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In this course, you will :
- Learn how to set up an experiment and the differences between experiments and observational studies.
- Learn about real-world statistics applications, key metrics, and SMART experiments: Specific, Measurable, Actionable, Realistic, and Timely.
- Learn about the following sources of bias: novelty and recency effects, as well as multiple comparison techniques (FDR, Bonferroni, Tukey).
- Understand the flaws of traditional methods, as well as the flaws in measuring the influence of recommendation engines using traditional regression and classification techniques.
Syllabus :
- Experiment Design
- Statistical Concerns of Experimentation
- A/B Testing
- Introduction to Recommendation Engines
- Matrix Factorization for Recommendations
