Welcome to the fundamentals of quantitative modeling. I'm going to start off by talking about some of the goals of this course. One of the things that you're going to get out of the course is exposure to the language of modeling. There are terms that people who fit models that tend to throw around a lot. And you want to understand those terms you want to have seen those terms used. By doing that you're going to just be much more comfortable as a participant in a group of people who are going through a quantitative modeling activity. I'm going to show you a variety of models that are used in a business setting and how those models are applied in practice. So you're going to get exposure to the sorts of models that people use. Of course, modeling is a huge, huge topic. There are so many models out there, I can't show you everything but I'll show you some of the important ones. We're going to discuss the process of modeling. Modeling tends not to be a linear process in the sense that you start You need something in the middle, and then you end. Modeling tends to be a much more iterative process. As you build a model, you look at its performance. You're perhaps not happy with the performance, so you need to go back and revisit some of the assumptions of the model, the formulation, etc. And so, we're going to go through the modeling process as well. As you move on to create your own models there's a big question that immediately occurs is which model should I use. And to help us think through that question, I'm going to talk about some of the characteristics of the models. And you need to think about the characteristics of the process you're trying to model, and map the characteristic of the business into the characteristics of the model. So at the end of this module we're going to talk about some characteristics, of some key mathematical functions that are used in modeling. And as we talk about those key characteristics, one needs to think about how they map to the characteristics of the business process, that you're interested in. We going to talk about the value and the limitations of quantitative model, as well. So what sorts of question are they really good at answering and what sorts of things would give you cause for concern in terms of applying the model itself? So it's very important to understand the limitations of models and one of the common misconceptions I believe is that many people think models can do more than they can actually do. They seem to think that there are panacea that they can answer all questions. And so, understanding their limitations is very, very important. And ultimately this course is going to provide a set of foundational material for the other courses in the specialization. In terms of resources that are used in the specialization and this course. There is software and in particular, Excel is used, Microsoft Excel, for implementing some of the models. Along with its analog, Google sheets, so those are implementation environments. In terms of graphics that are present in the slides that I'm showing you, there's a language called R which is an open source statistical, modeling platform and simulation platform that is very, very helpful. And finally, if you're a little rusty in your math. Or you just want to revisit some of the mathematical ideas as you go along through the course, then there is an E-book that you might find useful that really distills a set of mathematical ideas down to the essentials. That one needs to do quantitative business modeling. And so the idea there is that the language of quantitative modeling is mathematics and statistics. And if you're not 100% comfortable with those topics, if you're a little bit rusty, then here's a resource that can help you get up to speed very quickly on those mathematical ideas.