University of Minnesota

Introduction to Predictive Modeling

This course is part of Analytics for Decision Making Specialization

Taught in English

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De Liu

Instructor: De Liu

9,467 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(96 reviews)

12 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

20 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(96 reviews)

12 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Analytics for Decision Making Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

This module provides a brief overview of predictive modeling problems, illustrating their broad applications. It then focuses on the simplest form of predictive models: simple linear regression. The module follows a graphical approach to illustrate the structure of a simple linear regression model, the intuition for Ordinary Least Squares, and related concepts. Finally, we demonstrate how to use various Excel tools, including trendlines, the Regression tool, and the Trend() function, to fit a simple linear regression model and use it to form predictions.

What's included

9 videos1 reading4 quizzes1 discussion prompt

Building on Week 1, in this week we introduce multiple linear regression and its broad applications. Then, we cover how to fit a multiple linear regression model using Excel’s Regression tool and Trend() function and use the resulting model for predictions. The module further discusses the overfitting/underfitting problems and the basic principles of a good regression model. The module also introduces one approach for selecting a good model: backward elimination that can be implemented in Excel.

What's included

8 videos1 reading4 quizzes

In this week, we will learn how to prepare a dataset for predictive modeling and introduce Excel tools that can be leveraged to fulfill this goal. We will discuss different types of variables and how categorical, string, and datetime values may be leveraged in predictive modeling. Furthermore, we will discuss the intuition for including high-order and interaction variables in regression models, the issue of multicollinearity, and how to handle missing values. We will also introduce several handy Excel tools for data handling and exploration, including Pivot Table, IF() function, VLOOKUP function, and relative reference.

What's included

13 videos6 quizzes1 discussion prompt

This module focuses on a special subset of predictive modeling: time series forecasting. We discuss the nature of time-series data and the structure of time series forecasting problems. We then introduce a host of time series models for stationary data and data with trends and seasonality, with a focus on techniques that are easily implemented within Excel, including moving average, exponential smoothing, double moving average, Holt’s method, and Holt-Winters’ method. The module also covers linear-regression-based forecasting and a composite forecasting technique for boosting accuracy.

What's included

19 videos2 readings6 quizzes1 discussion prompt

Instructor

Instructor ratings
4.9 (39 ratings)
De Liu
University of Minnesota
1 Course9,467 learners

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Recommended if you're interested in Probability and Statistics

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