Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This comprehensive course introduces the concepts, processes, and applications of predictive modeling, focusing on linear regression, time series forecasting, and data preparation using Microsoft Excel.
Throughout the course, you will gain a solid understanding of predictive modeling, including the mechanics of regression models, model selection, and the handling of missing values. You will also delve into time series forecasting, learning about components of time series, model accuracy metrics, moving averages, exponential smoothing, and Holt-Winters' method. With practical hands-on learning opportunities, you will be able to apply these techniques to real-world datasets using Excel.
Designed for learners with basic math and statistics knowledge and familiarity with Excel, this course does not require a background in programming. By the end of the course, you will have the skills to fit linear regression models, conduct time series forecasting, and prepare data for predictive modeling in Excel.
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Introduction to Predictive Modeling comprises four modules, covering simple linear regression, multiple linear regression, data preparation, and time series forecasting in Excel. Each module provides a comprehensive understanding of predictive modeling techniques, equipping learners with practical skills for real-world applications.
Week 1 focuses on simple linear regression, providing an introduction to the mechanics of regression models and practical exercises using Excel. By the end of this module, learners will understand the basics of linear regression and its application for prediction.
Module 2 delves into multiple linear regression, emphasizing model fit, prediction, and building good regression models. Learners will gain practical skills in fitting and interpreting multiple regression models using Excel.
In Module 3, learners will explore the importance of data preparation, including handling different types of variables, using Excel functions for encoding, and dealing with missing values. This module equips learners with essential skills for data preprocessing in predictive modeling.
The final module covers time series forecasting, encompassing various models such as moving averages, exponential smoothing, Holt-Winters' method, and time series regression. Learners will acquire practical skills in forecasting and model evaluation using Excel.
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