Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Metrics Analysis and Data Driven Problem Solving
Skills for using quantitative metrics to diagnose and solve product or support problems. Candidates should be able to identify relevant key performance indicators such as customer satisfaction, response time, resolution rate, and first contact resolution, detect anomalies and trends, formulate and prioritize hypotheses about root causes, design experiments and controlled tests to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance and practical impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple metrics, and communicating findings and recommended changes to cross functional stakeholders.
Analytics and Dashboarding
Designing, building, and enabling dashboards and spreadsheet based analysis to turn data into actionable insights for different stakeholder audiences. Candidates should be able to define and prioritize key performance indicators and metrics for roles such as sales, marketing, finance, and executives; apply dashboard design principles that present complex data clearly; and enable self service analytics through reusable data models, standardized metrics, documentation, and user training. Practical spreadsheet skills are included: advanced formulas, pivot tables, lookup functions, data cleaning, filtering, charting, sensitivity and what if analysis, and performance optimization. Candidates should also speak to tools and platforms used such as Excel, Google Sheets, business intelligence platforms, visualization tools, and analytics platforms; consider refresh cadence, data validation and governance, interactivity and drill down patterns, and trade offs between standardized reporting and bespoke custom views.
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.
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.
Advanced Data Analysis and Statistics
Focuses on higher level analytical and statistical techniques for interpreting data and testing hypotheses. Topics include time series analysis, cohort and segmentation analysis, correlation and causation distinctions, descriptive versus inferential statistics, experimental design and hypothesis testing, consideration of sample size and power, detection of confounding variables including Simpson s paradox, and practical interpretation of results and limitations. Emphasizes choosing appropriate methods for given questions and communicating statistical findings clearly.
Data Problem Solving and Business Context
Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.
Data Analysis and Performance Measurement
Covers the end to end use of quantitative analysis to track, interpret, and act on business performance across accounts and campaigns. Candidates should be fluent in account level metrics such as customer retention rate, net revenue retention, annual recurring revenue, net promoter score, customer health scores, and customer lifetime value, as well as marketing and acquisition metrics such as click through rate, conversion rate, customer acquisition cost, return on advertising spend, and attribution model outcomes. Expect discussion of data sources and instrumentation, cohort and funnel analysis, segmentation, anomaly detection, attribution approaches, and calculating return on investment for initiatives. Candidates should be able to describe how they used analytics tools and queries, dashboards, and experiments or A B tests to identify at risk accounts or underperforming campaigns, prioritize actions, optimize strategies, and measure the impact of initiatives. Strong answers explain concrete metrics chosen, analysis methods, tools used, how results informed decisions, and how success was measured over time.
Data and Business Outcomes
This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.