Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Driven Decision Making
Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.
Exploratory Data Analysis
Exploratory Data Analysis is the systematic process of investigating and validating a dataset to understand its structure, content, and quality before modelling or reporting. Core activities include examining schema and data types, computing descriptive statistics such as counts, means, medians, standard deviations and quartiles, and measuring class balance and unique value counts. It covers distribution analysis, outlier detection, correlation and relationship exploration, and trend or seasonality checks for time series. Data validation and quality checks include identifying missing values, anomalies, inconsistent encodings, duplicates, and other data integrity issues. Practical techniques span SQL profiling and aggregation queries using GROUP BY, COUNT and DISTINCT; interactive data exploration with pandas and similar libraries; and visualization with histograms, box plots, scatter plots, heatmaps and time series charts to reveal patterns and issues. The process also includes feature summary and basic metric computation, sampling strategies, forming and documenting hypotheses, and recommending cleaning or transformation steps. Good Exploratory Data Analysis produces a clear record of findings, assumptions to validate, and next steps for cleaning, feature engineering, or modelling.
Audience Segmentation and Cohorts
Covers methods for dividing users or consumers into meaningful segments and analyzing their behavior over time using cohort analysis. Candidates should be able to choose segmentation dimensions such as demographics, acquisition channel, product usage, geography, device, or behavioral attributes, and justify those choices for a given business question. They should know how to design cohort analyses to measure retention, churn, lifetime value, and conversion funnels, and how to avoid common pitfalls such as Simpson's Paradox and survivorship bias. This topic also includes deriving behavioral insights to inform personalization, content and product strategy, marketing targeting, and persona development, as well as identifying underserved or high value segments. Expect discussion of relevant metrics, data requirements and quality considerations, approaches to visualization and interpretation, and typical tools and techniques used in analytics and experimentation to validate segment driven hypotheses.
Dashboard and Data Visualization Design
Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.
Netflix-Specific Data Analysis Scenarios
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.
Analysis to Recommendation and Decision Framing
Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.
Exploratory Data Analysis and Hypothesis Generation
Skills and processes for quickly understanding a dataset, identifying data quality issues, and turning initial observations into testable business hypotheses. Candidates should demonstrate techniques for profiling data, summarizing distributions, detecting outliers and missing values, exploring time series and cohort patterns, and visualizing key relationships. Discussion should cover practical tools and workflows for rapid exploration, how to document findings and assumptions, and how to prioritize hypotheses by business impact and data feasibility. Interviewers assess the candidate s ability to propose concrete next steps and simple tests to validate hypotheses and to surface instrumentation or data collection needs.
A/B Test Results Analysis
Learn to analyze results from A/B tests and experiments. Understand key statistics: sample size, statistical significance, confidence intervals, and p-values at a practical level (not deep theory). Practice interpreting test results: 'Is this difference real or just noise?' Learn common mistakes: stopping tests early, p-hacking, and running too many tests simultaneously.
Data Storytelling and Insight Communication
Skills for converting quantitative and qualitative analysis into a clear, persuasive narrative that guides stakeholders from findings to action. This includes leading with the headline insight, defining the business question, selecting the most relevant metrics and visual evidence, and structuring a concise story that explains what happened, why it happened, and what the recommended next steps are. Candidates should demonstrate tailoring of language and technical depth for diverse audiences from engineers to product managers to executives, summarizing trade offs and uncertainty in plain language, distinguishing correlation from causation, proposing follow up experiments or investigations, and producing concise executive summaries and status reports with an appropriate cadence. Interviewers evaluate the ability to persuade and align cross functional partners, answer questions about data validity and methodology, synthesize qualitative signals with quantitative results, and adapt presentation format and level of detail to the decision maker.