traditional applications of statistics. Probably still the major employers of statisticians are medical clinics, and research hospitals, pharmaceutical companies, biotech firms; these are all interested in some kind of a treatment plan or drug or device and they need to know how the treatment performs. And so lots of applications of statistics in the medical profession. The government hires lots of statisticians. I mentioned before, the Bureau of Labor Statistics, there's the Department of Economic Analysis, there's the Census Bureau. So there's a lot of use of statistics in government agencies.
09:10 DG: So really... And I can tell you also, just as someone who hires faculty members, I actually have a great difficulty in hiring statisticians, because they have so many job opportunities. They can go to universities as professors, they can go to industry, they can go to research clinics and so forth. So it really is a great time to study statistics, there's a great demand for statisticians throughout the economy.
09:42 DG: So statisticians tend to work sort of as consultants. Usually inside of a company, someone will have a problem and they will go to a statistician or a data scientist, and say, "This is my problem, this is the kinda data I have, this is the kind of information I'm looking to get out of that data." So there really is a great consulting type role for a statistician. As a result, statisticians usually work in groups. I had heard recently... I frequently go to the Institute for Mathematics and its Applications at the University of Minnesota, which is a organization that is devoted to promoting the use of mathematics in industry and connecting industries with universities. And I just happened to hear an anecdote that The Milwaukee Brewers, the professional baseball team, had come to the IMA looking to hire a data scientist. They wanted someone to work specifically with marketing tickets. Ticket sales across Major League Baseball have been going down. The Brewers actually hired a PhD-level data scientist and were so happy, I guess, they came back a year or two later looking to hire a second one.
10:55 DG: And just out of curiosity, I went to The Milwaukee Brewers webpage and looked at their staff, and they listed a couple of data engineers, a data architect, a business analyst. I had a list somewhere and I've misplaced it. And a couple of others, about six people that identified as working directly with data for this professional baseball team. One or two of them had degrees in statistics, a couple had degrees in mathematics, at least one had a degree in computer science. And I think that's very typical. What do statisticians do? They work in teams with people with varying skills, including statistics, math, computer science. They act as consultants helping the larger company address various kinds of problems.
11:47 DG: We have a graphic here of sorta describing the work of a statistician; exploring data, testing hypothesis, making a prediction, drawing actionable conclusions. I might just mention that before all of that happens, statisticians ideally are planning the experiment or the data collection because it's a lot easier to analyze and draw meaningful information out of data that's been collected properly. So experimental design is an important part also of what statisticians do. I'm gonna talk more about the curriculum later, but I think you'll see that the curriculum for our online program really has a great blend of very traditional statistics, topics, things that have been used for the last 100 years, like regression analysis, design of experiments and so forth. And the really modern and up-to-date methods of predictive modeling that companies are extremely interested in, data mining. And then the use of computing, which is central to statistics nowadays.
12:51 DG: Another aspect of being a successful statistician is... Well, I said, a statistician normally works as a consultant. That means that normally a statistician would have to convey the results of the analysis to someone who's not an expert in statistics, someone who is maybe interested in marketing, or interested in engine performance, or interested in customer satisfaction, or interested in how well this medical treatment works. And so a key skill for a statistician is to be able to explain the results of an analysis to a non-expert. And one of the things that we've built into our program is a lot of experience in doing projects, analyzing data and critically writing reports, summarizing, explaining, hopefully without using a lot of jargon or technical language in a way that non-experts can understand. So that's something that a statistician... That's a skill that a statistician needs to have and that's a skill that we have tried to build into our program.
13:57 DG: So this slide is sorta circling back, we already mentioned the job outlook is very good. This slide says, "Almost 90% of America's industries use statistics daily." I can't speak to that number exactly, but I can just tell you as someone who works at a university, an increasingly broad segment of the economy or the different industries, are coming to our university looking for people who are able to analyze data. So this slide resonates with me with my personal experience. Annual salaries are good. So, statisticians on the average make almost $90,000 a year according to the Bureau of Labor Statistics. The growth is much greater than average. I think overall, the number of jobs in the economy is expected to grow maybe 6% or 7%, the number of jobs in statistics is expected to grow more than 30%.
