Data Science Essentials - Crash Course in A/B Testing with Case Study

Data Science Essentials - Crash Course in A/B Testing with Case Study

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⌨️ Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing

5 of 17

5 of 17

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

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