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Data Science Essentials – Crash Course in A⧸B Testing with Full Project [2.5H]
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Streamline your journey to becoming a data science expert with our FREE workbook for 'The Complete Data Science Roadmap [2024]'! We've simplified the key steps and concepts, so you can focus on mastering the essentials without the overwhelm.
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.
⭐️ 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
#datascience2024 #lunartechai #abtesting #uxdesigner #lunartech #machinelearning #datascience #dataanalytics #softwareengineer #customerengagement
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.
⭐️ 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
#datascience2024 #lunartechai #abtesting #uxdesigner #lunartech #machinelearning #datascience #dataanalytics #softwareengineer #customerengagement
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