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Welcome to Experimentation in Data Science. I’m Catherine Truxillo. Since 2000, I’ve been in the Advanced Analytics Education group at SAS, where I’ve taught many courses covering statistics and machine learning topics using SAS software. I also manage a global team of analytical professionals who help people use SAS software to make smarter decisions based on data.

The principles of experimentation can help you to effectively and efficiently determine what works and doesn’t work in business decisions, identify your most profitable segments, and eliminate noise due to nuisance effects. In this course, you’ll learn about experimentation in business and why experiments are so critical to successful model implementation.

You’ll also learn some important terminology for experimentation. Then, we’ll take a look at incremental response modeling. You will learn to use Enterprise Miner software to fit this useful and interesting class of models and we’ll evaluate and discuss the results. Let’s get started!

Course Curriculum

1.0 Experimentation in Business Introduction
1.0 Experimentation in Business Introduction 00:00:00
1.0 Experimentation in Business Objectives
1.0 Experimentation in Business Objectives 00:00:00
1.1a Answering Business Questions
1.1a Answering Business Questions 00:00:00
1.1b Considerations for Formulating Experiments
1.1b Considerations for Formulating Experiments 00:00:00
1.1c Accounting for Variables that Cannot be Controlled
1.1c Accounting for Variables that Cannot be Controlled 00:00:00
1.1d Using Experiments in Business
1.1d Using Experiments in Business 00:00:00
1.2a Learning Terminology for Design of Experiments
1.2a Learning Terminology for Design of Experiments 00:00:00
1.2b Terminology- Response
1.2b Terminology- Response 00:00:00
1.2c Terminology- Factor and Factor Level
1.2c Terminology- Factor and Factor Level 00:00:00
1.2d Terminology- Effect
1.2d Terminology- Effect 00:00:00
1.2e Terminology- Treatment 00:00:00
1.2e Terminology- Treatment
1.2e Terminology- Treatment 00:00:00
1.2 Think About It
1.2 Think About It 00:00:00
1.2f Terminology- Experimental Unit, Replication, and Power
1.2f Terminology- Experimental Unit, Replication, and Power 00:00:00
1.2g Designing Efficient Experiments
1.2g Designing Efficient Experiments 00:00:00
1.2h Randomization in Experiments
1.2h Randomization in Experiments 00:00:00
1.2i Terminology- Orthogonality
1.2i Terminology- Orthogonality 00:00:00
1.2j Two-Level Full Factorial Coding
1.2j Two-Level Full Factorial Coding 00:00:00
1.2k Factorial Arrangement versus OFAT Tests
1.2k Factorial Arrangement versus OFAT Tests 00:00:00
1.2l Factors, Blocks, and Covariates
1.2l Factors, Blocks, and Covariates 00:00:00
1.2m Other Designed Experiments
1.2m Other Designed Experiments 00:00:00
1.2n Think About It
1.2n Think About It 00:00:00
2.0 Incremental Response Models Introduction
2.0 Incremental Response Models Introduction 00:00:00
2.0 Objectives
2.0 Objectives 00:00:00
2.1a Combining Experimentation with Predictive Modeling
2.1a Combining Experimentation with Predictive Modeling 00:00:00
2.1b Using Data Science to Inform Business Decisions
2.1b Using Data Science to Inform Business Decisions 00:00:00
2.1c Understanding Incremental Response
2.1c Understanding Incremental Response 00:00:00
2.1d Understanding the Role of the Control Group in Incremental Response
2.1d Understanding the Role of the Control Group in Incremental Response 00:00:00
2.1e Explaining the Importance of Incremental Response
2.1e Explaining the Importance of Incremental Response 00:00:00
2.1f Defining Incremental Response
2.1f Defining Incremental Response 00:00:00
2.2a Incremental Response Modeling
2.2a Incremental Response Modeling 00:00:00
2.2b Data Structure for Incremental Response Modeling
2.2b Data Structure for Incremental Response Modeling 00:00:00
2.2c Variable Selection in Incremental Response Modeling
2.2c Variable Selection in Incremental Response Modeling 00:00:00
2.2d Difference Score Model
2.2d Difference Score Model 00:00:00
2.2e Stepwise Regression Model
2.2e Stepwise Regression Model 00:00:00
2.2f Incremental Sales Model
2.2f Incremental Sales Model 00:00:00
2.2g Incremental Response/Sales Model Results
2.2g Incremental Response/Sales Model Results 00:00:00
2.2h Model Diagnostics
2.2h Model Diagnostics 00:00:00
2.2i Incremental Revenue Analysis
2.2i Incremental Revenue Analysis 00:00:00
2.2j Demo- Incremental Response Modeling
2.2j Demo- Incremental Response Modeling 00:00:00
Course Notes: Experimentation in Data Science
Course Notes: Experimentation in Data Science 00:00:00

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  • 1 week, 3 days
1 STUDENTS ENROLLED
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