Netflix-Specific Data Analysis Scenarios Questions
Netflix-specific data analysis scenarios covering streaming metrics, user engagement and retention analysis, content consumption patterns, evaluation of recommendation systems, A/B test design and analysis, cohort analysis, data visualization, and storytelling with data in the streaming domain.
MediumTechnical
0 practiced
Design an A/B experiment to evaluate a change in the ranking algorithm that reorders the recommendations feed. Primary metric: watch-time per user over 14 days. Guardrails: prevent >1% negative lift in 7-day retention. Minimum detectable effect: 2% with 80% power. Outline randomization unit, sample size calculation approach, monitoring plan, pre-specified metrics, and analysis plan (including heterogeneity and novelty checks).
EasyTechnical
0 practiced
Given a dataset with columns: user_id, content_id, genre, watch_start TIMESTAMP, watch_end TIMESTAMP, device_type, country — outline a prioritized exploratory data analysis plan and list 6 visualizations you would produce. For each visualization specify: chart type, x/y axes or aggregation, segmentation/facets, and the question that visualization answers. Also mention sampling strategies for very large datasets and preprocessing steps you would apply before plotting.
EasyTechnical
0 practiced
You're running an A/B test on a new recommendation UI that adds a 'More Like This' row. For the primary metric 'minutes watched per user per day', define the null and alternative hypotheses, explain Type I and Type II errors in this context, and describe what a p-value and a 95% confidence interval mean for stakeholders. Also mention what pre-test checks you would run before analyzing results.
HardTechnical
0 practiced
You want to optimize recommendations for long-term retention rather than immediate watch-time. Propose an approach using reinforcement learning or surrogate metrics: define state, action, reward (delayed vs immediate), discuss off-policy training, simulation or environment modeling, reward shaping, evaluation strategy, and deployment caveats for safety and interpretability.
MediumTechnical
0 practiced
Users self-select what to watch, causing selection bias in observed consumption data. Describe statistical methods to estimate true content preference under selection bias: propensity scoring / inverse probability weighting, instrumental variables, randomized exposure, and targeted experiments. For each method outline required assumptions, logging instrumentation needed, and limitations.
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