Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
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.
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.
Problem Definition and Hypothesis Formation
Break down ambiguous business questions into specific, answerable analytics problems and define what success looks like. Ask clarifying questions about business context, constraints, stakeholder expectations, and acceptance criteria. Use structured diagnosis and root cause analysis to isolate where a problem occurs by segmenting users, products, time periods, or geographies. Generate multiple testable hypotheses that explain observed outcomes, distinguish correlation from causation, and prioritize hypotheses by likelihood, potential impact, and ease of validation. Frame measurable metrics for each hypothesis and propose high level validation approaches or experiments to confirm or reject the hypotheses.
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.
Forecasting and Trend Analysis
Covers the full set of forecasting methods and trend analysis techniques used to project future business performance and validate predictions. Topics include time series techniques, moving averages, exponential smoothing, regression analysis, seasonality adjustments, straight line and percentage growth projections, and regression to the mean. Also covers driver based and bottom up build up models, top down allocations, scenario and sensitivity modeling, judgement based forecasts, and sales forecasting approaches such as pipeline based forecasting, deal probability weighting, collaborative forecasting, and historical trending. Includes model validation and accuracy assessment techniques, methods to identify and correct forecast bias, back testing against historical data, assessing confidence intervals and uncertainty, and communicating assumptions and forecast limitations.
Business Intelligence Background
A summary of business intelligence experience including the BI platforms and tools used, types of dashboards and reports built, data volumes and sources, analytical methods, stakeholder consumption patterns, and measurable business outcomes. Candidates should explain how BI efforts influenced decisions, examples of ETL or modeling work, and any leadership or ownership of BI initiatives.