Hello and welcome. One important reason, more and more businesses are investing in analytics platforms, machine learning, and artificial intelligence is to predict the future outcomes. Future of work lies in not just understanding the as this picture of business, but also to accurately predict the impact of today's decisions on company's future. This week, we'll look at how we can generate actionable insights that when implemented, will provide the businesses with a predictable future outcome. We will start to accomplish that goal by studying a powerful technique called linear regression analysis to establish the relationship between two business variables. This simple yet effective data modeling technique is helpful in formulating actions that you should recommend to the stakeholders in solving a business problem. There are a variety of models available to us that could range from simple linear regression equations to complex neural networks. However, we will cover only the basic modeling techniques, and show you how to implemented using Tableau. Next, we'll move on to another area where making predictions is very important and is known as demand forecasting. Many companies invest a lot of money on technology, skills, and processes to get the demand forecasting right. As a future manager, you should be well-versed in the terminology used in the implementation of demand forecast modeling. For producing great forecast models, time plots is a key visualization tool for interpreting demand over time. We'll study time plots using an example, and we'll gain knowledge on its constituents. Finally, we'll finish the week by getting an understanding on smoothing methods that help in analyzing the time plots, reducing the level of noise in the data, and are commonly used in prediction tasks. We'll explain this by doing a hands-on exercise using real life data. Let's get started.