Overview
Learn How To Define, Start, And Analyze The Results Of An A/B Test. Improve Business Performance Through A/B Testing
Syllabus
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- Introduction to A/B Testing
-- Definition and Purpose
-- Historical Context and Applications
-- Importance in Data-Driven Decision Making
- Fundamental Concepts of A/B Testing
-- Control and Treatment Groups
-- Randomization and Bias Reduction
-- Key Metrics and KPIs
- Designing an A/B Test
-- Hypothesis Formulation
-- Sample Size Determination
-- Test Duration and Run-Time Considerations
- Conducting A/B Tests with Python
-- Introduction to Python Libraries: NumPy, Pandas, SciPy
-- Loading and Preparing Data
-- Exploratory Data Analysis for A/B Testing
- Statistical Analysis in A/B Testing
-- Descriptive vs. Inferential Statistics
-- Significance Testing: p-value and Confidence Intervals
-- Types of Statistical Tests: t-Tests, Chi-Square Tests
- Implementing A/B Tests in Python
-- Writing Code for Test Execution
-- Handling Data Inconsistencies and Anomalies
-- Interpreting Test Results
- Advanced Techniques in A/B Testing
-- Multi-armed Bandit Approach
-- Sequential Testing Methods
-- Bayesian A/B Testing
- Case Studies and Real-World Applications
-- E-commerce and Conversion Rate Optimization
-- Product Feature Testing
-- Marketing Campaign Evaluation
- Best Practices and Ethical Considerations
-- Avoiding Common Pitfalls and Misinterpretations
-- Ensuring Statistical Power and Validity
-- Ethical Implications in Experimentation
- Tools and Resources for A/B Testing
-- Overview of Industry Tools and Platforms
-- Open-source Libraries and Community Resources
-- Further Reading and Research Papers
- Final Project
-- Design, Conduct, and Present an A/B Test
-- Peer Reviews and Feedback
-- Discussion and Insights on Learning Outcomes
Taught by
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On-Demand