15:00 DG: Here are some examples of different jobs that our graduates have taken. We have statisticians working at Mayo Clinic, at BioStat Solutions, which is a bio-statistical consulting firm, in IT at AA Networks, at Argus Information and Advisory Service, which services which does risk exposure in the credit industry. Not on the slide, but recently, one of our graduates went to Quicken Loans, increasing use of statistics in finance and in the credit industry. For Epic Systems, which produces healthcare software, our graduates have gotten jobs as Statistical Programmers, Business Intelligence Developers, Analysts, and so forth. So the job market is really very strong. And one of the things about statistics is that it is very broad. I mentioned before, the idea of working for a professional baseball team. That is probably not the first thing you would think of, if you think you're gonna get a degree in statistics but those kind of opportunities are there. And I think it's really true that you can work as a statistician analyzing data in almost any segment of the economy. And then also for government, and medical fields, and so forth.
16:18 DG: I think I'll turn it over to you now, Vinay, to talk about the overview of the program.
16:23 VP: Yeah, sure. Thank you, Dr Mark. So for our online program, I'll say, this program is very uniquely designed for working professionals. A lot of times when I'm talking to students, they always ask me, "How is this gonna impact me day-to-day, week-to-week in my life? I'm busy with family obligations, I'm busy with work, I usually work a little bit more than 40 hours a week, I'm usually somewhere between 40-50 hours a week type of stuff. How's this gonna impact me in that type of avenue and things like that?" The nice part of this program is the way that it is designed though. So we take our 15-week traditional semester, and we break it into two sessions. So there'll be a session A and B, if you think about it, where you're taking classes for seven-week increments. So you do get to take two courses per semester, but you're really only focusing on one course at a time.
17:14 VP: So for our students that have obligations... For example, I talked to a student earlier this week who's an umpire for his kid's baseball games, and he really didn't wanna give that up. And we talked about it, where we're like, "You're not gonna be learning two brand new topics, two new concepts in classes. You really do get to focus just on one course at a time, but it's also gonna be accelerated because you are taking two courses per semester." Additionally, we do go year round in the program. It makes no sense to say, "We're gonna be accelerating the program," and then only offer you two semesters in a year. Offering three semesters in a year really does speed up the program, so you get to take six classes in a year and then take up the other four classes. So the program only does take about 20 months to complete in that standpoint. So it's nice because you get to still have your family obligations that are going on, your work obligations.
18:08 VP: I talked to a student that's in our program, currently. And he was telling me that he's already drawing some of the concepts he learned in his first two classes directly into the job. And his work is super impressed with him, likes what he's learning and what he's getting, the outcomes out of it. And he said, "Yeah. If I was doing this, really, at a horrible pace where I was trying to learn way too much and too quickly," he's like, "I wouldn't be able to do that." So he really loves the seven-week format. Once again, this program is 30 credit hours. It's no thesis, there's no capstone. It's a course-only program, so you really do get to focus just on those 10 courses and to graduation. And it is 100% online, so there's no reason for you have to worry about coming up to Houghton if you don't live near by.
18:49 VP: If you do, you're more than welcome to always come to campus. There's always guest lecturers, there's always PhD students presenting their findings, there's always something going on at Tech. So if you wanted to attend the campus, you're more than welcome to do so. For most of our students though, the biggest draw for them is to come to campus is for graduation. And that's really the only time that you would want to come to campus, if you wanted to. But everything else from a program outline standpoint, the course work, everything else will be handled online. Only other time would be exams to make sure that you're taking them and we do require sometimes to have to be proctored, but we can talk about that more one-on-one and what that means. For the most standpoint, it's asynchronous. There's no set log in times. Our faculty members do a great job of creating short lecture videos for you, so you're not trying to digest too much information at once. So the one thing I will say Tech does very well is we do not just record lectures on campus, we don't make you watch a three-hour video, we do try to break 'em up into smaller videos so you can get a really good grasp on the concepts and ideas stuff.
