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Data Modeling and Regression Analysis in Business

Approx. 25 hours to complete

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Course Summary

Learn how to use regression analysis to make data-driven business decisions in this comprehensive course. From data modeling to creating predictive models, you'll gain practical skills and knowledge to apply in your career.

Key Learning Points

Understand the fundamentals of data modeling and regression analysis
Learn how to use regression analysis to make data-driven business decisions
Create predictive models to forecast future trends and outcomes

Learning Outcomes

Create and interpret data models to inform business decisions
Use regression analysis to develop predictive models
Apply regression analysis to real-world business scenarios

Prerequisites or good to have knowledge before taking this course

Basic understanding of statistics and probability
Familiarity with Excel or other data analysis tools

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video-based

Similar Courses

Data Analysis and Regression Modeling in Business Analytics
Data Science Essentials
Business Analytics

Related Education Paths

Related Books

Description

The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.

Outline

Module 0: Get Ready & Module 1: Introduction to Analytics and Evolution of Statistical Inference
Welcome to Data Modeling and Regression Analysis in Business
Rattle Installation Guidelines for Windows
R and Rattle Installation Instructions for Mac OS
Overview of Rattle
Lecture 1-1: Introduction to Analytics and Evolution of Statistical Inference
Lecture 1-2: From Data to Decisions
Lecture 1-3: The Evolution of Intelligent Machines
Lecture 1-4: Common Paradigms
Lecture 1-5-1: Examples of Paradigms – Part 1
Lecture 1-5-2: Examples of Paradigms – Part 2
Lecture 1-6: Introduction to Rattle
Lecture 1-7: Importing Datasets in Rattle
Lecture 1-8: Plotting Data and Creating Graphs in Rattle
Lecture 1-9: Rattle Practice and Summary
Syllabus
About the Discussion Forums
Glossary
Brand Descriptions
Update Your Profile
Module 0 Agenda
Rattle Tutorials (Interface, Windows, Mac)
Frequent Asked Questions
Module 1 Overview
Module 1 Readings, Data Sets, and Slides
Module 1 Peer Review Assignment Answer Key
Orientation Quiz
Module 1 Graded Quiz

Module 2: Dating with Data
Lecture 2-1: Explanatory Modeling Overview
Lecture 2-2: Developing and Estimating a Model
Lecture 2-3: Univariate and Bivariate Plots
Lecture 2-4: Bivariate Correlation
Lecture 2-5-1: Estimating With Simple Models - Part 1
Lecture 2-5-2: Estimating With Simple Models - Part 2
Lecture 2-6: Improving the Model
Lecture 2-7: Model Improvement Practice and Summary
Module 2 Overview
Module 2 Readings, Data Sets, and Slides
Module 2 Peer Review Assignment Answer Key
Module 2 Practice Problems
Module 2 Graded Quiz

Module 3: Model Development and Testing with Holdout Data
Lecture 3-1: Model Development Overview
Lecture 3-2: Introducing Root Mean Square Error
Lecture 3-3: Variable Selection
Lecture 3-4: Variable Selection with R Scripts
Lecture 3-5: Introduction to Mallow's CP
Lecture 3-6-1: Modeling Example – Part 1
Lecture 3-6-2: Modeling Example – Part 2
Lecture 3-7: Example Wrap-Up and Summary
Module 3 Overview
Module 3 Readings, Data Sets, and Slides
Module 3 Peer Review Assignment Answer Key
Module 3 Practice Problems (A)
Module 3 Practice Problems (B)
Module 3 Graded Quiz

Module 4: Curse of Dimensionality
Lecture 4-1: Data Types, Data Organization, and Data Modality
Lecture 4-2: Curse of Dimensionality
Lecture 4-3: Limitations of Scatterplots
Lecture 4-4: Principle Component Analysis
Lecture 4-5: Principle Component Analysis in Rattle
Lecture 4-6: Principle Component Analysis in Rattle With Regression
Lecture 4-7: Principle Component Analysis Exercise and Summary
Module 4 Overview
Module 4 Readings, Data Sets, and Slides
Module 4 Peer Review Assignment Answer Key
Module 4 Graded Quiz

Summary of User Reviews

Learn data modeling & regression analysis for business in this Coursera course. Users highly recommend it for its practical approach and real-life examples.

Key Aspect Users Liked About This Course

The practical approach and real-life examples provided by the course.

Pros from User Reviews

Plenty of real-life examples to help understand the concepts
The course is taught in a practical and easy-to-understand manner
Great for beginners to get a solid foundation in data modeling and regression analysis
The course is well-structured and easy to follow
The instructor is knowledgeable and engaging

Cons from User Reviews

Some users found the course to be too basic
The course could benefit from more advanced topics
Some users found the pace of the course to be slow
There is a lot of repetition in the course content
The course could benefit from more interactive elements

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