Hello and welcome to this specialization on Analytics for Decision Making. I'm Soumya Sen and I'm an associate professor of Information and Decision Sciences at the Carlson School of Management of the University of Minnesota. I'll be teaching courses two and three on optimization and advanced optimization for decision making. I'm here with my colleagues to talk about this specialization. I'd like to invite Professor De Lieu, to talk about his course. Hi, my name is De Lieu. I am a professor of Information and Decision Sciences at Carlson School of Management, University of Minnesota. I'll be glad to teach the first course, introduction to Predictive Modeling. Next, I'd like to introduce Alok Gupta. Hello, my name is Alok Gupta. I'm Associate Dean of Faculty and Research at Carlson School of Management at University of Minnesota. I'm also a professor at Information and Decision Sciences department. I'll be teaching the last course in this specialization, which focuses on Simulation Modeling. Simulation is one of the most flexible modeling techniques in analytics and I'm excited to be part of this journey with you. I'll now, send it back to Soumya to talk a little bit more about this specialization. The field of data analytics has many different methods that one can use. But the first step is you have a clean dataset. We're going to use the principles of data engineering in Professor De Lieu's course to create a clean dataset that we can work with. Next, we have different methods to choose from. The first is descriptive analytics, which is usually used to gain some initial insights, explore the data and find some patterns. There is predictive analytics, which we use to predict the values of variables of interest into the future. For example, you may be interested in predicting costs or the capacity of production, or even things like the yield of crops in the future. Then we have causal inference or causal analytics that is used to establish a causal relationship between some variables or factors of interest and then outcome of interest. Lastly, we have prescriptive analytics in which we use methods such as optimization and simulation in order to provide some best or optimal strategies that the company or the firm should adopt. These techniques usually build on predictive analytics. For example, predictive analytics can be used to project the demand or capacity or cost. Once you have those predicted values in the future, you can use it to decide what is the best course of action through using optimization or simulation models. We are going to look at these in the four courses that constitute this specialization. Now I'd like to invite Professor De Lieu to talk about his course, the first course in this specialization. Thank you, Soumya. Predictive modeling is arguably one of the most popular form of analytics. You can use it to predict crop yield using rainfall and temperature. How to predict house prices using location, size and other attributes, how to predict or store sales using past historical data. This course is designed to help you get started with predictive modeling. We are going to first introduce to you the important steps, concepts and techniques of modeling and then using regression-based models as example. Then we move on to teach you how to prepare data for predictive modeling in Excel, including dealing with missing values and dealing with different types of data. Finally, we'll spend some time on special and very useful type of predictive modeling, time series forecasting. I hope this course can not only expose you to the art of sciences of predictive modeling, but also provide a good exercise for your Excel muscles and get you ready for the remaining courses in this specialization and beyond. Next, I'll let Soumya talk about his next course. Thank you, De. In the course on optimization for decision-making, we are going to take the predictions to the next level where we are going to go from predictions to decision-making using optimization in face of different types of organizational constraints, such as budgetary constraint. We are going to look at different business contexts that can be modeled in an optimization framework and we're going to learn how to model linear optimization problem and use simplex method to find the optimal solution. For doing this, we will be using an Excel solver tool and working on several problems of practical relevance. We are going to use the solver tool in Excel to find the best strategy and prescribe that for the particular company. This is going to provide you with the basics of optimization. In the third course, which is the course on advanced models for decision making, we are going to start looking at more complex models by formulating them again as an optimization problem and then using that to prescribe decision-making in very real-world contexts. We're going to see applications of that in finance and cash flow management, in supply chains and inventory management, in human resource management and questions such as, allocation of officers or office assignment and scheduling staff. We're also going to see applications of these optimization models in production optimization. In all of these, we're going to use Excel, which is an easy to use tool in order to run this optimization problems using the solver built-in tool. Next, I'd like to invite Professor Alok Gupta, to talk about his course, which is the final course in this specialization. Thank you, Soumya. As I mentioned earlier, simulation is one of the most flexible analytics tool. The key difference as a tool as compared to other approaches that you learn in this specialization, it is that instead of single answer, we can typically get a range of answers with some measure of confidence associated with each outcome. In fact, simulation is often thought off as an art as well as science, and we'll try to cover both aspects in this course. Typically art refers to the issue of creating a model at the right level and of right type, depending upon the questions that you want to answer. I'll also include some tips and tricks that you usually don't find in books or academic literature and we'll also use material that you learn in other courses, such as optimization in this specialization to estimate parameters that we need to use in simulation model. I'll use one primary context to keep the continuity of ideas and address building increasingly complex and more sophisticated models. But the principles are general and are applicable to any simulation exercise. Similar to other courses in this specialization, we'll use Excel as the primary tool. A large part of the course focuses on what we call Monte-Carlo simulation but we also touch upon discrete event simulation, which goes into details of modeling individual level processes. We are really excited to have you as a part of this journey through these courses. While the content of the course has academic rigor is important to emphasize that you can readily apply these techniques of predictive analytics, prescriptive analytics including optimization and simulation in your everyday work environment, and turn yourself into a data analytics star. We look forward. To seeing you- In the course.