19:53 AZ: Great. Alright, thank you. Now, Dr Gockenbach, you can tell us a little bit more about the curriculum and also the faculty in the program.
20:04 DG: Okay, good. Well, this really is a strong and rigorous program and we might talk a little bit later about how much time investment is expected on your part. But I will tell you, I don't wanna hide the fact that it's challenging, it is rigorous, there is a little bit of theory involved, because you have to understand the basis of statistics and how mathematics is used in statistics. It's mostly though very applied in learning different techniques for using statistics. One of the things I'm really proud of is that the program is a nice blend of very traditional topics and very, very modern and cutting-edge topics, and I'll talk about those as we go through the individual courses.
20:46 DG: There is a big emphasis on statistical software, so certainly everyone has heard the term big data, and most statistical analysis now is done in the context of large data sets. So the use of software is critical. We've chosen two widely-used software packages. One is open source and freely available, that's R. And the other is probably the most popular commercial package, SAS. These are introduced in one of the courses I'll describe in a moment, and then used throughout the program for projects and homework assignments. So I think one of the things you'll come out with from this program is a very good facility for using software to analyze data.
21:35 DG: Some of the other skills that we've tried to incorporate in the program... In the real world, data tends to be messy. So not just like what you get in a textbook, where the data is perfect and it's all there, and it's all what you expect. In the real world, data values are sometimes missing, you have to know how to cope with that. Data might contain outliers, you have to know how to cope with those. We try to have students work with real data, real-world data so that they're not caught by surprise on the job when they come up with a messy data set. I mentioned this before, but being able to report on the findings of your analysis is critical. So once again, you're gonna be often reporting to non-experts, and you have to explain what the meaning of your findings is.
22:26 DG: Okay, and so I think we'll go and look at the actual curriculum. The way we've designed the course is that there are two core courses that have to be taken first. One is a little bit of theory, probability in Statistical Inference one, so that you understand how mathematical probability theory is used in statistics and you understand the idea of making a statistical inference, in other words, testing a hypothesis. Secondly, there's a survey course on statistical method. So, this is really introductory, it's a little bit fast-paced. So you might have noticed that one of the prerequisites for this program is to have an introductory course in statistics. In theory, you can get along without having had an introductory course because all the basics that you need are in these two courses. I think they would probably move a little quickly for you if you had never been exposed to statistics before and that's why we do require an introductory statistics course as a pre-requisite.
23:26 DG: I think Vinay already described the fact that these are all what we call half-semester courses, they run for seven weeks each. So in your first semester, you would take Probability and Statistical Inference 1 in the first-half semester, and Statistical Methods in the second-half semester. Once you have those two courses under your belt, you are able to take any of the other eight courses in the program pretty much in any order.
23:53 DG: Okay, so I'll just run through and briefly describe some of the other courses you have to take. Very traditional courses would be the first two on the list, Regression Analysis and Generalized Linear Regression. So one of the most basic problems in statistics is to have a statistical model that you believe describes the data that you're talking about, and those models always include unknown parameters. And the idea of regression is to use the data that you actually collect to complete the model, to fill in the numerical values of those parameters. Then you have a complete model and it could be used to make predictions and so forth.
24:36 DG: In terms of traditional topics, I might also mention the course that's on the last of this... The last course on the list here, Design and Analysis of Experiments. This actually goes way back more than 100 years ago to the work of Ronald Fisher who was sort of the grandfather of much modern statistics. He was analyzing agricultural data and the question is you... Testing a different kind of fertilizer or something like that. And how do you decide if a fertilizer is causing improvement or perhaps, which fertilizer is better than another? And he came up with a mathematical way of designing experiments so that the data would be meaningful. And I mentioned on an earlier slide that statisticians ideally are involved in not just analyzing the data, but actually in designing the collection of it because the experiment needs to be properly designed in order for the data to be as meaningful as possible.
