The Data Science of Experimental Design
Offered By: LinkedIn Learning
Course Description
Overview
Learn how to design an A/B test for a web page, run the test, analyze the data, and make decisions based on the results of the test.
Syllabus
Introduction
- Conducting an experiment online
- What you should know
- Experiment is a type of study
- Features of an experiment
- Circumstances for experimental testing
- When not to do an experiment
- Systems ready for experimental testing
- Comparability of experimental conditions
- Trying to increase conversions
- Different types of conversions
- Case definition of conversion
- Measuring a conversion
- Considering time period for conversions
- Rates versus frequencies of conversions
- Identify and prioritize conversions
- Operationalize counting conversions
- Document conversion case definitions
- Brainstorm denominators
- False positives and negatives
- Document denominators
- Determine time frames
- Baseline time-series analyses
- Data handling
- Baseline results as a guide
- Thinking about increasing conversions
- Strategies to increase conversions
- Planning a campaign
- Designing the test
- Testing the implementation
- Choosing a test statistic
- Choosing the chi-squared test
- Chi-squared test in Excel
- Installing G*Power
- Using G*Power
- Sample size simulation
- Planning the timeline
- Stratified analysis
- Conditional tests
- Overall analysis approach
- Time-series analysis
- Chi-squared analysis
- Interpretation
- What actions can we take?
- Report writing
Taught by
Monika Wahi
Related Courses
Data AnalysisJohns Hopkins University via Coursera Computing for Data Analysis
Johns Hopkins University via Coursera Scientific Computing
University of Washington via Coursera Introduction to Data Science
University of Washington via Coursera Web Intelligence and Big Data
Indian Institute of Technology Delhi via Coursera