Lesson 1: Overview of A/B Testing
This lesson will cover what A/B testing is and what it can be used for. It will also cover an example A/B test from start to finish, including how to decide how long to run the experiment, how to construct a binomial confidence interval for the results, and how to decide whether the change is worth the launch cost.
Lesson 2: Policy and Ethics for Experiments
This lesson will cover how to make sure the participants of your experiments are adequately protected and what questions you should be asking regarding the ethicality of experiments. It will cover four main ethics principles to consider when designing experiments: the risk to the user, the potential benefits, what alternatives users have to participating in the experiment, and the sensitivity of the data being collected.
Lesson 3: Choosing and Characterizing Metrics
One of the most important and time-consuming pieces of designing an A/B test is choosing and validating metrics to use in evaluating your experiment. This lesson will cover techniques for brainstorming metrics, what to do when you can't measure what you want directly, and characteristics you should consider when validating your metrics.
Lesson 4: Designing an Experiment
This lesson will cover how to design an A/B test. This includes how to choose which users will be in your experiment and control group - different online definitions of a "user", and what effects different choices will have on your experiment. It will also cover when to limit your experiment to a subset of your entire user base, how to calculate how many events you will need in order to draw strong conclusions from your results, and how this translates into how long to run the experiment. Finally, the lesson will cover how various design decisions affect the size of your experiment, so you will know which decisions to revisit if you need results more quickly.
Lesson 5: Analyzing Results
This lesson will cover how to analyze the results of your experiments. Step one is always to run some sanity checks so that you can catch problems with your experiment set-up. Then, you will learn how to check conclusions with multiple methods, including a hypothesis test on the effect size and a binomial sign test, if you get results that surprise you. You will also learn how measuring multiple metrics for the same experiment can make analysis difficult, and some techniques for handling multiple metrics. Finally, you will learn about several analysis "gotchas", and what to do if you see them, including how Simpson's Paradox can affect A/B tests, and why even statistically significant results might disappear when you launch.
Final Project: Design and Analyze an A/B Test
Make design decisions for an A/B test, including which metrics to measure and how long the test should be run. Analyze the results of an A/B test that was run by Udacity and recommend whether or not to launch the change.
- Diane Tang - Diane Tang is a Google Fellow currently working in Google Research on building data infrastructure and analytics for biological & medical applications. Prior to 2014, she was a leader on the AdsQuality team at Google. She joined Google in 2003 and has focused on logging, large-scale data analysis & infrastructure, experiment methodology and ads systems. She earned a bachelor's degree in Computer Science from Harvard in 1995 and a master's degree and PhD in Computer Science from Stanford in 2001. She holds many patents and is the author of numerous publications in mobile networking, information visualization, experiment methodology, data infrastructure, and data mining / large data.
- Carrie Grimes - Carrie Grimes Bostock is currently a Distinguished Engineer at Google, working on data driven resource planning, cost analysis, and distributed cluster management software as part of the Technical Infrastructure group. She joined Google in 2003, and spent most of the last 12 years working in Search and Search infrastructure on statistical and engineering problems in crawling and indexing quality, ranking evaluation, and forecasting. She graduated from Harvard with an A.B. in Anthropology/Archaeology in 1998, and an interest in quantitative methods for dealing with disparate data. She received a PhD at Stanford in 2003 in Statistics after working with David Donoho on Nonlinear Dimensionality Reduction problems.
- Caroline Buckey - Before joining Udacity, Caroline worked as a Software Engineer at Quixey, a startup building a search engine for apps. While receiving her undergraduate degree from Carnegie Mellon, she was a TA for six different courses, and that same love for teaching later led her to join Udacity. Outside of work, she likes reading fiction, playing board games, and drinking bubble tea.
Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant massives des cours en ligne ouverts (MOOCs).
Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.