Welcome to the fourth course of data analytic for business. In the first course of the specialization, they've covered how analytic problems emerge in an information life cycle that begins with events in the real world and ends with business actions. In his course, you also learned about technologies that enable analytical work. You examine data storage and data basis, along with big data and cloud technologies. And then in the second course, walk you through explanatory data analysis and prediction models. In the third course, you and I made a transition from predictive analytics to prescriptive analytics. You learn about capturing uncertainty through simulation, and you also learn about building optimization models. These three courses have given you a solid foundation of data analytics for business problems. Obviously, the topics that we have covered so far could be explored at the greater detail, which by the way that's exactly what we do hear at the Leeds School in our Master's Program in Business Analytics. So you might want to think about coming to Boulder for a year. Anyway, at this point you do have an overall understanding of how analytical organizations use data. You also know about the main techniques and models for data analytics. However, all the knowledge and the hard work of doing analysis to gain insights, are not enough to produce actionable items and results. The missing piece of the puzzle is effective communication, and this is what Dave, Dan and I are going to discuss in this fourth course. Tables and graphs are the main vehicles communicate quantitative information. However, few of those individuals making presentations or writing documents, have ever taken a formal course on designing effective tables and graphs. When should a table be used? When should a graph be used? If a graph is used, which type of graph should it be? So as part of this course, we will discuss simple guidelines that will help you make these decisions. If you Google data visualization you will get almost 30 million webpages. Data visualization tools have become very popular because of their power to communicate quantitative information through visual perception. A single well designed graph is able to communicate an incredibly large amount of information. Graphs are particularly helpful when you're trying to communicate patterns, trends or receptions. They are also effective when comparing several series of values. Data visualization is definitely great, but there are things that you need to avoid and you will learn about them here. The end of the day, communicating analytical resource is about telling a story, with numbers. Tables and graphs are instruments, but they're not the story. Your facts and findings are the story, and you need to communicate them well. Effective communication, it starts with knowing your audience. Then, you also need to know general principles of effective storytelling. I am glad that you have made it to this point of our specialization. Dave, Dan and I work really hard on creating a comprehensive view of data analytics. What you experience in the first three courses, and what you will experience in this one is the result of a coordinated effort. We did not want to just each of us do our own thing. This is our collective view of data analytics in the context of business problems and decision making.