Better Software Through Measurement
Pluralsight
Course Summary
This course will show you how to generate recommendations for your users, filter messages based on users' preferences, decide which web page performs best, keep track of timings in your application, and discover groups among items.
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Course Description
This course will show you how to generate recommendations for your users, filter messages based on users' preferences, decide which web page performs best, keep track of timings in your application, and discover groups among items. These techniques are at the heart of many of the largest search engines and online retailers, but can be used to good effect for smaller companies. Throughout the course, the emphasis will be on examining and extending working sample code. The algorithms will be presented intuitively and you do not need any advanced math background.
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Course Syllabus
Instrumentation: Streaming Metrics- 19m 8s
—Measure Everything 3m 36s
—DEMO: Streaming Mean and Variance 2m 36s
—Quantiles with QDigest 5m 21s
—DEMO: QDigest Implementation 2m 46s
—Calculating Quantiles 1m 19s
—DEMO: Quantile Implementation 2m 28s
—Summary 1m 0sOptimizing Conversion: A/B Testing- 12m 11s
—Introduction 0m 17s
—The Basic Idea 2m 57s
—Introducing Google Content Experiments 1m 30s
—Demo: Defining A Goal 0m 49s
—Demo: Setting up A Content Experiment 1m 38s
—Waiting for Results 2m 32s
—Demo: Estimating Sample Size 1m 13s
—Summary 1m 13sRecommendations: Item-based Recommendations- 21m 23sPersonalized Recommendations: Naive Bayesian Classifier- 22m 46sFinding Groups: k-means Clustering- 21m 58s
