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 indepth statistical analysis (think hypothesis testing, significance levels, and the nittygritty of pooled estimates, test statistics, pvalues, and assessing statistical significance) with the kind of data science theories big tech firms rely on, all alongside practical Python tutorials for realworld test implementation. Plus, there's a reallife case study thrown in to help you understand the concepts more.

✏ Course created by Tatev Karen Aslanyan.
More from Tatev here: https://lunartech.ai/

⭐ 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 PValues: 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

Thanks to our Champion and Sponsor supporters:
davthecoder
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南宮千影
Agustín Kussrow
Nattira Maneerat
Heather Wcislo
Serhiy Kalinets
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Otis Morgan
Oscar Rahnama



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