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.
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.
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.
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 Interpretation & Dashboard Literacy
Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.
Metrics and KPI Fundamentals
Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators. Candidates should be able to select meaningful metrics aligned to business objectives rather than vanity metrics, explain the difference between a metric and a target, and distinguish leading indicators from lagging indicators. Coverage includes decomposing complex outcomes into actionable component metrics, writing precise metric definitions such as what counts as a daily active user and monthly active user, calculating common metrics such as engagement rate, churn rate and conversion rates, establishing baselines and sensible targets, and interpreting signal versus noise including awareness of statistical variability. Also includes using segmentation and cohort analysis to diagnose metric movements and recommending two to three meaningful metrics for a hypothetical problem with justification and action plans.
Quantitative Analysis and Metrics Interpretation
Core skills for working with numeric business data: calculating and interpreting key metrics, comparing options numerically, identifying trends and anomalies, performing variance checks, and testing assumptions. Includes reading dashboards and query results, extracting meaningful insights from revenue and operational metrics, segmenting data, identifying outliers, and understanding what metrics indicate about business performance. Candidates should be comfortable stating and justifying assumptions, performing simple break even and cost benefit reasoning, and translating numbers into prioritized actions or follow up analyses. This topic covers cross functional metric types from sales and operations to product and marketing, and emphasizes structured thinking, correct metric definitions, basic descriptive statistics, and how to use data to support recommendations.
Marketing Analytics and Measurement
Covers the design, implementation, and interpretation of marketing measurement systems that connect marketing activities to business outcomes. Topics include defining and prioritizing key performance indicators across the marketing funnel from awareness and consideration through conversion, retention, and advocacy. Core metrics and diagnostic measures include click through rate, conversion rate, impressions, engagement and session metrics, bounce rate, lead volume, cost per lead, cost per acquisition, customer acquisition cost, customer lifetime value, return on advertising spend, return on investment, marketing influenced revenue, pipeline contribution, marketing qualified leads, sales accepted leads, and lead to opportunity conversion rates. Measurement frameworks and methods include last click and multi touch attribution approaches, marketing mix modeling, incrementality testing and holdout group experiments, randomized controlled experiments and split testing, and considerations for statistical significance, sample size, noise, and distinguishing correlation from causation. Also covers data and instrumentation concerns such as tagging and event tracking, data flows from advertising and marketing systems into analytics platforms and data warehouses, data quality and identity resolution, and privacy driven tracking limitations. Reporting and dashboard design topics include selecting leading versus lagging indicators, balancing granular event level dashboards with executive level summaries, setting realistic targets and benchmarks, communicating findings and recommended actions to stakeholders, and using measurement to inform channel mix, campaign optimization, and budget allocation decisions.
Measurement Design and Analysis
Practical measurement design and analytic techniques for producing reliable metric signals and proving impact. Includes instrumentation and tracking plans, experiment selection and validation, attribution modeling and its limitations, sample size and statistical considerations, identifying confounding variables, and reasoning about correlation versus causation. Also covers tradeoffs in data collection and data quality checks, cohort and segmentation design, baselining and threshold setting, designing dashboards and monitoring cadence, and connecting engineering and telemetry data to business outcomes. Candidates should be able to write clear measurement plans and success criteria, describe experiment and validation approaches, and explain how to operationalize results through reporting and iteration.