Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Analysis and Insight Generation
Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.
Large Dataset Management and Technical Analysis
Develop skills in working efficiently with large datasets: data cleaning and validation, efficient aggregation and manipulation, handling missing data, identifying and managing outliers. Master advanced Excel features or learn SQL for database queries. Practice data quality assessment. Learn efficient workflows that scale with dataset size. Understand data security and privacy considerations.
Data Driven Recommendations and Impact
Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.
SQL for Data Analysis
Using SQL as a tool for data analysis and reporting. Focuses on writing queries to extract metrics, perform aggregations, join disparate data sources, use subqueries and window functions for trends and rankings, and prepare data for dashboards and reports. Includes best practices for reproducible analytical queries, handling time series and date arithmetic, basic query optimization considerations for analytic workloads, and when to use SQL versus built in reporting tools in analytics platforms.
Applying Data Science Techniques to Business Problems
Recognizing when A/B testing is appropriate vs observational analysis. Suggesting SQL queries or analysis approaches that would answer the business question. Understanding when you'd need advanced modeling vs simpler analysis. Connecting technical approaches to business decisions (e.g., 'This cohort analysis would tell us whether the decline is from existing users or new users').
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Data Analysis and Requirements Translation
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural 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.
Insight Translation and Recommendations
The ability to move beyond reporting numbers to produce clear, actionable business recommendations and narratives. This includes summarizing the problem statement, approach, key findings, model or analysis performance, limitations, and recommended next steps framed as business actions. Candidates should demonstrate how insights map to business metrics and priorities, quantify potential impact and tradeoffs, propose experiments or interventions, and prioritize recommended actions. Effective communication techniques include concise storytelling, appropriate visualizations, translating technical metrics into business terms, anticipating stakeholder questions, and explicitly answering the questions so what and now what. Senior analysts connect root cause analysis to concrete proposals such as feature changes, pricing experiments, targeted support, or investment decisions, and explain risks, data assumptions, and implementation considerations.