Master Introduction to A/B Testing in 4 weeks through hands-on, project-based online training with DSTC.
The Introduction to A/B Testing course is a free, beginner-friendly self-paced program designed to help learners understand how experiments are used to compare different versions of a product, webpage, campaign, or strategy. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
The Introduction to A/B Testing course is a free, beginner-friendly self-paced program designed to help learners understand how experiments are used to compare different versions of a product, webpage, campaign, or strategy.
1. Translate Artificial Intelligence theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
β’ Master's and senior undergraduate students specializing in Artificial Intelligence
β’ R&D engineers and working professionals applying Artificial Intelligence in industry
β’ Academics and educators building research or teaching capacity in Artificial Intelligence
β’ Tangible, reproducible Artificial Intelligence work to show supervisors or employers.
β’ A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
What is A/B Testing? β’ Importance of Experimentation in Decision-Making β’ Control Group vs Test Group β’ Applications of A/B Testing
Creating Variations for Testing β’ Selecting Metrics and Goals β’ Understanding User Behavior Data β’ Importance of Fair and Reliable Testing
Collecting and Comparing Results β’ Understanding Conversion and Engagement Metrics β’ Basic Statistical Thinking in Testing β’ Interpreting Test Outcomes
Website and App Optimization β’ Marketing Campaign Testing β’ Product and User Experience Improvements β’ Business Decision-Making Through Experiments
A/B Testing in Data Science and Analytics β’ Role of AI in Experimentation β’ Career Opportunities in Analytics and Product Testing β’ Mini Learning Activity / Concept-Based Practice
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | A/B Testing |
| Covered Tool / Platform | Experimentation |
| Covered Tool / Platform | Data Analysis |
| Covered Tool / Platform | Conversion Metrics |
| Covered Tool / Platform | User Behavior Analysis |
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