25:35 DG: I'll jump to the middle of the list, Computational Statistics is a more advanced course on using software and computation to do the analysis of large data sets. So that's actually woven into eight of these courses. Two of them are theoretical, the Probability and Statistical Inference 1 and 2, but the other eight courses are all applied statistics courses. They all involve working with real data, using computer software in order to analyze that data. But then we have this one course, Computational Statistics, where you learn advanced methods for computing in statistics. And then Predictive Modeling is a very popular course. I could tell you from interacting with employers that they tend to be excited when they hear that our students have had training in predictive modeling. Many companies are interested in making predictions based on data. And then Statistical Data Mining is all about advanced techniques for gaining information out of large data sets. And then finally, Time Series Analysis and Forecasting is about analyzing data that comes in the form of a time series. In other words, data collected over time. So as I mentioned before, I'm proud of this program. It's broad, it is very rigorous. You will find it challenging, I'm sure, but I think you'll also find yourself very well-equipped to work as a statistician after you complete this program.
27:11 DG: The faculty, we have a nice blend, again, of teaching specialists and active researchers. The two core courses are taught by two of our teaching specialists. So Phil Kendall has his master's in statistics from Michigan State University. He teaches Probability and Statistical Inference 1 and 2. He is an award-winning teacher here at Michigan Tech, he's a member of our Academy of Teaching Excellence. And yeah, I can tell you that he's been a very popular instructor on campus, and also online. Ray Molzon earned his PhD in statistics here at Michigan Tech. He teaches the statistical methods course, he'll also be developing the regression analysis course to be offered next year. He's very experienced, he's extremely bright. He wrote a very... Really a very intellectually challenging dissertation on probability theory. And already, we're getting good comments. The program has not been going very long online, but we're already getting good comments about how helpful he is and how much the students like working with him. But I think you're gonna get off to a very good start in the program with these courses taught by the teaching specialists.
28:22 DG: And then the other faculty in the program are active researchers in statistics, biostatistics and econometrics. So Kui Zhang has his PhD in probability and statistics from Beijing University. That is the top university in China, sort of the Harvard of China. He's done a lot of work in biostatistics, especially in statistical genetics and genomics. So the idea of statistical genetics is to use statistical techniques to locate the causes of complex diseases in genetic data. Professor Zhang has written more than 100 publications, his research has been funded by the National Institutes of Health, and he is just an extremely accomplished faculty member.
29:09 DG: Yeonwoo Rho has her PhD in statistics from the University of Illinois. She works in time-series analysis, and especially in econometrics. Her work is currently supported by the National Science Foundation, and she is another enthusiastic and accomplished statistician. And then Ying Sha has her PhD in statistics from Michigan Tech. She works in statistical genetics, also in applied statistics. She's written more than 60 publications. Her work is supported by the National Institutes of Health. I should mention that Professor Zhang is the house professor of statistics and data science, and Professor Sha is the Portage Health Foundation endowed professor in community health. So these are very accomplished faculty members that are developing and teaching their courses. So Vinay, I believe I turn it over to you?
30:06 VP: Yes, sir. Alright. Hopefully you guys got a really good understanding of why statistics is important, as well as who our faculty members are and what they teach. I think that is the biggest highlight that I get on a lot of the calls with our students, because sometimes people have reservations when it's an online program, and who's gonna be teaching them and where they're coming from? And those type of questions, so it's great to hear about the faculty members' bios and things like that. And I know there's a lot of stuff online as well. So if you guys want to do further research, as Dr. Mark said, don't take our word for it. Go on our website, look at the bios and things like that. You guys will get a great grasp of ideas of what their research interests are, and what they are doing, and so forth on as well.
30:49 VP: From an online experience, though, making sure that our on-campus students and our online students have the same quality of education and that same feeling, they don't feel a disconnect. So you do have your virtual learning environment, which is gonna be Canvas. Canvas is a great facility to make sure you can interact with your faculty members and students. We also use Zoom as well. Zoom is great because you can do video calls on there, you can schedule meetings with your classmates that way. It's kind of like a virtual environment for you to kinda sorta just be able to talk to your students as well as your classmates or your faculty member directly about any questions you may have. The nice part of Zoom is also you can share your screen with them, they can share their screen. So if you're really having trouble with a software or something like that, or you can't get something to work correctly, it's nice because they can see what you're seeing and see what your input is and what's going on and why you're not getting the exact correct answer or something like that. So it does help out with those type of things.
