Data Analysis and Decision Making Course
This course will familiarize students with the assumptions underlying various statistical techniques and assist in identifying their appropriateness in a variety of situations. Students should be able to perform statistical analysis and interpret results in a meaningful way. Students are expected to relate results of such analyses to become information-based decision makers.
Welcome to data analysis. This is the most-loved course: MBC 638! It’s otherwise known as statistics. You know there’s learning objectives. You can read about those in the syllabus. But there’s two things we’re not going to do in this class. We aren’t going to learn stats in a vacuum. And we’re not going to focus on theory. So we’re going to use this thing called DMAIC as our framework. We’re going to apply stats within that framework. And the techniques that we learn and cover will be methods that convert data into information, so that you as a business person can make better, informed decisions.
We will spend time in five phases—defining, measuring, analyzing, improving, and controlling. So in the “define” phase, we’re going to define your problem, your process, your data. That’s descriptive statistics. We’re going to measure. We’re going to measure your process. We’re going to measure and evaluate your measurement system. We’re going to look to see, do you have measurement error? We’re going to analyze. We’re going to use some inferential statistic tools in that “analyze” phase. We’re going to move to improve. We’re going to fix your problem. We’re going to discover y equals f of x. And we’re going to use stats for kind of the direction in which we want to go. And we don’t want to plan on eliminating any experiments. We’re just going to give ourselves a direction with statistics. And then of course, the last phase is controlling. We’re going to use tools to help us focus on really what’s important.
So now, before we get started, I have three questions I’d like to ask you. I’m a data geek, so I love to collect data, and I’ve been collecting this forever—answers to these questions. First one, what do you do? What’s your job, career, or journey? Give me your 90-second elevator pitch about yourself. Second question, I want you to answer either yes or no, have you taken a statistics course in less than or equal to 10 years? And the third question I want you to answer for me is tell me something unique about yourself—maybe your hobby or, like, you have 10 cats at home, you brew beer, you were married on top of Mount Everest, whatever it is. And the reason I’m asking those three questions is I want to know in our class, do we have a bunch of fine finance folks? Do we have people in manufacturing? What about marketing? Do we have anybody that’s in PR? I want to get a sense of that to understand how different examples may work with that group. I also would like to know your comfort level with statistics and math in general. So that’s my reason for question two. And question three, I want to know something unique about you, because that may—after you listen to everyone else’s pitch—you might find out that you may have things in common with other people in the class. But it also might give you some ideas of what you could do a project on, because you may or may not—everybody in this class is going to have to pick a problem, a project, and it may be appropriate to use something that you do at home or you do for fun.
So now, I’ll answer those questions for you. My name is Leanne Martin. I’ve been in this industry for 20 years, 20-plus years, 15 of those at Xerox. I have a undergraduate degree in industrial engineering. And my master’s is in industrial statistics. So I know a little bit about everything and a lot about nothing. I have manufacturing experience, but I have also been in marketing as well as distribution. So I have all kinds of good examples. I’ve been in corporate customer communications, PR. So I’ve got some good examples that we can use in this class that relate to those areas that you don’t typically think would be data driven or data rich. I have experience online teaching, did it for a while for another organization before coming to Syracuse, and I’ve been in Syracuse now, teaching for almost 10 years. And that’s really what I love to do, which leads me to my unique thing, is that I coach figure skating—love it! It’s my passion. And I have some elite skaters; I’ve got some that actually are national dancers that have gone to Colorado Springs. And I think that’s what I can bring to this course, is I can take a look at the art and the science, get those two to blend, and not necessarily collide. And that’s what you’ll notice with this course. We’ll try to take the art of statistics, the science of statistics, and make it all make sense.
By the end of the course, students will be able to:
- Understand the value of data collection and analysis in acquiring knowledge and making decisions in today’s business environment.
- Identify and apply the appropriate statistical technique for a given set of conditions in order to answer a particular question.