In this applied Data Science Crash Course, we cover everything you need to know about A/B testing, from the concepts to the practical details they can apply in business. The course merges in-depth statistical analysis (think hypothesis testing, significance levels, and the nitty-gritty of pooled estimates, test statistics, p-values, and assessing statistical significance) with the kind of data science theories big tech firms rely on, all alongside practical Python tutorials for real-world test implementation. Plus, there's a real-life case study thrown in to help you understand the concepts more.
✏️ Course created by Tatev Karen Aslanyan.
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⭐️ Contents ⭐️
⌨️ (0:00:00) Video Introduction
⌨️ (0:03:49) Introduction to Data Science and A/B Testing
⌨️ (0:05:38) Basics of A/B Testing in Data Science
⌨️ (0:07:06) Key Parameters of A/B Testing for Data Scientists
⌨️ (0:09:24) Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing
⌨️ (0:19:55) Designing an A/B Test: Data Science Approach
⌨️ (0:37:56) Resources for A/B Testing in Data Science
⌨️ (0:39:22) Analyzing A/B Test Results in Python: Data Science Techniques
⌨️ (1:01:00) Data Science Portfolio Project: Case Study with AB Testing
⌨️ (1:04:38) Reintroduction to A/B Testing in the Data Science Process
⌨️ (1:21:07) Data Science Techniques: Loading Data with Pandas for A/B Testing
⌨️ (1:29:19) Data Science Visualization: Using Matplotlib and Seaborn for A/B Test Click Data
⌨️ (1:38:38) Data Science Power Analysis: Understanding A/B Test Model Parameters
⌨️ (1:44:25) Data Science Calculations: Pooled Estimates and Variance for A/B Testing
⌨️ (2:06:48) Computing A/B Test P-Values: Data Science Methods for Statistical Significance
⌨️ (2:12:42) Practical Significance in A/B Testing: A Data Science Perspective
⌨️ (2:29:07) Conclusion: Wrapping Up A/B Testing in Data Science
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