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⌨️ Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing
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Classroom Contents
Data Science Essentials - Crash Course in A/B Testing with Case Study
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- 1 ⌨️ Video Introduction
- 2 ⌨️ Introduction to Data Science and A/B Testing
- 3 ⌨️ Basics of A/B Testing in Data Science
- 4 ⌨️ Key Parameters of A/B Testing for Data Scientists
- 5 ⌨️ Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing
- 6 ⌨️ Designing an A/B Test: Data Science Approach
- 7 ⌨️ Resources for A/B Testing in Data Science
- 8 ⌨️ Analyzing A/B Test Results in Python: Data Science Techniques
- 9 ⌨️ Data Science Portfolio Project: Case Study with AB Testing
- 10 ⌨️ Reintroduction to A/B Testing in the Data Science Process
- 11 ⌨️ Data Science Techniques: Loading Data with Pandas for A/B Testing
- 12 ⌨️ Data Science Visualization: Using Matplotlib and Seaborn for A/B Test Click Data
- 13 ⌨️ Data Science Power Analysis: Understanding A/B Test Model Parameters
- 14 ⌨️ Data Science Calculations: Pooled Estimates and Variance for A/B Testing
- 15 ⌨️ Computing A/B Test P-Values: Data Science Methods for Statistical Significance
- 16 ⌨️ Practical Significance in A/B Testing: A Data Science Perspective
- 17 ⌨️ Conclusion: Wrapping Up A/B Testing in Data Science