31:44 VP: The nice part, like we mentioned earlier though, with this degree, if you would love to, you get to complete it completely at your own pace, at your own time period. So there's no set login times. You don't have to worry about watching a live lecture at 3:00 PM if you're at work or something like that, there's not gonna be set login times. Talked about this a little earlier as well with the content and skills in the course to the area of business. The biggest draw for this program, I will say, for most of our students is going to be the way that Techs designs this program. We designed this program with employers in mind and asking them from a technical and professional standpoint, "What type of skill sets would you require for somebody who was to become a statistician?" So we make sure that we listen to the businesses and those companies that are looking to hire you guys in the future and what you guys wanna get out of it, and that's where receiving that one-on-one attention is really helpful.
32:37 VP: You also will have a Student Success Coach that'll be assigned to you about six weeks before the start date. She is there to help with orientation, making sure you have any questions about what courses you wanna take next, what courses you need to be taking, as well as making sure that you're actually getting the textbooks ordered correctly. But the nice part of her role is also is that she's registered in the class that you're in. So for whatever reason, if it's week four and a video is not working, she can actually walk you through it because she's there in that class with you. It's also nice because she can actually reach out to the faculty member directly for you, on your behalf, if there's a bigger issue going on that's affecting multiple students, so you guys don't have to all email them all at once. So she can take care of those little things.
33:22 VP: It's actually... Like we talked about, the platform is being designed in Canvas, it's specifically designed to make sure that you guys have a good user experience and things like that. And then software is included for you guys. So like we mentioned, R is open-source, SAS is proprietary, but we do have a software distribution center that's available for our students. That opens up about three weeks before the start date, so you will be able to go in there and download the software that you need for your classes in that standpoint. And that's already included in your tuition and fees as well.
33:56 AZ: Alright, now take us through the admissions requirements.
34:00 VP: Sure. So admissions for next steps-wise, biggest thing is going to be having a strong math background. We talked about this before with the theoretical side of you have to know math to do well in this program. So we do require a minimum of at least two to three semesters of calculus, as well as one semester of linear algebra, as well as a introduced stats or probability type course. So there's at least five courses there that we do wanna make sure that you've completed before joining our program. The nice part is that Michigan Tech does offer a lot these classes online at the undergraduate level. So if you're missing a class or something like that, as a one-off, be sure to reach out to me afterwards. I can for sure give better... Give you more clarification on how to sign up for those classes.
34:45 VP: What's the advantage of you taking those classes with us here and so forth on? Great thing is if you already have those classes done and so forth on, have your bachelor's degree, it really doesn't matter what your bachelor's degree is in. We've had students come from a wide range of different disciplines. Just a couple semesters ago, I had a student who was a poly-sci major, who works in general elections. So doing statistics, we had election results, and making sure how to market different campaigns around different DC filibusters and things like that. And he already had the math background and just a poly-sci degree, and was able to go into our program.
35:22 VP: Nice part is also, there's no GRE or GMAT requirement in this program. So no entrance exams, nothing that Michigan Tech offers in-house either. So you don't have to take an exam through us even or anything like that, it's a very straightforward process. It starts off with, first and foremost with an interview. Talk to me, I'm gonna be your first and foremost resource to making sure that A] You've taken the right classes to qualify for our program. Answering any questions that you may have in regards to the program itself directly, to making sure it's a good fit for what you're looking for.
35:52 VP: Once we determine that, I will send you an email with the application link and checklist of documents. The application is very straightforward, takes about 10 to 15 minutes to get it knocked out. It's mostly just demographic information. There's no application fee either, so it's very straightforward in that regard. And then, once you complete your application, in about 24 to 48 hours, you get access to what's called MyMichiganTech. For some of you guys that done this already, MyMichiganTech is very straightforward. It's where you upload all your documents. So there'll be instructions there for you to upload your resume.
