4.3
stars
52 ratings

Sridhar Seshadri

7,428 already enrolled

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Data Modeling and Regression Analysis in Business

University of Illinois at Urbana-Champaign

About this Course

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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. 

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Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 25 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 25 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.5/5 (9 Ratings)

Sridhar Seshadri

Professor of Business Administration

Business Administration

16,543 Learners

2 Courses

Offered by

University of Illinois at Urbana-Champaign

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Syllabus - What you will learn from this course

Week1

Week 1

8 hours to complete

Module 0: Get Ready & Module 1: Introduction to Analytics and Evolution of Statistical Inference

8 hours to complete

14 videos (Total 93 min), 13 readings, 3 quizzes

14 videos

Welcome to Data Modeling and Regression Analysis in Business1mRattle Installation Guidelines for Windows11mR and Rattle Installation Instructions for Mac OS14mOverview of Rattle 7mLecture 1-1: Introduction to Analytics and Evolution of Statistical Inference3mLecture 1-2: From Data to Decisions9mLecture 1-3: The Evolution of Intelligent Machines3mLecture 1-4: Common Paradigms6mLecture 1-5-1: Examples of Paradigms – Part 14mLecture 1-5-2: Examples of Paradigms – Part 27mLecture 1-6: Introduction to Rattle4mLecture 1-7: Importing Datasets in Rattle8mLecture 1-8: Plotting Data and Creating Graphs in Rattle7mLecture 1-9: Rattle Practice and Summary3m

13 readings

Syllabus30mAbout the Discussion Forums10mGlossary30mBrand Descriptions30mUpdate Your Profile10mModule 0 Agenda5mRadiant and R based content in lieu of Rattle10mRattle Tutorials (Interface, Windows, Mac)30mFrequent Asked Questions10mRadiant introduction videos10mModule 1 Overview20mModule 1 Readings, Data Sets, and Slides1hModule 1 Peer Review Assignment Answer Key20m

2 practice exercises

Orientation Quiz30mModule 1 Graded Quiz30m

Week2

Week 2

6 hours to complete

Module 2: Dating with Data

6 hours to complete

8 videos (Total 63 min), 3 readings, 3 quizzes

8 videos

Lecture 2-1: Explanatory Modeling Overview5mLecture 2-2: Developing and Estimating a Model3mLecture 2-3: Univariate and Bivariate Plots8mLecture 2-4: Bivariate Correlation5mLecture 2-5-1: Estimating With Simple Models - Part 18mLecture 2-5-2: Estimating With Simple Models - Part 210mLecture 2-6: Improving the Model12mLecture 2-7: Model Improvement Practice and Summary8m

3 readings

Module 2 Overview20mModule 2 Readings, Data Sets, and Slides1hModule 2 Peer Review Assignment Answer Key20m

2 practice exercises

Module 2 Practice Problems40mModule 2 Graded Quiz30m

Week3

Week 3

6 hours to complete

Module 3: Model Development and Testing with Holdout Data

6 hours to complete

8 videos (Total 66 min), 3 readings, 4 quizzes

8 videos

Lecture 3-1: Model Development Overview3mLecture 3-2: Introducing Root Mean Square Error14mLecture 3-3: Variable Selection8mLecture 3-4: Variable Selection with R Scripts8mLecture 3-5: Introduction to Mallow's CP6mLecture 3-6-1: Modeling Example – Part 18mLecture 3-6-2: Modeling Example – Part 29mLecture 3-7: Example Wrap-Up and Summary6m

3 readings

Module 3 Overview20mModule 3 Readings, Data Sets, and Slides1hModule 3 Peer Review Assignment Answer Key20m

3 practice exercises

Module 3 Practice Problems (A)40mModule 3 Practice Problems (B)20mModule 3 Graded Quiz30m

Week4

Week 4

6 hours to complete

Module 4: Curse of Dimensionality

There has been a tremendous increase in the way data generation via sensors, digital platforms, user-generated content, etc. are being used in the industry. For example, sensors continuously record data and store it for analysis at a later point. In the way data gets captured, there can be a lot of redundancy. With more variables, comes more trouble! There may be very little (or no) incremental information gained from these sources. This is the problem of a high number of unwanted dimensions. To avoid this pitfall, data transformation and dimension reduction comes to the rescue by examining and extracting fewer dimensions while ensuring that it conveys the full information concisely.

6 hours to complete

7 videos (Total 67 min), 3 readings, 3 quizzes

7 videos

Lecture 4-1: Data Types, Data Organization, and Data Modality9mLecture 4-2: Curse of Dimensionality9mLecture 4-3: Limitations of Scatterplots10mLecture 4-4: Principle Component Analysis10mLecture 4-5: Principle Component Analysis in Rattle9mLecture 4-6: Principle Component Analysis in Rattle With Regression10mLecture 4-7: Principle Component Analysis Exercise and Summary8m

3 readings

Module 4 Overview20mModule 4 Readings, Data Sets, and Slides1hModule 4 Peer Review Assignment Answer Key20m

2 practice exercises

Module 4 Practice ProblemsModule 4 Graded Quiz30m

Reviews

4.3

14 reviews

5 stars
59.61%
4 stars
23.07%
3 stars
9.61%
1 star
7.69%

TOP REVIEWS FROM DATA MODELING AND REGRESSION ANALYSIS IN BUSINESS

by ASAug 11, 2019

The course was easy to understand and fun to practice at home which made it exciting and useful at the same time.

by BCAug 22, 2019

this course is easier to understand the statistical modeling, no need to learn the deep algorithm

View all reviews

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Data Modeling and Regression Analysis in Business

Data Modeling and Regression Analysis in Business

About this Course