This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). To succeed in this course, you should know basic math (the concept of functions, variables, and basic math notations such as summation and indices) and basic statistics (correlation, sample mean, standard deviation, and variance). During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel. The course provides a combination of conceptual and hands-on learning. This is an introductory course to predictive modeling. Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values. Understand different types of data and how they may be used in predictive models. Be able to fit several time-series-forecasting models (e.g., exponential smoothing and Holt-Winter’s method) in Excel, evaluate the goodness of fit, and use fitted models to make forecasts. Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling. Understand the problem of overfitting and underfitting and be able to conduct simple model selection. Be able to fit simple and multiple linear regression models to data, interpret the results, evaluate the goodness of fit, and use fitted models to make predictions. Understand the structure of and intuition behind linear regression models. Understand the concepts, processes, and applications of predictive modeling.

By the end of the course, you will be able to: This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization.

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