This course will introduce you to cleaning data and replacing missing values with imputed data to support demand forecasting. Demand forecasts are used to maximize revenue, build efficiencies in operational planning, and to drive future growth. Forecasting techniques can be applied to make realistic predictions of outcomes of everything from how demand affects pricing and sales opportunities to operational planning for electrical utilities and healthcare facilities. We can only have confidence in the demand predictions we produce, when we also have confidence in the data quality feeding those predictions. Ensuring that confidence requires using clean data with no missing values for our forecast models. Handling missing data is an essential part of prepping clean data for a demand forecast. In this course, we will review the principles of applying central measures of tendency and regression techniques to impute missing values. As you clean the data, you will visualize it with charts, replace inconsistent values and impute values while comparing the outcomes of the statistical techniques you have applied. When your data is clean, you will create a demand forecast. You will do this as we work side-by-side in the free-to-use software Google Sheets. By the end of this course, you will understand use cases for imputing missing values and be able to confidently apply multiple statistical imputation techniques in any spreadsheet software. Note: This course works best for learners who are based in the North America region. We re currently working on providing the same experience in other regions.