36:25 VP: Most of the time actually, the committee will move forward with one letter of recommendation. They do always reserve the right for a second letter of recommendation though. So, we do always put it on there as a two letters of recommendations, just to make sure that you guys have two people down just in case the committee does require that second one. Then we need transcripts from basically your final college or university that you attended. And the nice part is that we don't need official copies of transcripts to make a decision. You can actually just upload an unofficial copy, if you have one laying around at home already. For those of you who went to Tech, we can just move it over. So it's really nice in that standpoint as well. But transcripts should never be a hindrance. You don't have to call your school or anything like that to get those transcripts if you have 'em unofficial. Even if you have 'em through our portal and you have to copy and paste them through Word, I can work with you on how to get them uploaded correctly. So it's very straightforward on the transcripts to making sure that you take the right classes, what your GPA was, and all those type of things.
37:23 VP: And then lastly, there are gonna be two essays that you write. One is a personal statement, which is basically telling us about who you are. And then there's a statement of purpose, which tells us about... Basically about why you wanna do this degree. There are character limits to those. So we will talk for sure about that in our interview process though, to make sure that you don't go over it. I'm sure you will not go under because it's very hard to go under, but it's a very straightforward... And like I said, it all starts off by us having an interview, talking about the program and making sure that you have a really good grasp on what you're gonna be signing up for.
37:57 AZ: So, Vinay, tell us a little bit about what the next term is that students can enroll in and the deadlines associated given this time of year.
38:07 VP: Sure. So like we talked about, there's multiple start dates in a year. The next one that's coming up is on January 13th. We do have a priority deadline coming up for that on December 15th. I highly advise students though to take advantage of everything that's possibly available to you from access to the software, to your student success coach, all that type of stuff to get your application done as soon as possible. We are gonna be running into holidays soon. So for those students who don't have access to their transcripts and still need to get them, keep in mind, there's Thanksgiving coming up, there's Christmas, there's New Years. Unfortunately, campuses do close down very quickly, pretty much after finals are done. So if you wait, there's gonna be the likelihood of you being able to start when you wanted to, it does become harder. So as soon as possible, the sooner the better in that standpoint.
39:00 VP: Turnaround time period for my admissions decision doesn't take too long, it takes only about a week. But we wanna make sure that we get the strongest application together, as well as the strongest package, so the committee really has a good idea of who you are and why you wanna do this and so forth on. So December 15th is the deadline, but I would say the sooner the better. I would say even as soon as probably mid-November to late November will be the absolute latest, probably.
39:26 AZ: Okay. So January is the next start date. After that, there is May, and then again in the fall. But the next one being January. So, great. Alright, so now we're going to transition into our Q&A session. So if you have any questions, you can type those in the Q&A box at the bottom of your screen. Anything related to the program, the admissions process, or any of the information that Dr. Gockenbach shared earlier through the topical discussion. We've already gotten a few questions here, so I'll go ahead and read those. First off, the question is: "Is there a recommended path to take the required 10 classes? I know Dr. Gockenbach went over that with the two core courses happening first and then the remaining eight. Those remaining eight, is there any preferred order to take those?" I'll open this up to either of you, Vinay or Mark. That you'd like to answer.
40:32 VP: Sure, I can answer...
40:33 DG: Yeah I'll take that question. Allow me, Vinay.
40:36 VP: Oh, okay.
40:37 DG: We've really designed the program to be flexible, so that we can have these three start dates a year. And as a result, everyone just has to take the core courses first, and then the other courses can be taken in any order. The program is designed with that in mind.
40:55 VP: Yeah, and the only thing I was gonna add is that your student success coach, as well as your academic advisor that'll be assigned to you will work really closely with you to making sure that you're taking the classes that you need to with the expectation of graduation and things like that. So depending on what courses are gonna be running and when they think... They'll make a plan with you on exactly what a course outline should look like for you to actually finish up the program in 20 months.
41:19 AZ: Great, and is there an opportunity to take electives as part of this program? Mark, do you wanna answer that one?
41:28 DG: Yeah. At this point, there is not. We're really investing a lot to create these 10 high quality, online courses. So once we're through and the program is underway and perhaps in a couple of years, we may be developing electives and different tracks within the program. But currently, that does not exist.
