A/B Testing, A Data Science Perspective [Online Code]
A/B Testing, A Data Science Perspective [Online Code] by O'Reilly Media at ITHKS. Hurry! Limited time offer. Offer valid only while supplies last. Number of Videos: 1.25 hours - 9 lessons Author:Lisa Qian User Level:Beginner Deciding whether or not to launch a new product or feature is a
Deciding whether or not to launch a new product or feature is a resource management bet for any Internet business. Conducting rigorous online A/B tests flattens the risk. Drawing on her experience at Airbnb, data scientist Lisa Qian offers a practical ten-step guide to designing and executing statistically sound A/B tests. - Discover best practices for defining test goals and hypotheses - Learn to identify controls, treatments, key metrics, and data collection needs - Understand the role of appropriate logging in data collection - Determine how to frame your tests (size of difference detection, visitor sample size, etc.) - Master the importance of testing for systematic biases - Run power tests to determine how much data to collect - Learn how experimenting on logged out users can introduce bias - Understand when cannibalization is an issue and how to deal with it - Review accepted A/B testing tools (Google Analytics, Vanity, Unbounce, among others) Lisa Qian focuses on search and discovery at Airbnb. She has a PhD in Applied Physics from Stanford University.
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Features & Highlights
- Learn A/B Testing, A Data Science Perspective from a professional trainer on your own time at your own desk.
- This visual training method offers users increased retention and accelerated learning.
- Breaks even the most complex applications down into simplistic steps.