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Introduction to Predictive Modeling

Approx. 12 hours to complete

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Course Summary

This course teaches the fundamentals of predictive modeling and how to apply them to real-life business problems. Students will learn the entire process of creating a predictive model, from data collection and preparation to model selection and evaluation.

Key Learning Points

Learn the fundamentals of predictive modeling
Apply predictive modeling to real-life business problems
Understand the entire process of creating a predictive model

Learning Outcomes

Develop the ability to create predictive models for real-life business problems
Learn to select and evaluate models based on their performance
Understand the importance of data preparation in the predictive modeling process

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with programming in R or Python

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on exercises

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Description

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization.

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. By the end of the course, you will be able to: - Understand the concepts, processes, and applications of predictive modeling. - Understand the structure of and intuition behind linear regression models. - 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 problem of overfitting and underfitting and be able to conduct simple model selection. - Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling. - 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 different types of data and how they may be used in predictive models. - Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values. This is an introductory course to predictive modeling. The course provides a combination of conceptual and hands-on learning. During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel. 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). This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). For the best experience, you should have a recent version of Microsoft Excel installed on your computer (e.g., Excel 2013, 2016, 2019, or Office 365).

Outline

Week/Module 1: Simple Linear Regression
Analytics for Decision Making Specialization
Personal Introduction
Course Overview
Week/Module 1 Overview: What You Will Learn This Week
Introduction to Predictive Modeling
Introduction to Linear Regression
Understanding the Mechanics of a Regression Model
Using Excel to Conduct Linear Regression
Using Linear Regression for Prediction
Read this article on Applications of Predictive Analytics
Practice Quiz: Introduction to Linear Regression
Practice Quiz: Understanding the Mechanics of a Regression Model
Practice Quiz on Using Excel to Conduct Linear Regression
Week 1 Graded Quiz: Understanding Linear Regression

Week/Module 2: Multiple Linear Regression
Week 2 Overview on Multiple Linear Regression
What is Multiple Linear Regression?
Understand Model Fit and Prediction using Multiple Regression
Fitting and Interpreting Multiple Regression Models using Regression Tool
Making Predictions using the Regression Tool
Making Predictions using the Trend function
Building Good Regression Models
A Demonstration of Backward Elimination
Reading more on model specification and overfitting
Practice Quiz on an "Introduction to Multiple Linear Regression"
Practice Quiz on "Model Fit and Interpretation"
Practice Quiz on "Model Selection"
Module 2 Graded Quiz on Multiple Linear Regression

Week/Module 3: Data Preparation
Week 3 Overview: Preparing Your Data
Why Is Data Preparation Important?
Working with Different Types of Variables
Handling Different Types of Variables
Using Excel Pivot Table to Explore Column Values
Using Excel VLOOKUP to Encode Ordinal Variables
Using Excel IF function to Encode Nominal Variables
Other Uses of VLOOKUP and IF functions
Handling Data/Time Variables
Excel Demonstration of Handling Data/Time Variables
Handling High Order, Interaction Variables
Interaction Variables
Handling Missing Values & Module Summary
Practice Quiz on "Introduction to Data Preparation"
Practice Quiz on "String Variables"
Practice Quiz on "Date/Time Variables"
Practice Quiz on "High-Order and Interaction Variables"
Practice Quiz on "Handling Missing Values"
Module 3 Graded Quiz on "Preparing Your Data""

Week/Module 4: Time Series Forecasting
Week 4 Overview: Time Series Forecasting
Time Series Data and Time Series Forecasting
Components of Time Series
Model Accuracy Metrics
Moving Averages
How to Forecast using the Moving Averages Model
The Exponential Smoothing Model
Demonstration of Exponential Smoothing
Double Moving Averages
Demonstration of Double Moving Averages
Double Exponential Smoothing (Holt's Method)
Holt-Winters' Additive Model
A Demonstration of Holt-Winters' Additive Model
Holt-Winters' Multiplicative Model
Time Series Regression
Composite Forecast
Course Wrap Up: A Summary of What You Have Learned in this Course
Congratulations on Finishing "Introduction to Predictive Modeling"!
Carlson School of Management: Master of Science Program in Business Analytics (MSBA)
Carlson School of Management: MSBA Program Website
Management Information Systems (MIS) Research Center
Practice Quiz an "Introduction to Time Series Forecasting"
Practice Quiz on "Models for Stationary Data"
Practice Quiz on Time Series with Trends
Practice Quiz on "Time Series with Trends and Seasonality"
Practice Quiz on "Forecasting using Regression and Composite Models"
Week 4 Graded Quiz on "Time Series Forecasting"

Summary of User Reviews

This course on Introduction to Predictive Modeling has received positive reviews from its users. It is highly recommended for those who want to learn about predictive modeling. The course has been praised for its comprehensive content and interactive exercises.

Key Aspect Users Liked About This Course

The course has been praised for its comprehensive content and hands-on approach to learning.

Pros from User Reviews

The course material is well-structured and easy to follow
The interactive exercises provide a great learning experience
The instructors are knowledgeable and provide clear explanations
The course covers a wide range of topics in predictive modeling
The course provides practical applications of predictive modeling

Cons from User Reviews

Some users found the course to be too basic
The pace of the course may be too slow for advanced users
Some users felt that the course could have been more challenging
The course does not cover advanced topics in predictive modeling
The course may not be suitable for those with no prior knowledge of statistics

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