41:50 AZ: Okay, great. Next question is: "What is best for an incoming student to focus on to be best prepared to start the program?" Mark, I'll let you answer this one as well.
42:05 DG: So I think if it's been a while since you've taken any math courses, we do use the language of calculus constantly. Some of the courses rely on linear algebra, others not so much. And of course, just knowing the language of introductory statistics and statistical inference is critical. There are lots of great resources out there, Khan Academy and other internet resources. So, if it's been a while since you've had calculus, I would suggest you just try to brush up on some of those things. Also, once you decide to join the program, there is a period before classes start when you can download the software and make sure that it's working and I'd encourage you to do that before classes start and just get a basic working knowledge of SAS and R. From what I've heard talking to the instructors, the courses are going very smoothly and students have been quite successful. However, sometimes that first week is really a lot of work as students are just learning how to use the software, starting from scratch. So if you can do a little bit of that work before the program starts, I think it's really to your advantage.
43:18 AZ: Great. Next question is: "Can a Master's level degree help us with jobs beyond the entry level?" Mark, maybe you can speak to that. What do we see graduates doing with a degree in terms of level in their career?
43:40 DG: Well, I think that definitely within the data science field, the graduate degree is very useful. And data science companies do hire a lot of entry-level people with bachelor's degrees, but they're also looking for people with the deeper and more technical knowledge. And that's what the Master degree can get you. So I think that the answer is yes, definitely in the data science realm. For statisticians, for instance, the more traditional jobs, like at medical clinics, and so forth, the master's degree is the expected entry level degree for a statistician, at least a traditional statistician. So in that sense, this is what you need in order to get into the career.
44:25 AZ: Okay, great. Next question is: "Is there a preference for who the letters of recommendation should come from?" Vinay, I'll let you answer this one.
44:34 VP: Sure. So letters of recommendation, it really doesn't matter. It can be professional or academic. Usually the gold standard for Michigan Tech is going to be... If you've been out of school for not so long, and you have a... Somebody who has a PhD or a Master's, saying that you can do graduate level work, that is the ideal situation. But if you've been out of school for several years and don't have a faculty member in mind that can write you a letter of recommendation, it can be a supervisor, it can be somebody you work with and so forth on. Just not personal, of course. But professional or academic, it really doesn't matter.
45:11 AZ: Mark, anything you'd like to add there?
45:16 DG: No, I think Vinay gave good advice. Yeah, if you're newly graduated and there are professors who know you well, especially in quantitative courses, those would be ideal. But otherwise, if you've been in the working world, then a supervisor or a colleague who can speak to your abilities would be good.
45:34 AZ: Okay, great. Next question is about our online program, how is it different than other online Masters of Applied Statistics program? I know there's a lot of things we covered today in regards to this, but Mark, maybe you can just help summarize what some of the unique factors are that kind of sets our program apart.
45:58 DG: We definitely put a lot of emphasis on using good pedagogical techniques. In other words, these are not on campus courses that have just been sort of ported to the internet. They're designed with the online learner in mind from the beginning. So, I'm really proud of the quality of the courses and I think students will have a good experience in them. And I think the quality of the faculty is really pretty impressive. We didn't just go out and hire some part-time adjuncts to teach these courses so we can make money off of them. We have our top statistics researchers and teachers, these are very popular instructors here at Michigan Tech, but also very accomplished researchers. So, you're gonna learn real statistics from real experts, and I think that probably sets our program apart from some.
46:52 VP: And I'll say one thing is the format of the classes. The seven-week class sessions where you only get to take one class in the session, instead of two at the same time is very unique and something that a lot of students really do enjoy of having that work-life balance. And then, being able to take classes in the summer even. The 20-month pace is really, is accelerated to keep you in mind, to get you in and out of the program. We don't wanna keep you as a student forever here. We want you to graduate, we want you to do great things with the degree and come back and tell us of all the amazing things that you've done and what you wanted to get out of it, so.
47:28 AZ: Alright. Next question is: "Any advice to prospective students who've obtained their bachelor's degree almost 10 years ago and have the math background, but haven't used that knowledge since graduation?" Mark, I'll let you speak to this one.
47:44 DG: Yeah. I did touch on this earlier, but I would suggest taking advantage of some of the internet resources and just brushing up on your knowledge of calculus and introductory statistics, and linear algebra can be a little bit more on an as-needed basis. You're not gonna use a lot of linear algebra in the first semester. You will use it a lot in some of the courses that come later. But for brushing up on your knowledge, I would say you really need to know the language of calculus, you really need to know the basic terminology and concepts of introductory statistics. And of course, it depends on how you like best learning. Some might like to pick up a textbook, but I think a lot of people would like to take advantage of Khan Academy or other internet resources and review in that way.
48:31 AZ: Okay, great. Next question is about the software packages that we chose with SAS and R. This individual is asking about Python and how Python is used in the program?
48:46 DG: Yeah, there's no question that Python is very popular. It's especially popular in the data science world. So, this is a degree in statistics, not data science. I feel pretty confident, based on our research, that R and SAS are the most widely used statistical software packages. One of the things that I learned is that, long ago, as an undergraduate in a program in Math and Computer Science is once I learned a couple of languages, I could pick up the next one pretty easily. So, one of the things you'll get out of this program is you do have to learn two languages; R and SAS. If your particular context requires you to work in Python, I believe you wouldn't find it hard to move from those to Python. You'd have the statistical algorithmic knowledge and you'd have to learn some new syntax and coding techniques, but I think you would be well-positioned to do that.
49:42 AZ: Alright, great. Next question is about the online learning environment. This individual is used to using Blackboard, they're just wondering how Blackboard and Canvas compare. Any ideas?
49:58 VP: Sure. I think Canvas is actually more user-friendly. So, I have actually used both personally, so I can speak from my personal experience. Blackboard was very much click-heavy, so there was a lot of clicking to get to where you needed to get to. Canvas is a little bit smoother and cleaned out, though, so it's a little bit easier to use. And that's where we talk about getting access to Orientation earlier, getting access to Early Access, 'cause that's really what's gonna get you prepared, because Orientation is actually housed in Canvas. That opens up six weeks before classes start. So it's really nice to get access into there, in the beginning of December, pretty much, so you can pretty much be ready with the coursework and everything else that needs to be running in there.
50:43 AZ: Great, great. Alright, so I think this will be our last question. But after that, feel free to reach out and contact us with any specific questions. There is a link to schedule an appointment with Vinay, and you can also, in the survey, put any remaining questions or needs for us and we can follow up with you on that as well. So the final question is in regards to our students in the program. "Do most of them already use statistics in their career, or will this program help prepare a student to start a career in stats or data?" I believe it's a mixture of both, but I'll let you two speak to that, whoever would like to speak to that.
51:31 VP: Sure. I can go first. So, students, population-wise, I would say it's a mixture, for sure, because we have students that are teachers that have... That are math teachers, have a very strong background in mathematics, but don't have a stats background, but wanna move into more of a traditional statistics role. So they don't have as much experience with the data side or the statistical side, but they're looking for a career change, for example. So, that happens as well. But then, there's also the mix of students that are coming in as business analysts or are statisticians already that are just looking for career advancement, or just more of that advanced statistical methods and just a knowledge side of it. So, I think it's a good mixture though. So even if you don't have a strong career yet in statistics... And I will say, for sure, students are doing well in this program, I follow up with my students that I've enrolled in the past, and they've always told me that they do enjoy it, they do like it in that standpoint of what they're getting out of it, so...
52:27 AZ: Alright, great. Well, that will conclude our question and answer session for today. I have put our contact information here. Feel free to reach out to us via phone, email or the website, and you can also schedule an appointment with Vinay at any time. Like he said, the first step to start the admissions process is to schedule an admissions interview. So, if you're interested after today's session, please get in touch with Vinay, and he will be able to assist you. Thank you so much for joining us today and for participating, we really appreciate it. We hope this was helpful, and we do appreciate you taking the survey at the end of the webinar, so that we can evaluate the success of the webinar. Thank you again and have a great day.